Using ontologies for structuring organizational knowledge in Home Care assistance.

PubMed

Valls, Aida; Gibert, Karina; Sánchez, David; Batet, Montserrat

2010-05-01

Information Technologies and Knowledge-based Systems can significantly improve the management of complex distributed health systems, where supporting multidisciplinarity is crucial and communication and synchronization between the different professionals and tasks becomes essential. This work proposes the use of the ontological paradigm to describe the organizational knowledge of such complex healthcare institutions as a basis to support their management. The ontology engineering process is detailed, as well as the way to maintain the ontology updated in front of changes. The paper also analyzes how such an ontology can be exploited in a real healthcare application and the role of the ontology in the customization of the system. The particular case of senior Home Care assistance is addressed, as this is a highly distributed field as well as a strategic goal in an ageing Europe. The proposed ontology design is based on a Home Care medical model defined by an European consortium of Home Care professionals, framed in the scope of the K4Care European project (FP6). Due to the complexity of the model and the knowledge gap existing between the - textual - medical model and the strict formalization of an ontology, an ontology engineering methodology (On-To-Knowledge) has been followed. After applying the On-To-Knowledge steps, the following results were obtained: the feasibility study concluded that the ontological paradigm and the expressiveness of modern ontology languages were enough to describe the required medical knowledge; after the kick-off and refinement stages, a complete and non-ambiguous definition of the Home Care model, including its main components and interrelations, was obtained; the formalization stage expressed HC medical entities in the form of ontological classes, which are interrelated by means of hierarchies, properties and semantically rich class restrictions; the evaluation, carried out by exploiting the ontology into a knowledge-driven e-health application running on a real scenario, showed that the ontology design and its exploitation brought several benefits with regards to flexibility, adaptability and work efficiency from the end-user point of view; for the maintenance stage, two software tools are presented, aimed to address the incorporation and modification of healthcare units and the personalization of ontological profiles. The paper shows that the ontological paradigm and the expressiveness of modern ontology languages can be exploited not only to represent terminology in a non-ambiguous way, but also to formalize the interrelations and organizational structures involved in a real and distributed healthcare environment. This kind of ontologies facilitates the adaptation in front of changes in the healthcare organization or Care Units, supports the creation of profile-based interaction models in a transparent and seamless way, and increases the reusability and generality of the developed software components. As a conclusion of the exploitation of the developed ontology in a real medical scenario, we can say that an ontology formalizing organizational interrelations is a key component for building effective distributed knowledge-driven e-health systems. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Where to search top-K biomedical ontologies?

PubMed

Oliveira, Daniela; Butt, Anila Sahar; Haller, Armin; Rebholz-Schuhmann, Dietrich; Sahay, Ratnesh

2018-03-20

Searching for precise terms and terminological definitions in the biomedical data space is problematic, as researchers find overlapping, closely related and even equivalent concepts in a single or multiple ontologies. Search engines that retrieve ontological resources often suggest an extensive list of search results for a given input term, which leads to the tedious task of selecting the best-fit ontological resource (class or property) for the input term and reduces user confidence in the retrieval engines. A systematic evaluation of these search engines is necessary to understand their strengths and weaknesses in different search requirements. We have implemented seven comparable Information Retrieval ranking algorithms to search through ontologies and compared them against four search engines for ontologies. Free-text queries have been performed, the outcomes have been judged by experts and the ranking algorithms and search engines have been evaluated against the expert-based ground truth (GT). In addition, we propose a probabilistic GT that is developed automatically to provide deeper insights and confidence to the expert-based GT as well as evaluating a broader range of search queries. The main outcome of this work is the identification of key search factors for biomedical ontologies together with search requirements and a set of recommendations that will help biomedical experts and ontology engineers to select the best-suited retrieval mechanism in their search scenarios. We expect that this evaluation will allow researchers and practitioners to apply the current search techniques more reliably and that it will help them to select the right solution for their daily work. The source code (of seven ranking algorithms), ground truths and experimental results are available at https://github.com/danielapoliveira/bioont-search-benchmark.

Ontology modularization to improve semantic medical image annotation.

PubMed

Wennerberg, Pinar; Schulz, Klaus; Buitelaar, Paul

2011-02-01

Searching for medical images and patient reports is a significant challenge in a clinical setting. The contents of such documents are often not described in sufficient detail thus making it difficult to utilize the inherent wealth of information contained within them. Semantic image annotation addresses this problem by describing the contents of images and reports using medical ontologies. Medical images and patient reports are then linked to each other through common annotations. Subsequently, search algorithms can more effectively find related sets of documents on the basis of these semantic descriptions. A prerequisite to realizing such a semantic search engine is that the data contained within should have been previously annotated with concepts from medical ontologies. One major challenge in this regard is the size and complexity of medical ontologies as annotation sources. Manual annotation is particularly time consuming labor intensive in a clinical environment. In this article we propose an approach to reducing the size of clinical ontologies for more efficient manual image and text annotation. More precisely, our goal is to identify smaller fragments of a large anatomy ontology that are relevant for annotating medical images from patients suffering from lymphoma. Our work is in the area of ontology modularization, which is a recent and active field of research. We describe our approach, methods and data set in detail and we discuss our results. Copyright © 2010 Elsevier Inc. All rights reserved.

Using Ontological Engineering to Organize Learning/Instructional Theories and Build a Theory-Aware Authoring System

ERIC Educational Resources Information Center

Hayashi, Yusuke; Bourdeau, Jacqueline; Mizoguchi, Riichiro

2009-01-01

This paper describes the achievements of an innovative eight-year research program first introduced in Mizoguchi and Bourdeau (2000), which was aimed at building a theory-aware authoring system by using ontological engineering. To date, we have proposed OMNIBUS, an ontology that comprehensively covers different learning/instructional theories and…

Studies on Experimental Ontology and Knowledge Service Development in Bio-Environmental Engineering

NASA Astrophysics Data System (ADS)

Zhang, Yunliang

2018-01-01

The existing domain-related ontology and information service patterns are analyzed, and the main problems faced by the experimental scheme knowledge service were clarified. The ontology framework model for knowledge service of Bio-environmental Engineering was proposed from the aspects of experimental materials, experimental conditions and experimental instruments, and this ontology will be combined with existing knowledge organization systems to organize scientific and technological literatures, data and experimental schemes. With the similarity and priority calculation, it can improve the related domain research.

Epistemology, Ontology and Ethics: "Galaxies Away from the Engineering World"?

ERIC Educational Resources Information Center

Christensen, Steen Hyldgaard; Erno-Kjolhede, Erik

2008-01-01

Philosophy of technology/philosophy of science has recently become part of the curriculum of engineering degree programmes in Denmark. However, to what extent do teachers of engineering see it as meaningful for students to work with relatively abstract philosophical concepts such as epistemology, ontology and ethics as part of engineering degree…

A Knowledge Engineering Approach to Develop Domain Ontology

ERIC Educational Resources Information Center

Yun, Hongyan; Xu, Jianliang; Xiong, Jing; Wei, Moji

2011-01-01

Ontologies are one of the most popular and widespread means of knowledge representation and reuse. A few research groups have proposed a series of methodologies for developing their own standard ontologies. However, because this ontological construction concerns special fields, there is no standard method to build domain ontology. In this paper,…

A Study of the Use of Ontologies for Building Computer-Aided Control Engineering Self-Learning Educational Software

ERIC Educational Resources Information Center

García, Isaías; Benavides, Carmen; Alaiz, Héctor; Alonso, Angel

2013-01-01

This paper describes research on the use of knowledge models (ontologies) for building computer-aided educational software in the field of control engineering. Ontologies are able to represent in the computer a very rich conceptual model of a given domain. This model can be used later for a number of purposes in different software applications. In…

Surreptitious, Evolving and Participative Ontology Development: An End-User Oriented Ontology Development Methodology

ERIC Educational Resources Information Center

Bachore, Zelalem

2012-01-01

Ontology not only is considered to be the backbone of the semantic web but also plays a significant role in distributed and heterogeneous information systems. However, ontology still faces limited application and adoption to date. One of the major problems is that prevailing engineering-oriented methodologies for building ontologies do not…

Modular Knowledge Representation and Reasoning in the Semantic Web

NASA Astrophysics Data System (ADS)

Serafini, Luciano; Homola, Martin

Construction of modular ontologies by combining different modules is becoming a necessity in ontology engineering in order to cope with the increasing complexity of the ontologies and the domains they represent. The modular ontology approach takes inspiration from software engineering, where modularization is a widely acknowledged feature. Distributed reasoning is the other side of the coin of modular ontologies: given an ontology comprising of a set of modules, it is desired to perform reasoning by combination of multiple reasoning processes performed locally on each of the modules. In the last ten years, a number of approaches for combining logics has been developed in order to formalize modular ontologies. In this chapter, we survey and compare the main formalisms for modular ontologies and distributed reasoning in the Semantic Web. We select four formalisms build on formal logical grounds of Description Logics: Distributed Description Logics, ℰ-connections, Package-based Description Logics and Integrated Distributed Description Logics. We concentrate on expressivity and distinctive modeling features of each framework. We also discuss reasoning capabilities of each framework.

Supporting the analysis of ontology evolution processes through the combination of static and dynamic scaling functions in OQuaRE.

PubMed

Duque-Ramos, Astrid; Quesada-Martínez, Manuel; Iniesta-Moreno, Miguela; Fernández-Breis, Jesualdo Tomás; Stevens, Robert

2016-10-17

The biomedical community has now developed a significant number of ontologies. The curation of biomedical ontologies is a complex task and biomedical ontologies evolve rapidly, so new versions are regularly and frequently published in ontology repositories. This has the implication of there being a high number of ontology versions over a short time span. Given this level of activity, ontology designers need to be supported in the effective management of the evolution of biomedical ontologies as the different changes may affect the engineering and quality of the ontology. This is why there is a need for methods that contribute to the analysis of the effects of changes and evolution of ontologies. In this paper we approach this issue from the ontology quality perspective. In previous work we have developed an ontology evaluation framework based on quantitative metrics, called OQuaRE. Here, OQuaRE is used as a core component in a method that enables the analysis of the different versions of biomedical ontologies using the quality dimensions included in OQuaRE. Moreover, we describe and use two scales for evaluating the changes between the versions of a given ontology. The first one is the static scale used in OQuaRE and the second one is a new, dynamic scale, based on the observed values of the quality metrics of a corpus defined by all the versions of a given ontology (life-cycle). In this work we explain how OQuaRE can be adapted for understanding the evolution of ontologies. Its use has been illustrated with the ontology of bioinformatics operations, types of data, formats, and topics (EDAM). The two scales included in OQuaRE provide complementary information about the evolution of the ontologies. The application of the static scale, which is the original OQuaRE scale, to the versions of the EDAM ontology reveals a design based on good ontological engineering principles. The application of the dynamic scale has enabled a more detailed analysis of the evolution of the ontology, measured through differences between versions. The statistics of change based on the OQuaRE quality scores make possible to identify key versions where some changes in the engineering of the ontology triggered a change from the OQuaRE quality perspective. In the case of the EDAM, this study let us to identify that the fifth version of the ontology has the largest impact in the quality metrics of the ontology, when comparative analyses between the pairs of consecutive versions are performed.

A Study of the Use of Ontologies for Building Computer-Aided Control Engineering Self-Learning Educational Software

NASA Astrophysics Data System (ADS)

García, Isaías; Benavides, Carmen; Alaiz, Héctor; Alonso, Angel

2013-08-01

This paper describes research on the use of knowledge models (ontologies) for building computer-aided educational software in the field of control engineering. Ontologies are able to represent in the computer a very rich conceptual model of a given domain. This model can be used later for a number of purposes in different software applications. In this study, domain ontology about the field of lead-lag compensator design has been built and used for automatic exercise generation, graphical user interface population and interaction with the user at any level of detail, including explanations about why things occur. An application called Onto-CELE (ontology-based control engineering learning environment) uses the ontology for implementing a learning environment that can be used for self and lifelong learning purposes. The experience has shown that the use of knowledge models as the basis for educational software applications is capable of showing students the whole complexity of the analysis and design processes at any level of detail. A practical experience with postgraduate students has shown the mentioned benefits and possibilities of the approach.

Ontology or formal ontology

NASA Astrophysics Data System (ADS)

Žáček, Martin

2017-07-01

Ontology or formal ontology? Which word is correct? The aim of this article is to introduce correct terms and explain their basis. Ontology describes a particular area of interest (domain) in a formal way - defines the classes of objects that are in that area, and relationships that may exist between them. Meaning of ontology consists mainly in facilitating communication between people, improve collaboration of software systems and in the improvement of systems engineering. Ontology in all these areas offer the possibility of unification of view, maintaining consistency and unambiguity.

Global polar geospatial information service retrieval based on search engine and ontology reasoning

USGS Publications Warehouse

Chen, Nengcheng; E, Dongcheng; Di, Liping; Gong, Jianya; Chen, Zeqiang

2007-01-01

In order to improve the access precision of polar geospatial information service on web, a new methodology for retrieving global spatial information services based on geospatial service search and ontology reasoning is proposed, the geospatial service search is implemented to find the coarse service from web, the ontology reasoning is designed to find the refined service from the coarse service. The proposed framework includes standardized distributed geospatial web services, a geospatial service search engine, an extended UDDI registry, and a multi-protocol geospatial information service client. Some key technologies addressed include service discovery based on search engine and service ontology modeling and reasoning in the Antarctic geospatial context. Finally, an Antarctica multi protocol OWS portal prototype based on the proposed methodology is introduced.

Prioritising lexical patterns to increase axiomatisation in biomedical ontologies. The role of localisation and modularity.

PubMed

Quesada-Martínez, M; Fernández-Breis, J T; Stevens, R; Mikroyannidi, E

2015-01-01

This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". In previous work, we have defined methods for the extraction of lexical patterns from labels as an initial step towards semi-automatic ontology enrichment methods. Our previous findings revealed that many biomedical ontologies could benefit from enrichment methods using lexical patterns as a starting point.Here, we aim to identify which lexical patterns are appropriate for ontology enrichment, driving its analysis by metrics to prioritised the patterns. We propose metrics for suggesting which lexical regularities should be the starting point to enrich complex ontologies. Our method determines the relevance of a lexical pattern by measuring its locality in the ontology, that is, the distance between the classes associated with the pattern, and the distribution of the pattern in a certain module of the ontology. The methods have been applied to four significant biomedical ontologies including the Gene Ontology and SNOMED CT. The metrics provide information about the engineering of the ontologies and the relevance of the patterns. Our method enables the suggestion of links between classes that are not made explicit in the ontology. We propose a prioritisation of the lexical patterns found in the analysed ontologies. The locality and distribution of lexical patterns offer insights into the further engineering of the ontology. Developers can use this information to improve the axiomatisation of their ontologies.

Noesis: Ontology based Scoped Search Engine and Resource Aggregator for Atmospheric Science

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Movva, S.; Li, X.; Cherukuri, P.; Graves, S.

2006-12-01

The goal for search engines is to return results that are both accurate and complete. The search engines should find only what you really want and find everything you really want. Search engines (even meta search engines) lack semantics. The basis for search is simply based on string matching between the user's query term and the resource database and the semantics associated with the search string is not captured. For example, if an atmospheric scientist is searching for "pressure" related web resources, most search engines return inaccurate results such as web resources related to blood pressure. In this presentation Noesis, which is a meta-search engine and a resource aggregator that uses domain ontologies to provide scoped search capabilities will be described. Noesis uses domain ontologies to help the user scope the search query to ensure that the search results are both accurate and complete. The domain ontologies guide the user to refine their search query and thereby reduce the user's burden of experimenting with different search strings. Semantics are captured by refining the query terms to cover synonyms, specializations, generalizations and related concepts. Noesis also serves as a resource aggregator. It categorizes the search results from different online resources such as education materials, publications, datasets, web search engines that might be of interest to the user.

A unified architecture for biomedical search engines based on semantic web technologies.

PubMed

Jalali, Vahid; Matash Borujerdi, Mohammad Reza

2011-04-01

There is a huge growth in the volume of published biomedical research in recent years. Many medical search engines are designed and developed to address the over growing information needs of biomedical experts and curators. Significant progress has been made in utilizing the knowledge embedded in medical ontologies and controlled vocabularies to assist these engines. However, the lack of common architecture for utilized ontologies and overall retrieval process, hampers evaluating different search engines and interoperability between them under unified conditions. In this paper, a unified architecture for medical search engines is introduced. Proposed model contains standard schemas declared in semantic web languages for ontologies and documents used by search engines. Unified models for annotation and retrieval processes are other parts of introduced architecture. A sample search engine is also designed and implemented based on the proposed architecture in this paper. The search engine is evaluated using two test collections and results are reported in terms of precision vs. recall and mean average precision for different approaches used by this search engine.

OntoPop: An Ontology Population System for the Semantic Web

NASA Astrophysics Data System (ADS)

Thongkrau, Theerayut; Lalitrojwong, Pattarachai

The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance.

OMOGENIA: A Semantically Driven Collaborative Environment

NASA Astrophysics Data System (ADS)

Liapis, Aggelos

Ontology creation can be thought of as a social procedure. Indeed the concepts involved in general need to be elicited from communities of domain experts and end-users by teams of knowledge engineers. Many problems in ontology creation appear to resemble certain problems in software design, particularly with respect to the setup of collaborative systems. For instance, the resolution of conceptual conflicts between formalized ontologies is a major engineering problem as ontologies move into widespread use on the semantic web. Such conflict resolution often requires human collaboration and cannot be achieved by automated methods with the exception of simple cases. In this chapter we discuss research in the field of computer-supported cooperative work (CSCW) that focuses on classification and which throws light on ontology building. Furthermore, we present a semantically driven collaborative environment called OMOGENIA as a natural way to display and examine the structure of an evolving ontology in a collaborative setting.

Evolving BioAssay Ontology (BAO): modularization, integration and applications

PubMed Central

2014-01-01

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets. PMID:25093074

Evolving BioAssay Ontology (BAO): modularization, integration and applications.

PubMed

Abeyruwan, Saminda; Vempati, Uma D; Küçük-McGinty, Hande; Visser, Ubbo; Koleti, Amar; Mir, Ahsan; Sakurai, Kunie; Chung, Caty; Bittker, Joshua A; Clemons, Paul A; Brudz, Steve; Siripala, Anosha; Morales, Arturo J; Romacker, Martin; Twomey, David; Bureeva, Svetlana; Lemmon, Vance; Schürer, Stephan C

2014-01-01

The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.

Usage of the Jess Engine, Rules and Ontology to Query a Relational Database

NASA Astrophysics Data System (ADS)

Bak, Jaroslaw; Jedrzejek, Czeslaw; Falkowski, Maciej

We present a prototypical implementation of a library tool, the Semantic Data Library (SDL), which integrates the Jess (Java Expert System Shell) engine, rules and ontology to query a relational database. The tool extends functionalities of previous OWL2Jess with SWRL implementations and takes full advantage of the Jess engine, by separating forward and backward reasoning. The optimization of integration of all these technologies is an advancement over previous tools. We discuss the complexity of the query algorithm. As a demonstration of capability of the SDL library, we execute queries using crime ontology which is being developed in the Polish PPBW project.

Developing and Validating the Socio-Technical Model in Ontology Engineering

NASA Astrophysics Data System (ADS)

Silalahi, Mesnan; Indra Sensuse, Dana; Giri Sucahyo, Yudho; Fadhilah Akmaliah, Izzah; Rahayu, Puji; Cahyaningsih, Elin

2018-03-01

This paper describes results from an attempt to develop a model in ontology engineering methodology and a way to validate the model. The approach to methodology in ontology engineering is from the point view of socio-technical system theory. Qualitative research synthesis is used to build the model using meta-ethnography. In order to ensure the objectivity of the measurement, inter-rater reliability method was applied using a multi-rater Fleiss Kappa. The results show the accordance of the research output with the diamond model in the socio-technical system theory by evidence of the interdependency of the four socio-technical variables namely people, technology, structure and task.

A Foundational Approach to Designing Geoscience Ontologies

NASA Astrophysics Data System (ADS)

Brodaric, B.

2009-05-01

E-science systems are increasingly deploying ontologies to aid online geoscience research. Geoscience ontologies are typically constructed independently by isolated individuals or groups who tend to follow few design principles. This limits the usability of the ontologies due to conceptualizations that are vague, conflicting, or narrow. Advances in foundational ontologies and formal engineering approaches offer promising solutions, but these advanced techniques have had limited application in the geosciences. This paper develops a design approach for geoscience ontologies by extending aspects of the DOLCE foundational ontology and the OntoClean method. Geoscience examples will be presented to demonstrate the feasibility of the approach.

The Fusion Model of Intelligent Transportation Systems Based on the Urban Traffic Ontology

NASA Astrophysics Data System (ADS)

Yang, Wang-Dong; Wang, Tao

On these issues unified representation of urban transport information using urban transport ontology, it defines the statute and the algebraic operations of semantic fusion in ontology level in order to achieve the fusion of urban traffic information in the semantic completeness and consistency. Thus this paper takes advantage of the semantic completeness of the ontology to build urban traffic ontology model with which we resolve the problems as ontology mergence and equivalence verification in semantic fusion of traffic information integration. Information integration in urban transport can increase the function of semantic fusion, and reduce the amount of data integration of urban traffic information as well enhance the efficiency and integrity of traffic information query for the help, through the practical application of intelligent traffic information integration platform of Changde city, the paper has practically proved that the semantic fusion based on ontology increases the effect and efficiency of the urban traffic information integration, reduces the storage quantity, and improve query efficiency and information completeness.

Improved object optimal synthetic description, modeling, learning, and discrimination by GEOGINE computational kernel

NASA Astrophysics Data System (ADS)

Fiorini, Rodolfo A.; Dacquino, Gianfranco

2005-03-01

GEOGINE (GEOmetrical enGINE), a state-of-the-art OMG (Ontological Model Generator) based on n-D Tensor Invariants for n-Dimensional shape/texture optimal synthetic representation, description and learning, was presented in previous conferences elsewhere recently. Improved computational algorithms based on the computational invariant theory of finite groups in Euclidean space and a demo application is presented. Progressive model automatic generation is discussed. GEOGINE can be used as an efficient computational kernel for fast reliable application development and delivery in advanced biomedical engineering, biometric, intelligent computing, target recognition, content image retrieval, data mining technological areas mainly. Ontology can be regarded as a logical theory accounting for the intended meaning of a formal dictionary, i.e., its ontological commitment to a particular conceptualization of the world object. According to this approach, "n-D Tensor Calculus" can be considered a "Formal Language" to reliably compute optimized "n-Dimensional Tensor Invariants" as specific object "invariant parameter and attribute words" for automated n-Dimensional shape/texture optimal synthetic object description by incremental model generation. The class of those "invariant parameter and attribute words" can be thought as a specific "Formal Vocabulary" learned from a "Generalized Formal Dictionary" of the "Computational Tensor Invariants" language. Even object chromatic attributes can be effectively and reliably computed from object geometric parameters into robust colour shape invariant characteristics. As a matter of fact, any highly sophisticated application needing effective, robust object geometric/colour invariant attribute capture and parameterization features, for reliable automated object learning and discrimination can deeply benefit from GEOGINE progressive automated model generation computational kernel performance. Main operational advantages over previous, similar approaches are: 1) Progressive Automated Invariant Model Generation, 2) Invariant Minimal Complete Description Set for computational efficiency, 3) Arbitrary Model Precision for robust object description and identification.

The Study on Collaborative Manufacturing Platform Based on Agent

NASA Astrophysics Data System (ADS)

Zhang, Xiao-yan; Qu, Zheng-geng

To fulfill the trends of knowledge-intensive in collaborative manufacturing development, we have described multi agent architecture supporting knowledge-based platform of collaborative manufacturing development platform. In virtue of wrapper service and communication capacity agents provided, the proposed architecture facilitates organization and collaboration of multi-disciplinary individuals and tools. By effectively supporting the formal representation, capture, retrieval and reuse of manufacturing knowledge, the generalized knowledge repository based on ontology library enable engineers to meaningfully exchange information and pass knowledge across boundaries. Intelligent agent technology increases traditional KBE systems efficiency and interoperability and provides comprehensive design environments for engineers.

A Systematic Analysis of Term Reuse and Term Overlap across Biomedical Ontologies

PubMed Central

Kamdar, Maulik R.; Tudorache, Tania; Musen, Mark A.

2016-01-01

Reusing ontologies and their terms is a principle and best practice that most ontology development methodologies strongly encourage. Reuse comes with the promise to support the semantic interoperability and to reduce engineering costs. In this paper, we present a descriptive study of the current extent of term reuse and overlap among biomedical ontologies. We use the corpus of biomedical ontologies stored in the BioPortal repository, and analyze different types of reuse and overlap constructs. While we find an approximate term overlap between 25–31%, the term reuse is only <9%, with most ontologies reusing fewer than 5% of their terms from a small set of popular ontologies. Clustering analysis shows that the terms reused by a common set of ontologies have >90% semantic similarity, hinting that ontology developers tend to reuse terms that are sibling or parent–child nodes. We validate this finding by analysing the logs generated from a Protégé plugin that enables developers to reuse terms from BioPortal. We find most reuse constructs were 2-level subtrees on the higher levels of the class hierarchy. We developed a Web application that visualizes reuse dependencies and overlap among ontologies, and that proposes similar terms from BioPortal for a term of interest. We also identified a set of error patterns that indicate that ontology developers did intend to reuse terms from other ontologies, but that they were using different and sometimes incorrect representations. Our results stipulate the need for semi-automated tools that augment term reuse in the ontology engineering process through personalized recommendations. PMID:28819351

Constructive Ontology Engineering

ERIC Educational Resources Information Center

Sousan, William L.

2010-01-01

The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in…

Exploiting Semantic Web Technologies to Develop OWL-Based Clinical Practice Guideline Execution Engines.

PubMed

Jafarpour, Borna; Abidi, Samina Raza; Abidi, Syed Sibte Raza

2016-01-01

Computerizing paper-based CPG and then executing them can provide evidence-informed decision support to physicians at the point of care. Semantic web technologies especially web ontology language (OWL) ontologies have been profusely used to represent computerized CPG. Using semantic web reasoning capabilities to execute OWL-based computerized CPG unties them from a specific custom-built CPG execution engine and increases their shareability as any OWL reasoner and triple store can be utilized for CPG execution. However, existing semantic web reasoning-based CPG execution engines suffer from lack of ability to execute CPG with high levels of expressivity, high cognitive load of computerization of paper-based CPG and updating their computerized versions. In order to address these limitations, we have developed three CPG execution engines based on OWL 1 DL, OWL 2 DL and OWL 2 DL + semantic web rule language (SWRL). OWL 1 DL serves as the base execution engine capable of executing a wide range of CPG constructs, however for executing highly complex CPG the OWL 2 DL and OWL 2 DL + SWRL offer additional executional capabilities. We evaluated the technical performance and medical correctness of our execution engines using a range of CPG. Technical evaluations show the efficiency of our CPG execution engines in terms of CPU time and validity of the generated recommendation in comparison to existing CPG execution engines. Medical evaluations by domain experts show the validity of the CPG-mediated therapy plans in terms of relevance, safety, and ordering for a wide range of patient scenarios.

Module Extraction for Efficient Object Queries over Ontologies with Large ABoxes

PubMed Central

Xu, Jia; Shironoshita, Patrick; Visser, Ubbo; John, Nigel; Kabuka, Mansur

2015-01-01

The extraction of logically-independent fragments out of an ontology ABox can be useful for solving the tractability problem of querying ontologies with large ABoxes. In this paper, we propose a formal definition of an ABox module, such that it guarantees complete preservation of facts about a given set of individuals, and thus can be reasoned independently w.r.t. the ontology TBox. With ABox modules of this type, isolated or distributed (parallel) ABox reasoning becomes feasible, and more efficient data retrieval from ontology ABoxes can be attained. To compute such an ABox module, we present a theoretical approach and also an approximation for SHIQ ontologies. Evaluation of the module approximation on different types of ontologies shows that, on average, extracted ABox modules are significantly smaller than the entire ABox, and the time for ontology reasoning based on ABox modules can be improved significantly. PMID:26848490

Using a Foundational Ontology for Reengineering a Software Enterprise Ontology

NASA Astrophysics Data System (ADS)

Perini Barcellos, Monalessa; de Almeida Falbo, Ricardo

The knowledge about software organizations is considerably relevant to software engineers. The use of a common vocabulary for representing the useful knowledge about software organizations involved in software projects is important for several reasons, such as to support knowledge reuse and to allow communication and interoperability between tools. Domain ontologies can be used to define a common vocabulary for sharing and reuse of knowledge about some domain. Foundational ontologies can be used for evaluating and re-designing domain ontologies, giving to these real-world semantics. This paper presents an evaluating of a Software Enterprise Ontology that was reengineered using the Unified Foundation Ontology (UFO) as basis.

GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution

PubMed Central

2011-01-01

Background Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data. Results We present GOMMA, a generic infrastructure for managing and analyzing life science ontologies and their evolution. GOMMA utilizes a generic repository to uniformly and efficiently manage ontology versions and different kinds of mappings. Furthermore, it provides components for ontology matching, and determining evolutionary ontology changes. These components are used by analysis tools, such as the Ontology Evolution Explorer (OnEX) and the detection of unstable ontology regions. We introduce the component-based infrastructure and show analysis results for selected components and life science applications. GOMMA is available at http://dbs.uni-leipzig.de/GOMMA. Conclusions GOMMA provides a comprehensive and scalable infrastructure to manage large life science ontologies and analyze their evolution. Key functions include a generic storage of ontology versions and mappings, support for ontology matching and determining ontology changes. The supported features for analyzing ontology changes are helpful to assess their impact on ontology-dependent applications such as for term enrichment. GOMMA complements OnEX by providing functionalities to manage various versions of mappings between two ontologies and allows combining different match approaches. PMID:21914205

Ontological engineering versus metaphysics

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Tomanek, Roman; Mulawka, Jan

2011-10-01

It has been recognized that ontologies are a semantic version of world wide web and can be found in knowledge-based systems. A recent time survey of this field also suggest that practical artificial intelligence systems may be motivated by this research. Especially strong artificial intelligence as well as concept of homo computer can also benefit from their use. The main objective of this contribution is to present and review already created ontologies and identify the main advantages which derive such approach for knowledge management systems. We would like to present what ontological engineering borrows from metaphysics and what a feedback it can provide to natural language processing, simulations and modelling. The potential topics of further development from philosophical point of view is also underlined.

Towards a reference plant trait ontology for modeling knowledge of plant traits and phenotypes

USDA-ARS?s Scientific Manuscript database

Ontology engineering and knowledge modeling for the plant sciences is expected to contribute to the understanding of the basis of plant traits that determine phenotypic expression in a given environment. Several crop- or clade-specific plant trait ontologies have been developed to describe plant tr...

A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text

ERIC Educational Resources Information Center

Nguyen, Bao-An; Yang, Don-Lin

2012-01-01

An ontology is an effective formal representation of knowledge used commonly in artificial intelligence, semantic web, software engineering, and information retrieval. In open and distance learning, ontologies are used as knowledge bases for e-learning supplements, educational recommenders, and question answering systems that support students with…

Ontology-Based Retrieval of Spatially Related Objects for Location Based Services

NASA Astrophysics Data System (ADS)

Haav, Hele-Mai; Kaljuvee, Aivi; Luts, Martin; Vajakas, Toivo

Advanced Location Based Service (LBS) applications have to integrate information stored in GIS, information about users' preferences (profile) as well as contextual information and information about application itself. Ontology engineering provides methods to semantically integrate several data sources. We propose an ontology-driven LBS development framework: the paper describes the architecture of ontologies and their usage for retrieval of spatially related objects relevant to the user. Our main contribution is to enable personalised ontology driven LBS by providing a novel approach for defining personalised semantic spatial relationships by means of ontologies. The approach is illustrated by an industrial case study.

Applications of Ontology Design Patterns in Biomedical Ontologies

PubMed Central

Mortensen, Jonathan M.; Horridge, Matthew; Musen, Mark A.; Noy, Natalya F.

2012-01-01

Ontology design patterns (ODPs) are a proposed solution to facilitate ontology development, and to help users avoid some of the most frequent modeling mistakes. ODPs originate from similar approaches in software engineering, where software design patterns have become a critical aspect of software development. There is little empirical evidence for ODP prevalence or effectiveness thus far. In this work, we determine the use and applicability of ODPs in a case study of biomedical ontologies. We encoded ontology design patterns from two ODP catalogs. We then searched for these patterns in a set of eight ontologies. We found five patterns of the 69 patterns. Two of the eight ontologies contained these patterns. While ontology design patterns provide a vehicle for capturing formally reoccurring models and best practices in ontology design, we show that today their use in a case study of widely used biomedical ontologies is limited. PMID:23304337

Semantics-Based Intelligent Indexing and Retrieval of Digital Images - A Case Study

NASA Astrophysics Data System (ADS)

Osman, Taha; Thakker, Dhavalkumar; Schaefer, Gerald

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they typically rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this chapter we present a semantically enabled image annotation and retrieval engine that is designed to satisfy the requirements of commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as presenting our initial thoughts on exploiting lexical databases for explicit semantic-based query expansion.

A Customizable Language Learning Support System Using Ontology-Driven Engine

ERIC Educational Resources Information Center

Wang, Jingyun; Mendori, Takahiko; Xiong, Juan

2013-01-01

This paper proposes a framework for web-based language learning support systems designed to provide customizable pedagogical procedures based on the analysis of characteristics of both learner and course. This framework employs a course-centered ontology and a teaching method ontology as the foundation for the student model, which includes learner…

Research-IQ: Development and Evaluation of an Ontology-anchored Integrative Query Tool

PubMed Central

Borlawsky, Tara B.; Lele, Omkar; Payne, Philip R. O.

2011-01-01

Investigators in the translational research and systems medicine domains require highly usable, efficient and integrative tools and methods that allow for the navigation of and reasoning over emerging large-scale data sets. Such resources must cover a spectrum of granularity from bio-molecules to population phenotypes. Given such information needs, we report upon the initial design and evaluation of an ontology-anchored integrative query tool, Research-IQ, which employs a combination of conceptual knowledge engineering and information retrieval techniques to enable the intuitive and rapid construction of queries, in terms of semi-structured textual propositions, that can subsequently be applied to integrative data sets. Our initial results, based upon both quantitative and qualitative evaluations of the efficacy and usability of Research-IQ, demonstrate its potential to increase clinical and translational research throughput. PMID:21821150

Methodology to build medical ontology from textual resources.

PubMed

Baneyx, Audrey; Charlet, Jean; Jaulent, Marie-Christine

2006-01-01

In the medical field, it is now established that the maintenance of unambiguous thesauri goes through ontologies. Our research task is to help pneumologists code acts and diagnoses with a software that represents medical knowledge through a domain ontology. In this paper, we describe our general methodology aimed at knowledge engineers in order to build various types of medical ontologies based on terminology extraction from texts. The hypothesis is to apply natural language processing tools to textual patient discharge summaries to develop the resources needed to build an ontology in pneumology. Results indicate that the joint use of distributional analysis and lexico-syntactic patterns performed satisfactorily for building such ontologies.

Content-oriented Approach to Organization of Theories and Its Utilization

NASA Astrophysics Data System (ADS)

Hayashi, Yusuke; Bourdeau, Jacqueline; Mizoguch, Riichiro

In spite of the fact that the relation between theory and practice is a foundation of scientific and technological development, the trend of increasing the gap between theory and practice accelerates in these years. The gap embraces a risk of distrust of science and technology. Ontological engineering as the content-oriented research is expected to contribute to the resolution of the gap. This paper presents the feasibility of organization of theoretical knowledge on ontological engineering and new-generation intelligent systems based on it through an application of ontological engineering in the area of learning/instruction support. This area also has the problem of the gap between theory and practice, and its resolution is strongly required. So far we proposed OMNIBUS ontology, which is a comprehensive ontology that covers different learning/instructional theories and paradigms, and SMARTIES, which is a theory-aware and standard-compliant authoring system for making learning/instructional scenarios based on OMNIBUS ontology. We believe the theory-awareness and standard-compliance bridge the gap between theory and practice because it links theories to practical use of standard technologies and enables practitioners to easily enjoy theoretical support while using standard technologies in practice. The following goals are set in order to achieve it; computers (1) understand a variety of learning/instructional theories based on the organization of them, (2) utilize the understanding for helping authors' learning/instructional scenario making and (3) make such theoretically sound scenarios interoperable within the framework of standard technologies. This paper suggests an ontological engineering solution to the achievement of these three goals. Although the evaluation is far from complete in terms of practical use, we believe that the results of this study address high-level technical challenges from the viewpoint of the current state of the art in the research area of artificial intelligence not only in education but also in general, and therefore we hope that constitute a substantial contribution for organization of theoretical knowledge in many other areas.

The BioPrompt-box: an ontology-based clustering tool for searching in biological databases.

PubMed

Corsi, Claudio; Ferragina, Paolo; Marangoni, Roberto

2007-03-08

High-throughput molecular biology provides new data at an incredible rate, so that the increase in the size of biological databanks is enormous and very rapid. This scenario generates severe problems not only at indexing time, where suitable algorithmic techniques for data indexing and retrieval are required, but also at query time, since a user query may produce such a large set of results that their browsing and "understanding" becomes humanly impractical. This problem is well known to the Web community, where a new generation of Web search engines is being developed, like Vivisimo. These tools organize on-the-fly the results of a user query in a hierarchy of labeled folders that ease their browsing and knowledge extraction. We investigate this approach on biological data, and propose the so called The BioPrompt-boxsoftware system which deploys ontology-driven clustering strategies for making the searching process of biologists more efficient and effective. The BioPrompt-box (Bpb) defines a document as a biological sequence plus its associated meta-data taken from the underneath databank--like references to ontologies or to external databanks, and plain texts as comments of researchers and (title, abstracts or even body of) papers. Bpboffers several tools to customize the search and the clustering process over its indexed documents. The user can search a set of keywords within a specific field of the document schema, or can execute Blastto find documents relative to homologue sequences. In both cases the search task returns a set of documents (hits) which constitute the answer to the user query. Since the number of hits may be large, Bpbclusters them into groups of homogenous content, organized as a hierarchy of labeled clusters. The user can actually choose among several ontology-based hierarchical clustering strategies, each offering a different "view" of the returned hits. Bpbcomputes these views by exploiting the meta-data present within the retrieved documents such as the references to Gene Ontology, the taxonomy lineage, the organism and the keywords. Of course, the approach is flexible enough to leave room for future additions of other meta-information. The ultimate goal of the clustering process is to provide the user with several different readings of the (maybe numerous) query results and show possible hidden correlations among them, thus improving their browsing and understanding. Bpb is a powerful search engine that makes it very easy to perform complex queries over the indexed databanks (currently only UNIPROT is considered). The ontology-based clustering approach is efficient and effective, and could thus be applied successfully to larger databanks, like GenBank or EMBL.

The BioPrompt-box: an ontology-based clustering tool for searching in biological databases

PubMed Central

Corsi, Claudio; Ferragina, Paolo; Marangoni, Roberto

2007-01-01

Background High-throughput molecular biology provides new data at an incredible rate, so that the increase in the size of biological databanks is enormous and very rapid. This scenario generates severe problems not only at indexing time, where suitable algorithmic techniques for data indexing and retrieval are required, but also at query time, since a user query may produce such a large set of results that their browsing and "understanding" becomes humanly impractical. This problem is well known to the Web community, where a new generation of Web search engines is being developed, like Vivisimo. These tools organize on-the-fly the results of a user query in a hierarchy of labeled folders that ease their browsing and knowledge extraction. We investigate this approach on biological data, and propose the so called The BioPrompt-boxsoftware system which deploys ontology-driven clustering strategies for making the searching process of biologists more efficient and effective. Results The BioPrompt-box (Bpb) defines a document as a biological sequence plus its associated meta-data taken from the underneath databank – like references to ontologies or to external databanks, and plain texts as comments of researchers and (title, abstracts or even body of) papers. Bpboffers several tools to customize the search and the clustering process over its indexed documents. The user can search a set of keywords within a specific field of the document schema, or can execute Blastto find documents relative to homologue sequences. In both cases the search task returns a set of documents (hits) which constitute the answer to the user query. Since the number of hits may be large, Bpbclusters them into groups of homogenous content, organized as a hierarchy of labeled clusters. The user can actually choose among several ontology-based hierarchical clustering strategies, each offering a different "view" of the returned hits. Bpbcomputes these views by exploiting the meta-data present within the retrieved documents such as the references to Gene Ontology, the taxonomy lineage, the organism and the keywords. Of course, the approach is flexible enough to leave room for future additions of other meta-information. The ultimate goal of the clustering process is to provide the user with several different readings of the (maybe numerous) query results and show possible hidden correlations among them, thus improving their browsing and understanding. Conclusion Bpb is a powerful search engine that makes it very easy to perform complex queries over the indexed databanks (currently only UNIPROT is considered). The ontology-based clustering approach is efficient and effective, and could thus be applied successfully to larger databanks, like GenBank or EMBL. PMID:17430575

Application of neuroanatomical ontologies for neuroimaging data annotation.

PubMed

Turner, Jessica A; Mejino, Jose L V; Brinkley, James F; Detwiler, Landon T; Lee, Hyo Jong; Martone, Maryann E; Rubin, Daniel L

2010-01-01

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are "part of" which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website. The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining.

Defining Resilience and Vulnerability Based on Ontology Engineering Approach

NASA Astrophysics Data System (ADS)

Kumazawa, T.; Matsui, T.; Endo, A.

2014-12-01

It is necessary to reflect the concepts of resilience and vulnerability into the assessment framework of "Human-Environmental Security", but it is also in difficulty to identify the linkage between both concepts because of the difference of the academic community which has discussed each concept. The authors have been developing the ontology which deals with the sustainability of the social-ecological systems (SESs). Resilience and vulnerability are also the concepts in the target world which this ontology covers. Based on this point, this paper aims at explicating the semantic relationship between the concepts of resilience and vulnerability based on ontology engineering approach. For this purpose, we first examine the definitions of resilience and vulnerability which the existing literatures proposed. Second, we incorporate the definitions in the ontology dealing with sustainability of SESs. Finally, we focus on the "Water-Energy-Food Nexus Index" to assess Human-Environmental Security, and clarify how the concepts of resilience and vulnerability are linked semantically through the concepts included in these index items.

OWLing Clinical Data Repositories With the Ontology Web Language

PubMed Central

Pastor, Xavier; Lozano, Esther

2014-01-01

Background The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions. Objective The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users. Methods We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface. Results Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification. Conclusions OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems. PMID:25599697

OWLing Clinical Data Repositories With the Ontology Web Language.

PubMed

Lozano-Rubí, Raimundo; Pastor, Xavier; Lozano, Esther

2014-08-01

The health sciences are based upon information. Clinical information is usually stored and managed by physicians with precarious tools, such as spreadsheets. The biomedical domain is more complex than other domains that have adopted information and communication technologies as pervasive business tools. Moreover, medicine continuously changes its corpus of knowledge because of new discoveries and the rearrangements in the relationships among concepts. This scenario makes it especially difficult to offer good tools to answer the professional needs of researchers and constitutes a barrier that needs innovation to discover useful solutions. The objective was to design and implement a framework for the development of clinical data repositories, capable of facing the continuous change in the biomedicine domain and minimizing the technical knowledge required from final users. We combined knowledge management tools and methodologies with relational technology. We present an ontology-based approach that is flexible and efficient for dealing with complexity and change, integrated with a solid relational storage and a Web graphical user interface. Onto Clinical Research Forms (OntoCRF) is a framework for the definition, modeling, and instantiation of data repositories. It does not need any database design or programming. All required information to define a new project is explicitly stated in ontologies. Moreover, the user interface is built automatically on the fly as Web pages, whereas data are stored in a generic repository. This allows for immediate deployment and population of the database as well as instant online availability of any modification. OntoCRF is a complete framework to build data repositories with a solid relational storage. Driven by ontologies, OntoCRF is more flexible and efficient to deal with complexity and change than traditional systems and does not require very skilled technical people facilitating the engineering of clinical software systems.

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

PubMed Central

Green, Jason M.; Harnsomburana, Jaturon; Schaeffer, Mary L.; Lawrence, Carolyn J.; Shyu, Chi-Ren

2011-01-01

Model Organism Databases, including the various plant genome databases, collect and enable access to massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc, as well as textual descriptions of many of these entities. While a variety of basic browsing and search capabilities are available to allow researchers to query and peruse the names and attributes of phenotypic data, next-generation search mechanisms that allow querying and ranking of text descriptions are much less common. In addition, the plant community needs an innovative way to leverage the existing links in these databases to search groups of text descriptions simultaneously. Furthermore, though much time and effort have been afforded to the development of plant-related ontologies, the knowledge embedded in these ontologies remains largely unused in available plant search mechanisms. Addressing these issues, we have developed a unique search engine for mutant phenotypes from MaizeGDB. This advanced search mechanism integrates various text description sources in MaizeGDB to aid a user in retrieving desired mutant phenotype information. Currently, descriptions of mutant phenotypes, loci and gene products are utilized collectively for each search, though expansion of the search mechanism to include other sources is straightforward. The retrieval engine, to our knowledge, is the first engine to exploit the content and structure of available domain ontologies, currently the Plant and Gene Ontologies, to expand and enrich retrieval results in major plant genomic databases. Database URL: http:www.PhenomicsWorld.org/QBTA.php PMID:21558151

Application of Ontology Technology in Health Statistic Data Analysis.

PubMed

Guo, Minjiang; Hu, Hongpu; Lei, Xingyun

2017-01-01

Research Purpose: establish health management ontology for analysis of health statistic data. Proposed Methods: this paper established health management ontology based on the analysis of the concepts in China Health Statistics Yearbook, and used protégé to define the syntactic and semantic structure of health statistical data. six classes of top-level ontology concepts and their subclasses had been extracted and the object properties and data properties were defined to establish the construction of these classes. By ontology instantiation, we can integrate multi-source heterogeneous data and enable administrators to have an overall understanding and analysis of the health statistic data. ontology technology provides a comprehensive and unified information integration structure of the health management domain and lays a foundation for the efficient analysis of multi-source and heterogeneous health system management data and enhancement of the management efficiency.

An approach to development of ontological knowledge base in the field of scientific and research activity in Russia

NASA Astrophysics Data System (ADS)

Murtazina, M. Sh; Avdeenko, T. V.

2018-05-01

The state of art and the progress in application of semantic technologies in the field of scientific and research activity have been analyzed. Even elementary empirical comparison has shown that the semantic search engines are superior in all respects to conventional search technologies. However, semantic information technologies are insufficiently used in the field of scientific and research activity in Russia. In present paper an approach to construction of ontological model of knowledge base is proposed. The ontological model is based on the upper-level ontology and the RDF mechanism for linking several domain ontologies. The ontological model is implemented in the Protégé environment.

Towards Agile Ontology Maintenance

NASA Astrophysics Data System (ADS)

Luczak-Rösch, Markus

Ontologies are an appropriate means to represent knowledge on the Web. Research on ontology engineering reached practices for an integrative lifecycle support. However, a broader success of ontologies in Web-based information systems remains unreached while the more lightweight semantic approaches are rather successful. We assume, paired with the emerging trend of services and microservices on the Web, new dynamic scenarios gain momentum in which a shared knowledge base is made available to several dynamically changing services with disparate requirements. Our work envisions a step towards such a dynamic scenario in which an ontology adapts to the requirements of the accessing services and applications as well as the user's needs in an agile way and reduces the experts' involvement in ontology maintenance processes.

A common layer of interoperability for biomedical ontologies based on OWL EL.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Wimalaratne, Sarala; Rebholz-Schuhmann, Dietrich; Schofield, Paul; Gkoutos, Georgios V

2011-04-01

Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their semantics explicitly. However, current biomedical ontologies do not facilitate data integration and interoperability yet, since reasoning over these ontologies is very complex and cannot be performed efficiently or is even impossible. We propose the use of less expressive subsets of ontology representation languages to enable efficient reasoning and achieve the goal of genuine interoperability between ontologies. We present and evaluate EL Vira, a framework that transforms OWL ontologies into the OWL EL subset, thereby enabling the use of tractable reasoning. We illustrate which OWL constructs and inferences are kept and lost following the conversion and demonstrate the performance gain of reasoning indicated by the significant reduction of processing time. We applied EL Vira to the open biomedical ontologies and provide a repository of ontologies resulting from this conversion. EL Vira creates a common layer of ontological interoperability that, for the first time, enables the creation of software solutions that can employ biomedical ontologies to perform inferences and answer complex queries to support scientific analyses. The EL Vira software is available from http://el-vira.googlecode.com and converted OBO ontologies and their mappings are available from http://bioonto.gen.cam.ac.uk/el-ont.

OPPL-Galaxy, a Galaxy tool for enhancing ontology exploitation as part of bioinformatics workflows

PubMed Central

2013-01-01

Background Biomedical ontologies are key elements for building up the Life Sciences Semantic Web. Reusing and building biomedical ontologies requires flexible and versatile tools to manipulate them efficiently, in particular for enriching their axiomatic content. The Ontology Pre Processor Language (OPPL) is an OWL-based language for automating the changes to be performed in an ontology. OPPL augments the ontologists’ toolbox by providing a more efficient, and less error-prone, mechanism for enriching a biomedical ontology than that obtained by a manual treatment. Results We present OPPL-Galaxy, a wrapper for using OPPL within Galaxy. The functionality delivered by OPPL (i.e. automated ontology manipulation) can be combined with the tools and workflows devised within the Galaxy framework, resulting in an enhancement of OPPL. Use cases are provided in order to demonstrate OPPL-Galaxy’s capability for enriching, modifying and querying biomedical ontologies. Conclusions Coupling OPPL-Galaxy with other bioinformatics tools of the Galaxy framework results in a system that is more than the sum of its parts. OPPL-Galaxy opens a new dimension of analyses and exploitation of biomedical ontologies, including automated reasoning, paving the way towards advanced biological data analyses. PMID:23286517

Ontology construction and application in practice case study of health tourism in Thailand.

PubMed

Chantrapornchai, Chantana; Choksuchat, Chidchanok

2016-01-01

Ontology is one of the key components in semantic webs. It contains the core knowledge for an effective search. However, building ontology requires the carefully-collected knowledge which is very domain-sensitive. In this work, we present the practice of ontology construction for a case study of health tourism in Thailand. The whole process follows the METHONTOLOGY approach, which consists of phases: information gathering, corpus study, ontology engineering, evaluation, publishing, and the application construction. Different sources of data such as structure web documents like HTML and other documents are acquired in the information gathering process. The tourism corpora from various tourism texts and standards are explored. The ontology is evaluated in two aspects: automatic reasoning using Pellet, and RacerPro, and the questionnaires, used to evaluate by experts of the domains: tourism domain experts and ontology experts. The ontology usability is demonstrated via the semantic web application and via example axioms. The developed ontology is actually the first health tourism ontology in Thailand with the published application.

Building an ontology of pulmonary diseases with natural language processing tools using textual corpora.

PubMed

Baneyx, Audrey; Charlet, Jean; Jaulent, Marie-Christine

2007-01-01

Pathologies and acts are classified in thesauri to help physicians to code their activity. In practice, the use of thesauri is not sufficient to reduce variability in coding and thesauri are not suitable for computer processing. We think the automation of the coding task requires a conceptual modeling of medical items: an ontology. Our task is to help lung specialists code acts and diagnoses with software that represents medical knowledge of this concerned specialty by an ontology. The objective of the reported work was to build an ontology of pulmonary diseases dedicated to the coding process. To carry out this objective, we develop a precise methodological process for the knowledge engineer in order to build various types of medical ontologies. This process is based on the need to express precisely in natural language the meaning of each concept using differential semantics principles. A differential ontology is a hierarchy of concepts and relationships organized according to their similarities and differences. Our main research hypothesis is to apply natural language processing tools to corpora to develop the resources needed to build the ontology. We consider two corpora, one composed of patient discharge summaries and the other being a teaching book. We propose to combine two approaches to enrich the ontology building: (i) a method which consists of building terminological resources through distributional analysis and (ii) a method based on the observation of corpus sequences in order to reveal semantic relationships. Our ontology currently includes 1550 concepts and the software implementing the coding process is still under development. Results show that the proposed approach is operational and indicates that the combination of these methods and the comparison of the resulting terminological structures give interesting clues to a knowledge engineer for the building of an ontology.

Margin based ontology sparse vector learning algorithm and applied in biology science.

PubMed

Gao, Wei; Qudair Baig, Abdul; Ali, Haidar; Sajjad, Wasim; Reza Farahani, Mohammad

2017-01-01

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

The OCareCloudS project: Toward organizing care through trusted cloud services.

PubMed

De Backere, Femke; Ongenae, Femke; Vannieuwenborg, Frederic; Van Ooteghem, Jan; Duysburgh, Pieter; Jansen, Arne; Hoebeke, Jeroen; Wuyts, Kim; Rossey, Jen; Van den Abeele, Floris; Willems, Karen; Decancq, Jasmien; Annema, Jan Henk; Sulmon, Nicky; Van Landuyt, Dimitri; Verstichel, Stijn; Crombez, Pieter; Ackaert, Ann; De Grooff, Dirk; Jacobs, An; De Turck, Filip

2016-01-01

The increasing elderly population and the shift from acute to chronic illness makes it difficult to care for people in hospitals and rest homes. Moreover, elderly people, if given a choice, want to stay at home as long as possible. In this article, the methodologies to develop a cloud-based semantic system, offering valuable information and knowledge-based services, are presented. The information and services are related to the different personal living hemispheres of the patient, namely the daily care-related needs, the social needs and the daily life assistance. Ontologies are used to facilitate the integration, analysis, aggregation and efficient use of all the available data in the cloud. By using an interdisciplinary research approach, where user researchers, (ontology) engineers, researchers and domain stakeholders are at the forefront, a platform can be developed of great added value for the patients that want to grow old in their own home and for their caregivers.

Ontology-Driven Business Modelling: Improving the Conceptual Representation of the REA Ontology

NASA Astrophysics Data System (ADS)

Gailly, Frederik; Poels, Geert

Business modelling research is increasingly interested in exploring how domain ontologies can be used as reference models for business models. The Resource Event Agent (REA) ontology is a primary candidate for ontology-driven modelling of business processes because the REA point of view on business reality is close to the conceptual modelling perspective on business models. In this paper Ontology Engineering principles are employed to reengineer REA in order to make it more suitable for ontology-driven business modelling. The new conceptual representation of REA that we propose uses a single representation formalism, includes a more complete domain axiomatizat-ion (containing definitions of concepts, concept relations and ontological axioms), and is proposed as a generic model that can be instantiated to create valid business models. The effects of these proposed improvements on REA-driven business modelling are demonstrated using a business modelling example.

Managing changes in distributed biomedical ontologies using hierarchical distributed graph transformation.

PubMed

Shaban-Nejad, Arash; Haarslev, Volker

2015-01-01

The issue of ontology evolution and change management is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies and interactions with other existing ontologies have been widely neglected. In our research, after revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, Represent, Legitimate and Reproduce (RLR), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general and aids in tracking and representing the changes, particularly through the use of category theory and hierarchical graph transformation.

Using ontology network structure in text mining.

PubMed

Berndt, Donald J; McCart, James A; Luther, Stephen L

2010-11-13

Statistical text mining treats documents as bags of words, with a focus on term frequencies within documents and across document collections. Unlike natural language processing (NLP) techniques that rely on an engineered vocabulary or a full-featured ontology, statistical approaches do not make use of domain-specific knowledge. The freedom from biases can be an advantage, but at the cost of ignoring potentially valuable knowledge. The approach proposed here investigates a hybrid strategy based on computing graph measures of term importance over an entire ontology and injecting the measures into the statistical text mining process. As a starting point, we adapt existing search engine algorithms such as PageRank and HITS to determine term importance within an ontology graph. The graph-theoretic approach is evaluated using a smoking data set from the i2b2 National Center for Biomedical Computing, cast as a simple binary classification task for categorizing smoking-related documents, demonstrating consistent improvements in accuracy.

Ontology Design Patterns as Interfaces (invited)

NASA Astrophysics Data System (ADS)

Janowicz, K.

2015-12-01

In recent years ontology design patterns (ODP) have gained popularity among knowledge engineers. ODPs are modular but self-contained building blocks that are reusable and extendible. They minimize the amount of ontological commitments and thereby are easier to integrate than large monolithic ontologies. Typically, patterns are not directly used to annotate data or to model certain domain problems but are combined and extended to form data and purpose-driven local ontologies that serve the needs of specific applications or communities. By relying on a common set of patterns these local ontologies can be aligned to improve interoperability and enable federated queries without enforcing a top-down model of the domain. In previous work, we introduced ontological views as layer on top of ontology design patterns to ease the reuse, combination, and integration of patterns. While the literature distinguishes multiple types of patterns, e.g., content patterns or logical patterns, we propose to use them as interfaces here to guide the development of ontology-driven systems.

Ontological analysis of SNOMED CT.

PubMed

Héja, Gergely; Surján, György; Varga, Péter

2008-10-27

SNOMED CT is the most comprehensive medical terminology. However, its use for intelligent services based on formal reasoning is questionable. The analysis of the structure of SNOMED CT is based on the formal top-level ontology DOLCE. The analysis revealed several ontological and knowledge-engineering errors, the most important are errors in the hierarchy (mostly from an ontological point of view, but also regarding medical aspects) and the mixing of subsumption relations with other types (mostly 'part of'). The found errors impede formal reasoning. The paper presents a possible way to correct these problems.

Data-driven Ontology Development: A Case Study at NASA's Atmospheric Science Data Center

NASA Astrophysics Data System (ADS)

Hertz, J.; Huffer, E.; Kusterer, J.

2012-12-01

Well-founded ontologies are key to enabling transformative semantic technologies and accelerating scientific research. One example is semantically enabled search and discovery, making scientific data accessible and more understandable by accurately modeling a complex domain. The ontology creation process remains a challenge for many anxious to pursue semantic technologies. The key may be that the creation process -- whether formal, community-based, automated or semi-automated -- should encompass not only a foundational core and supplemental resources but also a focus on the purpose or mission the ontology is created to support. Are there tools or processes to de-mystify, assess or enhance the resulting ontology? We suggest that comparison and analysis of a domain-focused ontology can be made using text engineering tools for information extraction, tokenizers, named entity transducers and others. The results are analyzed to ensure the ontology reflects the core purpose of the domain's mission and that the ontology integrates and describes the supporting data in the language of the domain - how the science is analyzed and discussed among all users of the data. Commonalities and relationships among domain resources describing the Clouds and Earth's Radiant Energy (CERES) Bi-Directional Scan (BDS) datasets from NASA's Atmospheric Science Data Center are compared. The domain resources include: a formal ontology created for CERES; scientific works such as papers, conference proceedings and notes; information extracted from the datasets (i.e., header metadata); and BDS scientific documentation (Algorithm Theoretical Basis Documents, collection guides, data quality summaries and others). These resources are analyzed using the open source software General Architecture for Text Engineering, a mature framework for computational tasks involving human language.

iSMART: Ontology-based Semantic Query of CDA Documents

PubMed Central

Liu, Shengping; Ni, Yuan; Mei, Jing; Li, Hanyu; Xie, Guotong; Hu, Gang; Liu, Haifeng; Hou, Xueqiao; Pan, Yue

2009-01-01

The Health Level 7 Clinical Document Architecture (CDA) is widely accepted as the format for electronic clinical document. With the rich ontological references in CDA documents, the ontology-based semantic query could be performed to retrieve CDA documents. In this paper, we present iSMART (interactive Semantic MedicAl Record reTrieval), a prototype system designed for ontology-based semantic query of CDA documents. The clinical information in CDA documents will be extracted into RDF triples by a declarative XML to RDF transformer. An ontology reasoner is developed to infer additional information by combining the background knowledge from SNOMED CT ontology. Then an RDF query engine is leveraged to enable the semantic queries. This system has been evaluated using the real clinical documents collected from a large hospital in southern China. PMID:20351883

Model Driven Engineering with Ontology Technologies

NASA Astrophysics Data System (ADS)

Staab, Steffen; Walter, Tobias; Gröner, Gerd; Parreiras, Fernando Silva

Ontologies constitute formal models of some aspect of the world that may be used for drawing interesting logical conclusions even for large models. Software models capture relevant characteristics of a software artifact to be developed, yet, most often these software models have limited formal semantics, or the underlying (often graphical) software language varies from case to case in a way that makes it hard if not impossible to fix its semantics. In this contribution, we survey the use of ontology technologies for software modeling in order to carry over advantages from ontology technologies to the software modeling domain. It will turn out that ontology-based metamodels constitute a core means for exploiting expressive ontology reasoning in the software modeling domain while remaining flexible enough to accommodate varying needs of software modelers.

Open the `black box' creativity and innovation: a study of activities in R&D departments. Some prospects for engineering education

NASA Astrophysics Data System (ADS)

Millet, Charlyne; Oget, David; Cavallucci, Denis

2017-11-01

Innovation is a key component to the success and longevity of companies. Our research opens the 'black box' of creativity and innovation in R&D teams. We argue that understanding the nature of R&D projects in terms of creativity/innovation, efficiency/inefficiency, is important for designing education policies and improving engineering curriculum. Our research addresses the inventive design process, a lesser known aspect of the innovation process, in 197 R&D departments of industrial sector companies in France. One fundamental issue facing companies is to evaluate processes and results of innovation. Results show that the evaluation of innovation is confined by a lack of methodology of inventive projects. We will be establishing the foundations of a formal ontology for inventive design projects and finally some recommendations for engineering education.

Gradient Learning Algorithms for Ontology Computing

PubMed Central

Gao, Wei; Zhu, Linli

2014-01-01

The gradient learning model has been raising great attention in view of its promising perspectives for applications in statistics, data dimensionality reducing, and other specific fields. In this paper, we raise a new gradient learning model for ontology similarity measuring and ontology mapping in multidividing setting. The sample error in this setting is given by virtue of the hypothesis space and the trick of ontology dividing operator. Finally, two experiments presented on plant and humanoid robotics field verify the efficiency of the new computation model for ontology similarity measure and ontology mapping applications in multidividing setting. PMID:25530752

Ontology to relational database transformation for web application development and maintenance

NASA Astrophysics Data System (ADS)

Mahmudi, Kamal; Inggriani Liem, M. M.; Akbar, Saiful

2018-03-01

Ontology is used as knowledge representation while database is used as facts recorder in a KMS (Knowledge Management System). In most applications, data are managed in a database system and updated through the application and then they are transformed to knowledge as needed. Once a domain conceptor defines the knowledge in the ontology, application and database can be generated from the ontology. Most existing frameworks generate application from its database. In this research, ontology is used for generating the application. As the data are updated through the application, a mechanism is designed to trigger an update to the ontology so that the application can be rebuilt based on the newest ontology. By this approach, a knowledge engineer has a full flexibility to renew the application based on the latest ontology without dependency to a software developer. In many cases, the concept needs to be updated when the data changed. The framework is built and tested in a spring java environment. A case study was conducted to proof the concepts.

Semi Automatic Ontology Instantiation in the domain of Risk Management

NASA Astrophysics Data System (ADS)

Makki, Jawad; Alquier, Anne-Marie; Prince, Violaine

One of the challenging tasks in the context of Ontological Engineering is to automatically or semi-automatically support the process of Ontology Learning and Ontology Population from semi-structured documents (texts). In this paper we describe a Semi-Automatic Ontology Instantiation method from natural language text, in the domain of Risk Management. This method is composed from three steps 1 ) Annotation with part-of-speech tags, 2) Semantic Relation Instances Extraction, 3) Ontology instantiation process. It's based on combined NLP techniques using human intervention between steps 2 and 3 for control and validation. Since it heavily relies on linguistic knowledge it is not domain dependent which is a good feature for portability between the different fields of risk management application. The proposed methodology uses the ontology of the PRIMA1 project (supported by the European community) as a Generic Domain Ontology and populates it via an available corpus. A first validation of the approach is done through an experiment with Chemical Fact Sheets from Environmental Protection Agency2.

Proposed actions are no actions: re-modeling an ontology design pattern with a realist top-level ontology.

PubMed

Seddig-Raufie, Djamila; Jansen, Ludger; Schober, Daniel; Boeker, Martin; Grewe, Niels; Schulz, Stefan

2012-09-21

Ontology Design Patterns (ODPs) are representational artifacts devised to offer solutions for recurring ontology design problems. They promise to enhance the ontology building process in terms of flexibility, re-usability and expansion, and to make the result of ontology engineering more predictable. In this paper, we analyze ODP repositories and investigate their relation with upper-level ontologies. In particular, we compare the BioTop upper ontology to the Action ODP from the NeOn an ODP repository. In view of the differences in the respective approaches, we investigate whether the Action ODP can be embedded into BioTop. We demonstrate that this requires re-interpreting the meaning of classes of the NeOn Action ODP in the light of the precepts of realist ontologies. As a result, the re-design required clarifying the ontological commitment of the ODP classes by assigning them to top-level categories. Thus, ambiguous definitions are avoided. Classes of real entities are clearly distinguished from classes of information artifacts. The proposed approach avoids the commitment to the existence of unclear future entities which underlies the NeOn Action ODP. Our re-design is parsimonious in the sense that existing BioTop content proved to be largely sufficient to define the different types of actions and plans. The proposed model demonstrates that an expressive upper-level ontology provides enough resources and expressivity to represent even complex ODPs, here shown with the different flavors of Action as proposed in the NeOn ODP. The advantage of ODP inclusion into a top-level ontology is the given predetermined dependency of each class, an existing backbone structure and well-defined relations. Our comparison shows that the use of some ODPs is more likely to cause problems for ontology developers, rather than to guide them. Besides the structural properties, the explanation of classification results were particularly hard to grasp for 'self-sufficient' ODPs as compared with implemented and 'embedded' upper-level structures which, for example in the case of BioTop, offer a detailed description of classes and relations in an axiomatic network. This ensures unambiguous interpretation and provides more concise constraints to leverage on in the ontology engineering process.

Developing a Domain Ontology: the Case of Water Cycle and Hydrology

NASA Astrophysics Data System (ADS)

Gupta, H.; Pozzi, W.; Piasecki, M.; Imam, B.; Houser, P.; Raskin, R.; Ramachandran, R.; Martinez Baquero, G.

2008-12-01

A semantic web ontology enables semantic data integration and semantic smart searching. Several organizations have attempted to implement smart registration and integration or searching using ontologies. These are the NOESIS (NSF project: LEAD) and HydroSeek (NSF project: CUAHS HIS) data discovery engines and the NSF project GEON. All three applications use ontologies to discover data from multiple sources and projects. The NASA WaterNet project was established to identify creative, innovative ways to bridge NASA research results to real world applications, linking decision support needs to available data, observations, and modeling capability. WaterNet (NASA project) utilized the smart query tool Noesis as a testbed to test whether different ontologies (and different catalog searches) could be combined to match resources with user needs. NOESIS contains the upper level SWEET ontology that accepts plug in domain ontologies to refine user search queries, reducing the burden of multiple keyword searches. Another smart search interface was that developed for CUAHSI, HydroSeek, that uses a multi-layered concept search ontology, tagging variables names from any number of data sources to specific leaf and higher level concepts on which the search is executed. This approach has proven to be quite successful in mitigating semantic heterogeneity as the user does not need to know the semantic specifics of each data source system but just uses a set of common keywords to discover the data for a specific temporal and geospatial domain. This presentation will show tests with Noesis and Hydroseek lead to the conclusion that the construction of a complex, and highly heterogeneous water cycle ontology requires multiple ontology modules. To illustrate the complexity and heterogeneity of a water cycle ontology, Hydroseek successfully utilizes WaterOneFlow to integrate data across multiple different data collections, such as USGS NWIS. However,different methodologies are employed by the Earth Science, the Hydrological, and Hydraulic Engineering Communities, and each community employs models that require different input data. If a sub-domain ontology is created for each of these,describing water balance calculations, then the resulting structure of the semantic network describing these various terms can be rather complex, heterogeneous, and overlapping, and will require "mapping" between equivalent terms in the ontologies, along with the development of an upper level conceptual or domain ontology to utilize and link to those already in existence.

Tackling the challenges of matching biomedical ontologies.

PubMed

Faria, Daniel; Pesquita, Catia; Mott, Isabela; Martins, Catarina; Couto, Francisco M; Cruz, Isabel F

2018-01-15

Biomedical ontologies pose several challenges to ontology matching due both to the complexity of the biomedical domain and to the characteristics of the ontologies themselves. The biomedical tracks in the Ontology Matching Evaluation Initiative (OAEI) have spurred the development of matching systems able to tackle these challenges, and benchmarked their general performance. In this study, we dissect the strategies employed by matching systems to tackle the challenges of matching biomedical ontologies and gauge the impact of the challenges themselves on matching performance, using the AgreementMakerLight (AML) system as the platform for this study. We demonstrate that the linear complexity of the hash-based searching strategy implemented by most state-of-the-art ontology matching systems is essential for matching large biomedical ontologies efficiently. We show that accounting for all lexical annotations (e.g., labels and synonyms) in biomedical ontologies leads to a substantial improvement in F-measure over using only the primary name, and that accounting for the reliability of different types of annotations generally also leads to a marked improvement. Finally, we show that cross-references are a reliable source of information and that, when using biomedical ontologies as background knowledge, it is generally more reliable to use them as mediators than to perform lexical expansion. We anticipate that translating traditional matching algorithms to the hash-based searching paradigm will be a critical direction for the future development of the field. Improving the evaluation carried out in the biomedical tracks of the OAEI will also be important, as without proper reference alignments there is only so much that can be ascertained about matching systems or strategies. Nevertheless, it is clear that, to tackle the various challenges posed by biomedical ontologies, ontology matching systems must be able to efficiently combine multiple strategies into a mature matching approach.

User centered and ontology based information retrieval system for life sciences.

PubMed

Sy, Mohameth-François; Ranwez, Sylvie; Montmain, Jacky; Regnault, Armelle; Crampes, Michel; Ranwez, Vincent

2012-01-25

Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help.

User centered and ontology based information retrieval system for life sciences

PubMed Central

2012-01-01

Background Because of the increasing number of electronic resources, designing efficient tools to retrieve and exploit them is a major challenge. Some improvements have been offered by semantic Web technologies and applications based on domain ontologies. In life science, for instance, the Gene Ontology is widely exploited in genomic applications and the Medical Subject Headings is the basis of biomedical publications indexation and information retrieval process proposed by PubMed. However current search engines suffer from two main drawbacks: there is limited user interaction with the list of retrieved resources and no explanation for their adequacy to the query is provided. Users may thus be confused by the selection and have no idea on how to adapt their queries so that the results match their expectations. Results This paper describes an information retrieval system that relies on domain ontology to widen the set of relevant documents that is retrieved and that uses a graphical rendering of query results to favor user interactions. Semantic proximities between ontology concepts and aggregating models are used to assess documents adequacy with respect to a query. The selection of documents is displayed in a semantic map to provide graphical indications that make explicit to what extent they match the user's query; this man/machine interface favors a more interactive and iterative exploration of data corpus, by facilitating query concepts weighting and visual explanation. We illustrate the benefit of using this information retrieval system on two case studies one of which aiming at collecting human genes related to transcription factors involved in hemopoiesis pathway. Conclusions The ontology based information retrieval system described in this paper (OBIRS) is freely available at: http://www.ontotoolkit.mines-ales.fr/ObirsClient/. This environment is a first step towards a user centred application in which the system enlightens relevant information to provide decision help. PMID:22373375

Semantics-Based Interoperability Framework for the Geosciences

NASA Astrophysics Data System (ADS)

Sinha, A.; Malik, Z.; Raskin, R.; Barnes, C.; Fox, P.; McGuinness, D.; Lin, K.

2008-12-01

Interoperability between heterogeneous data, tools and services is required to transform data to knowledge. To meet geoscience-oriented societal challenges such as forcing of climate change induced by volcanic eruptions, we suggest the need to develop semantic interoperability for data, services, and processes. Because such scientific endeavors require integration of multiple data bases associated with global enterprises, implicit semantic-based integration is impossible. Instead, explicit semantics are needed to facilitate interoperability and integration. Although different types of integration models are available (syntactic or semantic) we suggest that semantic interoperability is likely to be the most successful pathway. Clearly, the geoscience community would benefit from utilization of existing XML-based data models, such as GeoSciML, WaterML, etc to rapidly advance semantic interoperability and integration. We recognize that such integration will require a "meanings-based search, reasoning and information brokering", which will be facilitated through inter-ontology relationships (ontologies defined for each discipline). We suggest that Markup languages (MLs) and ontologies can be seen as "data integration facilitators", working at different abstraction levels. Therefore, we propose to use an ontology-based data registration and discovery approach to compliment mark-up languages through semantic data enrichment. Ontologies allow the use of formal and descriptive logic statements which permits expressive query capabilities for data integration through reasoning. We have developed domain ontologies (EPONT) to capture the concept behind data. EPONT ontologies are associated with existing ontologies such as SUMO, DOLCE and SWEET. Although significant efforts have gone into developing data (object) ontologies, we advance the idea of developing semantic frameworks for additional ontologies that deal with processes and services. This evolutionary step will facilitate the integrative capabilities of scientists as we examine the relationships between data and external factors such as processes that may influence our understanding of "why" certain events happen. We emphasize the need to go from analysis of data to concepts related to scientific principles of thermodynamics, kinetics, heat flow, mass transfer, etc. Towards meeting these objectives, we report on a pair of related service engines: DIA (Discovery, integration and analysis), and SEDRE (Semantically-Enabled Data Registration Engine) that utilize ontologies for semantic interoperability and integration.

owlcpp: a C++ library for working with OWL ontologies.

PubMed

Levin, Mikhail K; Cowell, Lindsay G

2015-01-01

The increasing use of ontologies highlights the need for a library for working with ontologies that is efficient, accessible from various programming languages, and compatible with common computational platforms. We developed owlcpp, a library for storing and searching RDF triples, parsing RDF/XML documents, converting triples into OWL axioms, and reasoning. The library is written in ISO-compliant C++ to facilitate efficiency, portability, and accessibility from other programming languages. Internally, owlcpp uses the Raptor RDF Syntax library for parsing RDF/XML and the FaCT++ library for reasoning. The current version of owlcpp is supported under Linux, OSX, and Windows platforms and provides an API for Python. The results of our evaluation show that, compared to other commonly used libraries, owlcpp is significantly more efficient in terms of memory usage and searching RDF triple stores. owlcpp performs strict parsing and detects errors ignored by other libraries, thus reducing the possibility of incorrect semantic interpretation of ontologies. owlcpp is available at http://owl-cpp.sf.net/ under the Boost Software License, Version 1.0.

OntoCAT -- simple ontology search and integration in Java, R and REST/JavaScript

PubMed Central

2011-01-01

Background Ontologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups. Results OntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application. Conclusions OntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases. Availability http://www.ontocat.org PMID:21619703

OntoCAT--simple ontology search and integration in Java, R and REST/JavaScript.

PubMed

Adamusiak, Tomasz; Burdett, Tony; Kurbatova, Natalja; Joeri van der Velde, K; Abeygunawardena, Niran; Antonakaki, Despoina; Kapushesky, Misha; Parkinson, Helen; Swertz, Morris A

2011-05-29

Ontologies have become an essential asset in the bioinformatics toolbox and a number of ontology access resources are now available, for example, the EBI Ontology Lookup Service (OLS) and the NCBO BioPortal. However, these resources differ substantially in mode, ease of access, and ontology content. This makes it relatively difficult to access each ontology source separately, map their contents to research data, and much of this effort is being replicated across different research groups. OntoCAT provides a seamless programming interface to query heterogeneous ontology resources including OLS and BioPortal, as well as user-specified local OWL and OBO files. Each resource is wrapped behind easy to learn Java, Bioconductor/R and REST web service commands enabling reuse and integration of ontology software efforts despite variation in technologies. It is also available as a stand-alone MOLGENIS database and a Google App Engine application. OntoCAT provides a robust, configurable solution for accessing ontology terms specified locally and from remote services, is available as a stand-alone tool and has been tested thoroughly in the ArrayExpress, MOLGENIS, EFO and Gen2Phen phenotype use cases. http://www.ontocat.org.

Knowledge Engineering and Education.

ERIC Educational Resources Information Center

Lopez, Antonio M., Jr.; Donlon, James

2001-01-01

Discusses knowledge engineering, computer software, and possible applications in the field of education. Highlights include the distinctions between data, information, and knowledge; knowledge engineering as a subfield of artificial intelligence; knowledge acquisition; data mining; ontology development for subject terms; cognitive apprentices; and…

Ontology-Driven Information Integration

NASA Technical Reports Server (NTRS)

Tissot, Florence; Menzel, Chris

2005-01-01

Ontology-driven information integration (ODII) is a method of computerized, automated sharing of information among specialists who have expertise in different domains and who are members of subdivisions of a large, complex enterprise (e.g., an engineering project, a government agency, or a business). In ODII, one uses rigorous mathematical techniques to develop computational models of engineering and/or business information and processes. These models are then used to develop software tools that support the reliable processing and exchange of information among the subdivisions of this enterprise or between this enterprise and other enterprises.

Termontography and DOGMA for Knowledge Engineering within PROLIX

NASA Astrophysics Data System (ADS)

de Baer, Peter; Meersman, Robert; Temmerman, Rita

In this article, we describe our ongoing research to combine two approaches, i.e. Termontography and DOGMA, for knowledge engineering. Both approaches have in common that they mainly rely on natural language to describe meaning. Termontography is a special form of terminography that results in an ontologically structured terminological resource. DOGMA is an abbreviation of Developing Ontology Guided Mediation for Agents. The DOGMA approach results in a scalable and modular ontology that can easily be (re)used for different domains and applications. Both Termontography and DOGMA have already been used separately during several research projects. In this article we explain how both approaches are being combined within the PROLIX project, and what the advantages of this combination are. The goal of PROLIX is to develop an open, integrated reference architecture for process-oriented learning and information exchange.

Development of an Agile Knowledge Engineering Framework in Support of Multi-Disciplinary Translational Research

PubMed Central

Borlawsky, Tara B.; Dhaval, Rakesh; Hastings, Shannon L.; Payne, Philip R. O.

2009-01-01

In October 2006, the National Institutes of Health launched a new national consortium, funded through Clinical and Translational Science Awards (CTSA), with the primary objective of improving the conduct and efficiency of the inherently multi-disciplinary field of translational research. To help meet this goal, the Ohio State University Center for Clinical and Translational Science has launched a knowledge management initiative that is focused on facilitating widespread semantic interoperability among administrative, basic science, clinical and research computing systems, both internally and among the translational research community at-large, through the integration of domain-specific standard terminologies and ontologies with local annotations. This manuscript describes an agile framework that builds upon prevailing knowledge engineering and semantic interoperability methods, and will be implemented as part this initiative. PMID:21347164

Development of an agile knowledge engineering framework in support of multi-disciplinary translational research.

PubMed

Borlawsky, Tara B; Dhaval, Rakesh; Hastings, Shannon L; Payne, Philip R O

2009-03-01

In October 2006, the National Institutes of Health launched a new national consortium, funded through Clinical and Translational Science Awards (CTSA), with the primary objective of improving the conduct and efficiency of the inherently multi-disciplinary field of translational research. To help meet this goal, the Ohio State University Center for Clinical and Translational Science has launched a knowledge management initiative that is focused on facilitating widespread semantic interoperability among administrative, basic science, clinical and research computing systems, both internally and among the translational research community at-large, through the integration of domain-specific standard terminologies and ontologies with local annotations. This manuscript describes an agile framework that builds upon prevailing knowledge engineering and semantic interoperability methods, and will be implemented as part this initiative.

The ACGT Master Ontology and its applications – Towards an ontology-driven cancer research and management system

PubMed Central

Brochhausen, Mathias; Spear, Andrew D.; Cocos, Cristian; Weiler, Gabriele; Martín, Luis; Anguita, Alberto; Stenzhorn, Holger; Daskalaki, Evangelia; Schera, Fatima; Schwarz, Ulf; Sfakianakis, Stelios; Kiefer, Stephan; Dörr, Martin; Graf, Norbert; Tsiknakis, Manolis

2017-01-01

Objective This paper introduces the objectives, methods and results of ontology development in the EU co-funded project Advancing Clinico-genomic Trials on Cancer – Open Grid Services for Improving Medical Knowledge Discovery (ACGT). While the available data in the life sciences has recently grown both in amount and quality, the full exploitation of it is being hindered by the use of different underlying technologies, coding systems, category schemes and reporting methods on the part of different research groups. The goal of the ACGT project is to contribute to the resolution of these problems by developing an ontology-driven, semantic grid services infrastructure that will enable efficient execution of discovery-driven scientific workflows in the context of multi-centric, post-genomic clinical trials. The focus of the present paper is the ACGT Master Ontology (MO). Methods ACGT project researchers undertook a systematic review of existing domain and upper-level ontologies, as well as of existing ontology design software, implementation methods, and end-user interfaces. This included the careful study of best practices, design principles and evaluation methods for ontology design, maintenance, implementation, and versioning, as well as for use on the part of domain experts and clinicians. Results To date, the results of the ACGT project include (i) the development of a master ontology (the ACGT-MO) based on clearly defined principles of ontology development and evaluation; (ii) the development of a technical infra-structure (the ACGT Platform) that implements the ACGT-MO utilizing independent tools, components and resources that have been developed based on open architectural standards, and which includes an application updating and evolving the ontology efficiently in response to end-user needs; and (iii) the development of an Ontology-based Trial Management Application (ObTiMA) that integrates the ACGT-MO into the design process of clinical trials in order to guarantee automatic semantic integration without the need to perform a separate mapping process. PMID:20438862

Semi-automated ontology generation within OBO-Edit.

PubMed

Wächter, Thomas; Schroeder, Michael

2010-06-15

Ontologies and taxonomies have proven highly beneficial for biocuration. The Open Biomedical Ontology (OBO) Foundry alone lists over 90 ontologies mainly built with OBO-Edit. Creating and maintaining such ontologies is a labour-intensive, difficult, manual process. Automating parts of it is of great importance for the further development of ontologies and for biocuration. We have developed the Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG), a system which supports the creation and extension of OBO ontologies by semi-automatically generating terms, definitions and parent-child relations from text in PubMed, the web and PDF repositories. DOG4DAG is seamlessly integrated into OBO-Edit. It generates terms by identifying statistically significant noun phrases in text. For definitions and parent-child relations it employs pattern-based web searches. We systematically evaluate each generation step using manually validated benchmarks. The term generation leads to high-quality terms also found in manually created ontologies. Up to 78% of definitions are valid and up to 54% of child-ancestor relations can be retrieved. There is no other validated system that achieves comparable results. By combining the prediction of high-quality terms, definitions and parent-child relations with the ontology editor OBO-Edit we contribute a thoroughly validated tool for all OBO ontology engineers. DOG4DAG is available within OBO-Edit 2.1 at http://www.oboedit.org. Supplementary data are available at Bioinformatics online.

Transforming Systems Engineering through Model Centric Engineering

DTIC Science & Technology

2017-08-08

12 Figure 5. Semantic Web Technologies related to Layers of Abstraction ................................. 23 Figure 6. NASA /JPL Instantiation...of OpenMBEE (circa 2014) ................................................. 24 Figure 7. NASA /JPL Foundational Ontology for Systems Engineering...Engineering (DE) Transformation initiative, and our relationship that we have fostered with National Aeronautics and Space Administration ( NASA ) Jet

Ontological modelling of knowledge management for human-machine integrated design of ultra-precision grinding machine

NASA Astrophysics Data System (ADS)

Hong, Haibo; Yin, Yuehong; Chen, Xing

2016-11-01

Despite the rapid development of computer science and information technology, an efficient human-machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

CRAVE: a database, middleware and visualization system for phenotype ontologies.

PubMed

Gkoutos, Georgios V; Green, Eain C J; Greenaway, Simon; Blake, Andrew; Mallon, Ann-Marie; Hancock, John M

2005-04-01

A major challenge in modern biology is to link genome sequence information to organismal function. In many organisms this is being done by characterizing phenotypes resulting from mutations. Efficiently expressing phenotypic information requires combinatorial use of ontologies. However tools are not currently available to visualize combinations of ontologies. Here we describe CRAVE (Concept Relation Assay Value Explorer), a package allowing storage, active updating and visualization of multiple ontologies. CRAVE is a web-accessible JAVA application that accesses an underlying MySQL database of ontologies via a JAVA persistent middleware layer (Chameleon). This maps the database tables into discrete JAVA classes and creates memory resident, interlinked objects corresponding to the ontology data. These JAVA objects are accessed via calls through the middleware's application programming interface. CRAVE allows simultaneous display and linking of multiple ontologies and searching using Boolean and advanced searches.

Semantic Modeling of Requirements: Leveraging Ontologies in Systems Engineering

ERIC Educational Resources Information Center

Mir, Masood Saleem

2012-01-01

The interdisciplinary nature of "Systems Engineering" (SE), having "stakeholders" from diverse domains with orthogonal facets, and need to consider all stages of "lifecycle" of system during conception, can benefit tremendously by employing "Knowledge Engineering" (KE) to achieve semantic agreement among all…

Knowledge Representation and Management. From Ontology to Annotation. Findings from the Yearbook 2015 Section on Knowledge Representation and Management.

PubMed

Charlet, J; Darmoni, S J

2015-08-13

To summarize the best papers in the field of Knowledge Representation and Management (KRM). A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014. Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multilingual ontologies. Semantic models began to show their efficiency, coupled with annotation tools.

Knowledge Management within the Medical University.

PubMed

Rauzina, Svetlana Ye; Tikhonova, Tatiana A; Karpenko, Dmitriy S; Bogopolskiy, Gennady A; Zarubina, Tatiana V

2015-01-01

The aim of the work is studying the possibilities of ontological engineering in managing of medical knowledge. And also practical implementation of knowledge management system (KMS) in medical university. The educational process model is established that allows analyzing learning results within time scale. Glossary sub-system has been developed; ontologies of educational disciplines are constructed; environment for setup and solution of situational cases is established; ontological approach to assess competencies is developed. The possibilities of the system for solving situation tasks have been described. The approach to the evaluation of competence has been developed.

Ontology-Based Model Of Firm Competitiveness

NASA Astrophysics Data System (ADS)

Deliyska, Boryana; Stoenchev, Nikolay

2010-10-01

Competitiveness is important characteristics of each business organization (firm, company, corporation etc). It is of great significance for the organization existence and defines evaluation criteria of business success at microeconomical level. Each criterium comprises set of indicators with specific weight coefficients. In the work an ontology-based model of firm competitiveness is presented as a set of several mutually connected ontologies. It would be useful for knowledge structuring, standardization and sharing among experts and software engineers who develop application in the domain. Then the assessment of the competitiveness of various business organizations could be generated more effectively.

An ontology for major histocompatibility restriction.

PubMed

Vita, Randi; Overton, James A; Seymour, Emily; Sidney, John; Kaufman, Jim; Tallmadge, Rebecca L; Ellis, Shirley; Hammond, John; Butcher, Geoff W; Sette, Alessandro; Peters, Bjoern

2016-01-01

MHC molecules are a highly diverse family of proteins that play a key role in cellular immune recognition. Over time, different techniques and terminologies have been developed to identify the specific type(s) of MHC molecule involved in a specific immune recognition context. No consistent nomenclature exists across different vertebrate species. To correctly represent MHC related data in The Immune Epitope Database (IEDB), we built upon a previously established MHC ontology and created an ontology to represent MHC molecules as they relate to immunological experiments. This ontology models MHC protein chains from 16 species, deals with different approaches used to identify MHC, such as direct sequencing verses serotyping, relates engineered MHC molecules to naturally occurring ones, connects genetic loci, alleles, protein chains and multi-chain proteins, and establishes evidence codes for MHC restriction. Where available, this work is based on existing ontologies from the OBO foundry. Overall, representing MHC molecules provides a challenging and practically important test case for ontology building, and could serve as an example of how to integrate other ontology building efforts into web resources.

The use of concept maps during knowledge elicitation in ontology development processes – the nutrigenomics use case

PubMed Central

Castro, Alexander Garcia; Rocca-Serra, Philippe; Stevens, Robert; Taylor, Chris; Nashar, Karim; Ragan, Mark A; Sansone, Susanna-Assunta

2006-01-01

Background Incorporation of ontologies into annotations has enabled 'semantic integration' of complex data, making explicit the knowledge within a certain field. One of the major bottlenecks in developing bio-ontologies is the lack of a unified methodology. Different methodologies have been proposed for different scenarios, but there is no agreed-upon standard methodology for building ontologies. The involvement of geographically distributed domain experts, the need for domain experts to lead the design process, the application of the ontologies and the life cycles of bio-ontologies are amongst the features not considered by previously proposed methodologies. Results Here, we present a methodology for developing ontologies within the biological domain. We describe our scenario, competency questions, results and milestones for each methodological stage. We introduce the use of concept maps during knowledge acquisition phases as a feasible transition between domain expert and knowledge engineer. Conclusion The contributions of this paper are the thorough description of the steps we suggest when building an ontology, example use of concept maps, consideration of applicability to the development of lower-level ontologies and application to decentralised environments. We have found that within our scenario conceptual maps played an important role in the development process. PMID:16725019

Effective Tutorial Ontology Modeling on Organic Rice Farming for Non-Science & Technology Educated Farmers Using Knowledge Engineering

ERIC Educational Resources Information Center

Yanchinda, Jirawit; Chakpitak, Nopasit; Yodmongkol, Pitipong

2015-01-01

Knowledge of the appropriate technologies for sustainable development projects has encouraged grass roots development, which has in turn promoted sustainable and successful community development, which a requirement is to share and reuse this knowledge effectively. This research aims to propose a tutorial ontology effectiveness modeling on organic…

The Ontological Perspectives of the Semantic Web and the Metadata Harvesting Protocol: Applications of Metadata for Improving Web Search.

ERIC Educational Resources Information Center

Fast, Karl V.; Campbell, D. Grant

2001-01-01

Compares the implied ontological frameworks of the Open Archives Initiative Protocol for Metadata Harvesting and the World Wide Web Consortium's Semantic Web. Discusses current search engine technology, semantic markup, indexing principles of special libraries and online databases, and componentization and the distinction between data and…

Formal ontology for natural language processing and the integration of biomedical databases.

PubMed

Simon, Jonathan; Dos Santos, Mariana; Fielding, James; Smith, Barry

2006-01-01

The central hypothesis underlying this communication is that the methodology and conceptual rigor of a philosophically inspired formal ontology can bring significant benefits in the development and maintenance of application ontologies [A. Flett, M. Dos Santos, W. Ceusters, Some Ontology Engineering Procedures and their Supporting Technologies, EKAW2002, 2003]. This hypothesis has been tested in the collaboration between Language and Computing (L&C), a company specializing in software for supporting natural language processing especially in the medical field, and the Institute for Formal Ontology and Medical Information Science (IFOMIS), an academic research institution concerned with the theoretical foundations of ontology. In the course of this collaboration L&C's ontology, LinKBase, which is designed to integrate and support reasoning across a plurality of external databases, has been subjected to a thorough auditing on the basis of the principles underlying IFOMIS's Basic Formal Ontology (BFO) [B. Smith, Basic Formal Ontology, 2002. http://ontology.buffalo.edu/bfo]. The goal is to transform a large terminology-based ontology into one with the ability to support reasoning applications. Our general procedure has been the implementation of a meta-ontological definition space in which the definitions of all the concepts and relations in LinKBase are standardized in the framework of first-order logic. In this paper we describe how this principles-based standardization has led to a greater degree of internal coherence of the LinKBase structure, and how it has facilitated the construction of mappings between external databases using LinKBase as translation hub. We argue that the collaboration here described represents a new phase in the quest to solve the so-called "Tower of Babel" problem of ontology integration [F. Montayne, J. Flanagan, Formal Ontology: The Foundation for Natural Language Processing, 2003. http://www.landcglobal.com/].

Utilizing a structural meta-ontology for family-based quality assurance of the BioPortal ontologies.

PubMed

Ochs, Christopher; He, Zhe; Zheng, Ling; Geller, James; Perl, Yehoshua; Hripcsak, George; Musen, Mark A

2016-06-01

An Abstraction Network is a compact summary of an ontology's structure and content. In previous research, we showed that Abstraction Networks support quality assurance (QA) of biomedical ontologies. The development of an Abstraction Network and its associated QA methodologies, however, is a labor-intensive process that previously was applicable only to one ontology at a time. To improve the efficiency of the Abstraction-Network-based QA methodology, we introduced a QA framework that uses uniform Abstraction Network derivation techniques and QA methodologies that are applicable to whole families of structurally similar ontologies. For the family-based framework to be successful, it is necessary to develop a method for classifying ontologies into structurally similar families. We now describe a structural meta-ontology that classifies ontologies according to certain structural features that are commonly used in the modeling of ontologies (e.g., object properties) and that are important for Abstraction Network derivation. Each class of the structural meta-ontology represents a family of ontologies with identical structural features, indicating which types of Abstraction Networks and QA methodologies are potentially applicable to all of the ontologies in the family. We derive a collection of 81 families, corresponding to classes of the structural meta-ontology, that enable a flexible, streamlined family-based QA methodology, offering multiple choices for classifying an ontology. The structure of 373 ontologies from the NCBO BioPortal is analyzed and each ontology is classified into multiple families modeled by the structural meta-ontology. Copyright © 2016 Elsevier Inc. All rights reserved.

Ontology-Driven Provenance Management in eScience: An Application in Parasite Research

NASA Astrophysics Data System (ADS)

Sahoo, Satya S.; Weatherly, D. Brent; Mutharaju, Raghava; Anantharam, Pramod; Sheth, Amit; Tarleton, Rick L.

Provenance, from the French word "provenir", describes the lineage or history of a data entity. Provenance is critical information in scientific applications to verify experiment process, validate data quality and associate trust values with scientific results. Current industrial scale eScience projects require an end-to-end provenance management infrastructure. This infrastructure needs to be underpinned by formal semantics to enable analysis of large scale provenance information by software applications. Further, effective analysis of provenance information requires well-defined query mechanisms to support complex queries over large datasets. This paper introduces an ontology-driven provenance management infrastructure for biology experiment data, as part of the Semantic Problem Solving Environment (SPSE) for Trypanosoma cruzi (T.cruzi). This provenance infrastructure, called T.cruzi Provenance Management System (PMS), is underpinned by (a) a domain-specific provenance ontology called Parasite Experiment ontology, (b) specialized query operators for provenance analysis, and (c) a provenance query engine. The query engine uses a novel optimization technique based on materialized views called materialized provenance views (MPV) to scale with increasing data size and query complexity. This comprehensive ontology-driven provenance infrastructure not only allows effective tracking and management of ongoing experiments in the Tarleton Research Group at the Center for Tropical and Emerging Global Diseases (CTEGD), but also enables researchers to retrieve the complete provenance information of scientific results for publication in literature.

NanoParticle Ontology for Cancer Nanotechnology Research

PubMed Central

Thomas, Dennis G.; Pappu, Rohit V.; Baker, Nathan A.

2010-01-01

Data generated from cancer nanotechnology research are so diverse and large in volume that it is difficult to share and efficiently use them without informatics tools. In particular, ontologies that provide a unifying knowledge framework for annotating the data are required to facilitate the semantic integration, knowledge-based searching, unambiguous interpretation, mining and inferencing of the data using informatics methods. In this paper, we discuss the design and development of NanoParticle Ontology (NPO), which is developed within the framework of the Basic Formal Ontology (BFO), and implemented in the Ontology Web Language (OWL) using well-defined ontology design principles. The NPO was developed to represent knowledge underlying the preparation, chemical composition, and characterization of nanomaterials involved in cancer research. Public releases of the NPO are available through BioPortal website, maintained by the National Center for Biomedical Ontology. Mechanisms for editorial and governance processes are being developed for the maintenance, review, and growth of the NPO. PMID:20211274

A medical ontology for intelligent web-based skin lesions image retrieval.

PubMed

Maragoudakis, Manolis; Maglogiannis, Ilias

2011-06-01

Researchers have applied increasing efforts towards providing formal computational frameworks to consolidate the plethora of concepts and relations used in the medical domain. In the domain of skin related diseases, the variability of semantic features contained within digital skin images is a major barrier to the medical understanding of the symptoms and development of early skin cancers. The desideratum of making these standards machine-readable has led to their formalization in ontologies. In this work, in an attempt to enhance an existing Core Ontology for skin lesion images, hand-coded from image features, high quality images were analyzed by an autonomous ontology creation engine. We show that by exploiting agglomerative clustering methods with distance criteria upon the existing ontological structure, the original domain model could be enhanced with new instances, attributes and even relations, thus allowing for better classification and retrieval of skin lesion categories from the web.

Ontology of fractures

NASA Astrophysics Data System (ADS)

Zhong, Jian; Aydina, Atilla; McGuinness, Deborah L.

2009-03-01

Fractures are fundamental structures in the Earth's crust and they can impact many societal and industrial activities including oil and gas exploration and production, aquifer management, CO 2 sequestration, waste isolation, the stabilization of engineering structures, and assessing natural hazards (earthquakes, volcanoes, and landslides). Therefore, an ontology which organizes the concepts of fractures could help facilitate a sound education within, and communication among, the highly diverse professional and academic community interested in the problems cited above. We developed a process-based ontology that makes explicit specifications about fractures, their properties, and the deformation mechanisms which lead to their formation and evolution. Our ontology emphasizes the relationships among concepts such as the factors that influence the mechanism(s) responsible for the formation and evolution of specific fracture types. Our ontology is a valuable resource with a potential to applications in a number of fields utilizing recent advances in Information Technology, specifically for digital data and information in computers, grids, and Web services.

A PRACTICAL ONTOLOGY FOR THE LARGE-SCALE MODELING OF SCHOLARLY ARTIFACTS AND THEIR USAGE

DOE Office of Scientific and Technical Information (OSTI.GOV)

RODRIGUEZ, MARKO A.; BOLLEN, JOHAN; VAN DE SOMPEL, HERBERT

2007-01-30

The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scholarly ontology that was engineered to represent those classes for which large-scale bibliographic and usage data exists, supports usage research, and whose instantiation is scalable to the order of 50 million articles along with their associated artifacts (e.g. authors and journals) and an accompanying 1 billion usage events. The real worldmore » instantiation of the presented abstract ontology is a semantic network model of the scholarly community which lends the scholarly process to statistical analysis and computational support. They present the ontology, discuss its instantiation, and provide some example inference rules for calculating various scholarly artifact metrics.« less

An ontology of scientific experiments

PubMed Central

Soldatova, Larisa N; King, Ross D

2006-01-01

The formal description of experiments for efficient analysis, annotation and sharing of results is a fundamental part of the practice of science. Ontologies are required to achieve this objective. A few subject-specific ontologies of experiments currently exist. However, despite the unity of scientific experimentation, no general ontology of experiments exists. We propose the ontology EXPO to meet this need. EXPO links the SUMO (the Suggested Upper Merged Ontology) with subject-specific ontologies of experiments by formalizing the generic concepts of experimental design, methodology and results representation. EXPO is expressed in the W3C standard ontology language OWL-DL. We demonstrate the utility of EXPO and its ability to describe different experimental domains, by applying it to two experiments: one in high-energy physics and the other in phylogenetics. The use of EXPO made the goals and structure of these experiments more explicit, revealed ambiguities, and highlighted an unexpected similarity. We conclude that, EXPO is of general value in describing experiments and a step towards the formalization of science. PMID:17015305

COHeRE: Cross-Ontology Hierarchical Relation Examination for Ontology Quality Assurance.

PubMed

Cui, Licong

Biomedical ontologies play a vital role in healthcare information management, data integration, and decision support. Ontology quality assurance (OQA) is an indispensable part of the ontology engineering cycle. Most existing OQA methods are based on the knowledge provided within the targeted ontology. This paper proposes a novel cross-ontology analysis method, Cross-Ontology Hierarchical Relation Examination (COHeRE), to detect inconsistencies and possible errors in hierarchical relations across multiple ontologies. COHeRE leverages the Unified Medical Language System (UMLS) knowledge source and the MapReduce cloud computing technique for systematic, large-scale ontology quality assurance work. COHeRE consists of three main steps with the UMLS concepts and relations as the input. First, the relations claimed in source vocabularies are filtered and aggregated for each pair of concepts. Second, inconsistent relations are detected if a concept pair is related by different types of relations in different source vocabularies. Finally, the uncovered inconsistent relations are voted according to their number of occurrences across different source vocabularies. The voting result together with the inconsistent relations serve as the output of COHeRE for possible ontological change. The highest votes provide initial suggestion on how such inconsistencies might be fixed. In UMLS, 138,987 concept pairs were found to have inconsistent relationships across multiple source vocabularies. 40 inconsistent concept pairs involving hierarchical relationships were randomly selected and manually reviewed by a human expert. 95.8% of the inconsistent relations involved in these concept pairs indeed exist in their source vocabularies rather than being introduced by mistake in the UMLS integration process. 73.7% of the concept pairs with suggested relationship were agreed by the human expert. The effectiveness of COHeRE indicates that UMLS provides a promising environment to enhance qualities of biomedical ontologies by performing cross-ontology examination.

GalenOWL: Ontology-based drug recommendations discovery

PubMed Central

2012-01-01

Background Identification of drug-drug and drug-diseases interactions can pose a difficult problem to cope with, as the increasingly large number of available drugs coupled with the ongoing research activities in the pharmaceutical domain, make the task of discovering relevant information difficult. Although international standards, such as the ICD-10 classification and the UNII registration, have been developed in order to enable efficient knowledge sharing, medical staff needs to be constantly updated in order to effectively discover drug interactions before prescription. The use of Semantic Web technologies has been proposed in earlier works, in order to tackle this problem. Results This work presents a semantic-enabled online service, named GalenOWL, capable of offering real time drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standards such as the aforementioned ICD-10 and UNII, provide the backbone of the common representation of medical data, while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. A comparison of the developed ontology-based system with a similar system developed using a traditional business logic rule engine is performed, giving insights on the advantages and drawbacks of both implementations. Conclusions The use of Semantic Web technologies has been found to be a good match for developing drug recommendation systems. Ontologies can effectively encapsulate medical knowledge and rule-based reasoning can capture and encode the drug interactions knowledge. PMID:23256945

IDEF5 Ontology Description Capture Method: Concept Paper

NASA Technical Reports Server (NTRS)

Menzel, Christopher P.; Mayer, Richard J.

1990-01-01

The results of research towards an ontology capture method referred to as IDEF5 are presented. Viewed simply as the study of what exists in a domain, ontology is an activity that can be understood to be at work across the full range of human inquiry prompted by the persistent effort to understand the world in which it has found itself - and which it has helped to shape. In the contest of information management, ontology is the task of extracting the structure of a given engineering, manufacturing, business, or logistical domain and storing it in an usable representational medium. A key to effective integration is a system ontology that can be accessed and modified across domains and which captures common features of the overall system relevant to the goals of the disparate domains. If the focus is on information integration, then the strongest motivation for ontology comes from the need to support data sharing and function interoperability. In the correct architecture, an enterprise ontology base would allow th e construction of an integrated environment in which legacy systems appear to be open architecture integrated resources. If the focus is on system/software development, then support for the rapid acquisition of reliable systems is perhaps the strongest motivation for ontology. Finally, ontological analysis was demonstrated to be an effective first step in the construction of robust knowledge based systems.

Knowledge Management Framework for Emerging Infectious Diseases Preparedness and Response: Design and Development of Public Health Document Ontology

PubMed Central

Zhang, Zhizun; Gonzalez, Mila C; Morse, Stephen S

2017-01-01

Background There are increasing concerns about our preparedness and timely coordinated response across the globe to cope with emerging infectious diseases (EIDs). This poses practical challenges that require exploiting novel knowledge management approaches effectively. Objective This work aims to develop an ontology-driven knowledge management framework that addresses the existing challenges in sharing and reusing public health knowledge. Methods We propose a systems engineering-inspired ontology-driven knowledge management approach. It decomposes public health knowledge into concepts and relations and organizes the elements of knowledge based on the teleological functions. Both knowledge and semantic rules are stored in an ontology and retrieved to answer queries regarding EID preparedness and response. Results A hybrid concept extraction was implemented in this work. The quality of the ontology was evaluated using the formal evaluation method Ontology Quality Evaluation Framework. Conclusions Our approach is a potentially effective methodology for managing public health knowledge. Accuracy and comprehensiveness of the ontology can be improved as more knowledge is stored. In the future, a survey will be conducted to collect queries from public health practitioners. The reasoning capacity of the ontology will be evaluated using the queries and hypothetical outbreaks. We suggest the importance of developing a knowledge sharing standard like the Gene Ontology for the public health domain. PMID:29021130

TermGenie – a web-application for pattern-based ontology class generation

DOE PAGES

Dietze, Heiko; Berardini, Tanya Z.; Foulger, Rebecca E.; ...

2014-01-01

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 newmore » classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. Lastly, TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.« less

TermGenie – a web-application for pattern-based ontology class generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dietze, Heiko; Berardini, Tanya Z.; Foulger, Rebecca E.

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 newmore » classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. Lastly, TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.« less

TermGenie - a web-application for pattern-based ontology class generation.

PubMed

Dietze, Heiko; Berardini, Tanya Z; Foulger, Rebecca E; Hill, David P; Lomax, Jane; Osumi-Sutherland, David; Roncaglia, Paola; Mungall, Christopher J

2014-01-01

Biological ontologies are continually growing and improving from requests for new classes (terms) by biocurators. These ontology requests can frequently create bottlenecks in the biocuration process, as ontology developers struggle to keep up, while manually processing these requests and create classes. TermGenie allows biocurators to generate new classes based on formally specified design patterns or templates. The system is web-based and can be accessed by any authorized curator through a web browser. Automated rules and reasoning engines are used to ensure validity, uniqueness and relationship to pre-existing classes. In the last 4 years the Gene Ontology TermGenie generated 4715 new classes, about 51.4% of all new classes created. The immediate generation of permanent identifiers proved not to be an issue with only 70 (1.4%) obsoleted classes. TermGenie is a web-based class-generation system that complements traditional ontology development tools. All classes added through pre-defined templates are guaranteed to have OWL equivalence axioms that are used for automatic classification and in some cases inter-ontology linkage. At the same time, the system is simple and intuitive and can be used by most biocurators without extensive training.

Semantic similarity between ontologies at different scales

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Qingpeng; Haglin, David J.

In the past decade, existing and new knowledge and datasets has been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea viamore » studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three Gene Ontology slims (Plant, Yeast, and Candida, among which the latter two belong to the same kingdom—Fungi) using four popular measures commonly applied to biomedical ontologies (Resnik, Lin, Jiang-Conrath, and SimRel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performance of Jiang-Conrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by (a) consistently showing that Yeast and Candida are more similar (as compared to Plant) at different scales, and (b) small deviations of the similarity values after excluding a majority of nodes from several lower scales. This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.« less

Adaptive Semantic and Social Web-based learning and assessment environment for the STEM

NASA Astrophysics Data System (ADS)

Babaie, Hassan; Atchison, Chris; Sunderraman, Rajshekhar

2014-05-01

We are building a cloud- and Semantic Web-based personalized, adaptive learning environment for the STEM fields that integrates and leverages Social Web technologies to allow instructors and authors of learning material to collaborate in semi-automatic development and update of their common domain and task ontologies and building their learning resources. The semi-automatic ontology learning and development minimize issues related to the design and maintenance of domain ontologies by knowledge engineers who do not have any knowledge of the domain. The social web component of the personal adaptive system will allow individual and group learners to interact with each other and discuss their own learning experience and understanding of course material, and resolve issues related to their class assignments. The adaptive system will be capable of representing key knowledge concepts in different ways and difficulty levels based on learners' differences, and lead to different understanding of the same STEM content by different learners. It will adapt specific pedagogical strategies to individual learners based on their characteristics, cognition, and preferences, allow authors to assemble remotely accessed learning material into courses, and provide facilities for instructors to assess (in real time) the perception of students of course material, monitor their progress in the learning process, and generate timely feedback based on their understanding or misconceptions. The system applies a set of ontologies that structure the learning process, with multiple user friendly Web interfaces. These include the learning ontology (models learning objects, educational resources, and learning goal); context ontology (supports adaptive strategy by detecting student situation), domain ontology (structures concepts and context), learner ontology (models student profile, preferences, and behavior), task ontologies, technological ontology (defines devices and places that surround the student), pedagogy ontology, and learner ontology (defines time constraint, comment, profile).

Expert2OWL: A Methodology for Pattern-Based Ontology Development.

PubMed

Tahar, Kais; Xu, Jie; Herre, Heinrich

2017-01-01

The formalization of expert knowledge enables a broad spectrum of applications employing ontologies as underlying technology. These include eLearning, Semantic Web and expert systems. However, the manual construction of such ontologies is time-consuming and thus expensive. Moreover, experts are often unfamiliar with the syntax and semantics of formal ontology languages such as OWL and usually have no experience in developing formal ontologies. To overcome these barriers, we developed a new method and tool, called Expert2OWL that provides efficient features to support the construction of OWL ontologies using GFO (General Formal Ontology) as a top-level ontology. This method allows a close and effective collaboration between ontologists and domain experts. Essentially, this tool integrates Excel spreadsheets as part of a pattern-based ontology development and refinement process. Expert2OWL enables us to expedite the development process and modularize the resulting ontologies. We applied this method in the field of Chinese Herbal Medicine (CHM) and used Expert2OWL to automatically generate an accurate Chinese Herbology ontology (CHO). The expressivity of CHO was tested and evaluated using ontology query languages SPARQL and DL. CHO shows promising results and can generate answers to important scientific questions such as which Chinese herbal formulas contain which substances, which substances treat which diseases, and which ones are the most frequently used in CHM.

Ontology engineering for management of data in the transportation domain.

DOT National Transportation Integrated Search

2008-11-01

This report discusses work done as a collaboration between the Kansas Department of Transportation, the University of Kansas Civil Engineering Department, and the Dakota State University School of Business and Information Systems. The work was an exa...

Library and Information Science's Ontological Position in the Networked Society: Using New Technology to Get Back to an Old Practice

ERIC Educational Resources Information Center

Kåhre, Peter

2013-01-01

Introduction: This paper concerns the ontological position of library and informations science in the networked society. The aim of the study is to understand library use and library functions in the age of Internet and artificial intelligent programmed search engines. Theoretical approach: The approach discusses so called sociocognitive tools in…

An Ontology for Insider Threat Indicators Development and Applications

DTIC Science & Technology

2014-11-01

An Ontology for Insider Threat Indicators Development and Applications Daniel L. Costa , Matthew L. Collins, Samuel J. Perl, Michael J. Albrethsen...services, commit fraud against an organization, steal intellectual property, or conduct national security espionage, sabotaging systems and data, as...engineering plans from the victim organization’s computer systems to his new employer.  The insider accessed a web server with an administrator account

Ontologies, Knowledge Bases and Knowledge Management

DTIC Science & Technology

2002-07-01

AFRL-IF-RS-TR-2002-163 Final Technical Report July 2002 ONTOLOGIES, KNOWLEDGE BASES AND KNOWLEDGE MANAGEMENT USC Information ...and layer additional information necessary to make specific uses of the knowledge in this core. Finally, while we were able to find adequate solutions... knowledge base and inference engine. Figure 3.2: SDA Editor Interface 46 Although the SDA has access to information about the situation, we wanted the user

Formal ontologies in biomedical knowledge representation.

PubMed

Schulz, S; Jansen, L

2013-01-01

Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.

Gene Ontology Consortium: going forward

PubMed Central

2015-01-01

The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology. PMID:25428369

Representing Human Expertise by the OWL Web Ontology Language to Support Knowledge Engineering in Decision Support Systems.

PubMed

Ramzan, Asia; Wang, Hai; Buckingham, Christopher

2014-01-01

Clinical decision support systems (CDSSs) often base their knowledge and advice on human expertise. Knowledge representation needs to be in a format that can be easily understood by human users as well as supporting ongoing knowledge engineering, including evolution and consistency of knowledge. This paper reports on the development of an ontology specification for managing knowledge engineering in a CDSS for assessing and managing risks associated with mental-health problems. The Galatean Risk and Safety Tool, GRiST, represents mental-health expertise in the form of a psychological model of classification. The hierarchical structure was directly represented in the machine using an XML document. Functionality of the model and knowledge management were controlled using attributes in the XML nodes, with an accompanying paper manual for specifying how end-user tools should behave when interfacing with the XML. This paper explains the advantages of using the web-ontology language, OWL, as the specification, details some of the issues and problems encountered in translating the psychological model to OWL, and shows how OWL benefits knowledge engineering. The conclusions are that OWL can have an important role in managing complex knowledge domains for systems based on human expertise without impeding the end-users' understanding of the knowledge base. The generic classification model underpinning GRiST makes it applicable to many decision domains and the accompanying OWL specification facilitates its implementation.

Engineering Genders: A Spatial Analysis of Engineering, Gender, and Learning

ERIC Educational Resources Information Center

Weidler-Lewis, Joanna R.

2016-01-01

This three article dissertation is an investigation into the ontology of learning insofar as learning is a process of becoming. In each article I explore the general questions of who is learning, in what ways, and with what consequences. The context for this research is undergraduate engineering education with particular attention to the…

Knowledge acquisition and learning process description in context of e-learning

NASA Astrophysics Data System (ADS)

Kiselev, B. G.; Yakutenko, V. A.; Yuriev, M. A.

2017-01-01

This paper investigates the problem of design of e-learning and MOOC systems. It describes instructional design-based approaches to e-learning systems design: IMS Learning Design, MISA and TELOS. To solve this problem we present Knowledge Field of Educational Environment with Competence boundary conditions - instructional engineering method for self-learning systems design. It is based on the simplified TELOS approach and enables a user to create their individual learning path by choosing prerequisite and target competencies. The paper provides the ontology model for the described instructional engineering method, real life use cases and the classification of the presented model. Ontology model consists of 13 classes and 15 properties. Some of them are inherited from Knowledge Field of Educational Environment and some are new and describe competence boundary conditions and knowledge validation objects. Ontology model uses logical constraints and is described using OWL 2 standard. To give TELOS users better understanding of our approach we list mapping between TELOS and KFEEC.

A methodology for extending domain coverage in SemRep.

PubMed

Rosemblat, Graciela; Shin, Dongwook; Kilicoglu, Halil; Sneiderman, Charles; Rindflesch, Thomas C

2013-12-01

We describe a domain-independent methodology to extend SemRep coverage beyond the biomedical domain. SemRep, a natural language processing application originally designed for biomedical texts, uses the knowledge sources provided by the Unified Medical Language System (UMLS©). Ontological and terminological extensions to the system are needed in order to support other areas of knowledge. We extended SemRep's application by developing a semantic representation of a previously unsupported domain. This was achieved by adapting well-known ontology engineering phases and integrating them with the UMLS knowledge sources on which SemRep crucially depends. While the process to extend SemRep coverage has been successfully applied in earlier projects, this paper presents in detail the step-wise approach we followed and the mechanisms implemented. A case study in the field of medical informatics illustrates how the ontology engineering phases have been adapted for optimal integration with the UMLS. We provide qualitative and quantitative results, which indicate the validity and usefulness of our methodology. Published by Elsevier Inc.

Web information retrieval based on ontology

NASA Astrophysics Data System (ADS)

Zhang, Jian

2013-03-01

The purpose of the Information Retrieval (IR) is to find a set of documents that are relevant for a specific information need of a user. Traditional Information Retrieval model commonly used in commercial search engine is based on keyword indexing system and Boolean logic queries. One big drawback of traditional information retrieval is that they typically retrieve information without an explicitly defined domain of interest to the users so that a lot of no relevance information returns to users, which burden the user to pick up useful answer from these no relevance results. In order to tackle this issue, many semantic web information retrieval models have been proposed recently. The main advantage of Semantic Web is to enhance search mechanisms with the use of Ontology's mechanisms. In this paper, we present our approach to personalize web search engine based on ontology. In addition, key techniques are also discussed in our paper. Compared to previous research, our works concentrate on the semantic similarity and the whole process including query submission and information annotation.

Ontology Alignment Repair through Modularization and Confidence-Based Heuristics

PubMed Central

Santos, Emanuel; Faria, Daniel; Pesquita, Catia; Couto, Francisco M.

2015-01-01

Ontology Matching aims at identifying a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, alignments produced for large ontologies are often logically incoherent. It was only recently that the use of repair techniques to improve the coherence of ontology alignments began to be explored. This paper presents a novel modularization technique for ontology alignment repair which extracts fragments of the input ontologies that only contain the necessary classes and relations to resolve all detectable incoherences. The paper presents also an alignment repair algorithm that uses a global repair strategy to minimize both the degree of incoherence and the number of mappings removed from the alignment, while overcoming the scalability problem by employing the proposed modularization technique. Our evaluation shows that our modularization technique produces significantly small fragments of the ontologies and that our repair algorithm produces more complete alignments than other current alignment repair systems, while obtaining an equivalent degree of incoherence. Additionally, we also present a variant of our repair algorithm that makes use of the confidence values of the mappings to improve alignment repair. Our repair algorithm was implemented as part of AgreementMakerLight, a free and open-source ontology matching system. PMID:26710335

Ontology Alignment Repair through Modularization and Confidence-Based Heuristics.

PubMed

Santos, Emanuel; Faria, Daniel; Pesquita, Catia; Couto, Francisco M

2015-01-01

Ontology Matching aims at identifying a set of semantic correspondences, called an alignment, between related ontologies. In recent years, there has been a growing interest in efficient and effective matching methods for large ontologies. However, alignments produced for large ontologies are often logically incoherent. It was only recently that the use of repair techniques to improve the coherence of ontology alignments began to be explored. This paper presents a novel modularization technique for ontology alignment repair which extracts fragments of the input ontologies that only contain the necessary classes and relations to resolve all detectable incoherences. The paper presents also an alignment repair algorithm that uses a global repair strategy to minimize both the degree of incoherence and the number of mappings removed from the alignment, while overcoming the scalability problem by employing the proposed modularization technique. Our evaluation shows that our modularization technique produces significantly small fragments of the ontologies and that our repair algorithm produces more complete alignments than other current alignment repair systems, while obtaining an equivalent degree of incoherence. Additionally, we also present a variant of our repair algorithm that makes use of the confidence values of the mappings to improve alignment repair. Our repair algorithm was implemented as part of AgreementMakerLight, a free and open-source ontology matching system.

ER2OWL: Generating OWL Ontology from ER Diagram

NASA Astrophysics Data System (ADS)

Fahad, Muhammad

Ontology is the fundamental part of Semantic Web. The goal of W3C is to bring the web into (its full potential) a semantic web with reusing previous systems and artifacts. Most legacy systems have been documented in structural analysis and structured design (SASD), especially in simple or Extended ER Diagram (ERD). Such systems need up-gradation to become the part of semantic web. In this paper, we present ERD to OWL-DL ontology transformation rules at concrete level. These rules facilitate an easy and understandable transformation from ERD to OWL. The set of rules for transformation is tested on a structured analysis and design example. The framework provides OWL ontology for semantic web fundamental. This framework helps software engineers in upgrading the structured analysis and design artifact ERD, to components of semantic web. Moreover our transformation tool, ER2OWL, reduces the cost and time for building OWL ontologies with the reuse of existing entity relationship models.

Knowledge Management Framework for Emerging Infectious Diseases Preparedness and Response: Design and Development of Public Health Document Ontology.

PubMed

Zhang, Zhizun; Gonzalez, Mila C; Morse, Stephen S; Venkatasubramanian, Venkat

2017-10-11

There are increasing concerns about our preparedness and timely coordinated response across the globe to cope with emerging infectious diseases (EIDs). This poses practical challenges that require exploiting novel knowledge management approaches effectively. This work aims to develop an ontology-driven knowledge management framework that addresses the existing challenges in sharing and reusing public health knowledge. We propose a systems engineering-inspired ontology-driven knowledge management approach. It decomposes public health knowledge into concepts and relations and organizes the elements of knowledge based on the teleological functions. Both knowledge and semantic rules are stored in an ontology and retrieved to answer queries regarding EID preparedness and response. A hybrid concept extraction was implemented in this work. The quality of the ontology was evaluated using the formal evaluation method Ontology Quality Evaluation Framework. Our approach is a potentially effective methodology for managing public health knowledge. Accuracy and comprehensiveness of the ontology can be improved as more knowledge is stored. In the future, a survey will be conducted to collect queries from public health practitioners. The reasoning capacity of the ontology will be evaluated using the queries and hypothetical outbreaks. We suggest the importance of developing a knowledge sharing standard like the Gene Ontology for the public health domain. ©Zhizun Zhang, Mila C Gonzalez, Stephen S Morse, Venkat Venkatasubramanian. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 11.10.2017.

Ontology development for provenance tracing in National Climate Assessment of the US Global Change Research Program

NASA Astrophysics Data System (ADS)

Fu, Linyun; Ma, Xiaogang; Zheng, Jin; Goldstein, Justin; Duggan, Brian; West, Patrick; Aulenbach, Steve; Tilmes, Curt; Fox, Peter

2014-05-01

This poster will show how we used a case-driven iterative methodology to develop an ontology to represent the content structure and the associated provenance information in a National Climate Assessment (NCA) report of the US Global Change Research Program (USGCRP). We applied the W3C PROV-O ontology to implement a formal representation of provenance. We argue that the use case-driven, iterative development process and the application of a formal provenance ontology help efficiently incorporate domain knowledge from earth and environmental scientists in a well-structured model interoperable in the context of the Web of Data.

Seamless Integration of Desktop and Mobile Learning Experience through an Ontology-Based Adaptation Engine: Report of a Pilot-Project

ERIC Educational Resources Information Center

Mercurio, Marco; Torre, Ilaria; Torsani, Simone

2014-01-01

The paper describes a module within the distance language learning environment of the Language Centre at the Genoa University which adapts, through an ontology, learning activities to the device in use. Adaptation means not simply resizing a page but also the ability to transform the nature of a task so that it fits the device with the smallest…

An infrastructure for ontology-based information systems in biomedicine: RICORDO case study.

PubMed

Wimalaratne, Sarala M; Grenon, Pierre; Hoehndorf, Robert; Gkoutos, Georgios V; de Bono, Bernard

2012-02-01

The article presents an infrastructure for supporting the semantic interoperability of biomedical resources based on the management (storing and inference-based querying) of their ontology-based annotations. This infrastructure consists of: (i) a repository to store and query ontology-based annotations; (ii) a knowledge base server with an inference engine to support the storage of and reasoning over ontologies used in the annotation of resources; (iii) a set of applications and services allowing interaction with the integrated repository and knowledge base. The infrastructure is being prototyped and developed and evaluated by the RICORDO project in support of the knowledge management of biomedical resources, including physiology and pharmacology models and associated clinical data. The RICORDO toolkit and its source code are freely available from http://ricordo.eu/relevant-resources. sarala@ebi.ac.uk.

Semantic biomedical resource discovery: a Natural Language Processing framework.

PubMed

Sfakianaki, Pepi; Koumakis, Lefteris; Sfakianakis, Stelios; Iatraki, Galatia; Zacharioudakis, Giorgos; Graf, Norbert; Marias, Kostas; Tsiknakis, Manolis

2015-09-30

A plethora of publicly available biomedical resources do currently exist and are constantly increasing at a fast rate. In parallel, specialized repositories are been developed, indexing numerous clinical and biomedical tools. The main drawback of such repositories is the difficulty in locating appropriate resources for a clinical or biomedical decision task, especially for non-Information Technology expert users. In parallel, although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications has been slow and lags behind progress in the general NLP domain. The aim of the present study is to investigate the use of semantics for biomedical resources annotation with domain specific ontologies and exploit Natural Language Processing methods in empowering the non-Information Technology expert users to efficiently search for biomedical resources using natural language. A Natural Language Processing engine which can "translate" free text into targeted queries, automatically transforming a clinical research question into a request description that contains only terms of ontologies, has been implemented. The implementation is based on information extraction techniques for text in natural language, guided by integrated ontologies. Furthermore, knowledge from robust text mining methods has been incorporated to map descriptions into suitable domain ontologies in order to ensure that the biomedical resources descriptions are domain oriented and enhance the accuracy of services discovery. The framework is freely available as a web application at ( http://calchas.ics.forth.gr/ ). For our experiments, a range of clinical questions were established based on descriptions of clinical trials from the ClinicalTrials.gov registry as well as recommendations from clinicians. Domain experts manually identified the available tools in a tools repository which are suitable for addressing the clinical questions at hand, either individually or as a set of tools forming a computational pipeline. The results were compared with those obtained from an automated discovery of candidate biomedical tools. For the evaluation of the results, precision and recall measurements were used. Our results indicate that the proposed framework has a high precision and low recall, implying that the system returns essentially more relevant results than irrelevant. There are adequate biomedical ontologies already available, sufficiency of existing NLP tools and quality of biomedical annotation systems for the implementation of a biomedical resources discovery framework, based on the semantic annotation of resources and the use on NLP techniques. The results of the present study demonstrate the clinical utility of the application of the proposed framework which aims to bridge the gap between clinical question in natural language and efficient dynamic biomedical resources discovery.

Ontology Reuse in Geoscience Semantic Applications

NASA Astrophysics Data System (ADS)

Mayernik, M. S.; Gross, M. B.; Daniels, M. D.; Rowan, L. R.; Stott, D.; Maull, K. E.; Khan, H.; Corson-Rikert, J.

2015-12-01

The tension between local ontology development and wider ontology connections is fundamental to the Semantic web. It is often unclear, however, what the key decision points should be for new semantic web applications in deciding when to reuse existing ontologies and when to develop original ontologies. In addition, with the growth of semantic web ontologies and applications, new semantic web applications can struggle to efficiently and effectively identify and select ontologies to reuse. This presentation will describe the ontology comparison, selection, and consolidation effort within the EarthCollab project. UCAR, Cornell University, and UNAVCO are collaborating on the EarthCollab project to use semantic web technologies to enable the discovery of the research output from a diverse array of projects. The EarthCollab project is using the VIVO Semantic web software suite to increase discoverability of research information and data related to the following two geoscience-based communities: (1) the Bering Sea Project, an interdisciplinary field program whose data archive is hosted by NCAR's Earth Observing Laboratory (EOL), and (2) diverse research projects informed by geodesy through the UNAVCO geodetic facility and consortium. This presentation will outline of EarthCollab use cases, and provide an overview of key ontologies being used, including the VIVO-Integrated Semantic Framework (VIVO-ISF), Global Change Information System (GCIS), and Data Catalog (DCAT) ontologies. We will discuss issues related to bringing these ontologies together to provide a robust ontological structure to support the EarthCollab use cases. It is rare that a single pre-existing ontology meets all of a new application's needs. New projects need to stitch ontologies together in ways that fit into the broader semantic web ecosystem.

Gene Ontology Consortium: going forward.

PubMed

2015-01-01

The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

GoWeb: a semantic search engine for the life science web.

PubMed

Dietze, Heiko; Schroeder, Michael

2009-10-01

Current search engines are keyword-based. Semantic technologies promise a next generation of semantic search engines, which will be able to answer questions. Current approaches either apply natural language processing to unstructured text or they assume the existence of structured statements over which they can reason. Here, we introduce a third approach, GoWeb, which combines classical keyword-based Web search with text-mining and ontologies to navigate large results sets and facilitate question answering. We evaluate GoWeb on three benchmarks of questions on genes and functions, on symptoms and diseases, and on proteins and diseases. The first benchmark is based on the BioCreAtivE 1 Task 2 and links 457 gene names with 1352 functions. GoWeb finds 58% of the functional GeneOntology annotations. The second benchmark is based on 26 case reports and links symptoms with diseases. GoWeb achieves 77% success rate improving an existing approach by nearly 20%. The third benchmark is based on 28 questions in the TREC genomics challenge and links proteins to diseases. GoWeb achieves a success rate of 79%. GoWeb's combination of classical Web search with text-mining and ontologies is a first step towards answering questions in the biomedical domain. GoWeb is online at: http://www.gopubmed.org/goweb.

Reasoning Based Quality Assurance of Medical Ontologies: A Case Study

PubMed Central

Horridge, Matthew; Parsia, Bijan; Noy, Natalya F.; Musenm, Mark A.

2014-01-01

The World Health Organisation is using OWL as a key technology to develop ICD-11 – the next version of the well-known International Classification of Diseases. Besides providing better opportunities for data integration and linkages to other well-known ontologies such as SNOMED-CT, one of the main promises of using OWL is that it will enable various forms of automated error checking. In this paper we investigate how automated OWL reasoning, along with a Justification Finding Service can be used as a Quality Assurance technique for the development of large and complex ontologies such as ICD-11. Using the International Classification of Traditional Medicine (ICTM) – Chapter 24 of ICD-11 – as a case study, and an expert panel of knowledge engineers, we reveal the kinds of problems that can occur, how they can be detected, and how they can be fixed. Specifically, we found that a logically inconsistent version of the ICTM ontology could be repaired using justifications (minimal entailing subsets of an ontology). Although over 600 justifications for the inconsistency were initially computed, we found that there were three main manageable patterns or categories of justifications involving TBox and ABox axioms. These categories represented meaningful domain errors to an expert panel of ICTM project knowledge engineers, who were able to use them to successfully determine the axioms that needed to be revised in order to fix the problem. All members of the expert panel agreed that the approach was useful for debugging and ensuring the quality of ICTM. PMID:25954373

Knowledge modeling of coal mining equipments based on ontology

NASA Astrophysics Data System (ADS)

Zhang, Baolong; Wang, Xiangqian; Li, Huizong; Jiang, Miaomiao

2017-06-01

The problems of information redundancy and sharing are universe in coal mining equipment management. In order to improve the using efficiency of knowledge of coal mining equipments, this paper proposed a new method of knowledge modeling based on ontology. On the basis of analyzing the structures and internal relations of coal mining equipment knowledge, taking OWL as ontology construct language, the ontology model of coal mining equipment knowledge is built with the help of Protégé 4.3 software tools. The knowledge description method will lay the foundation for the high effective knowledge management and sharing, which is very significant for improving the production management level of coal mining enterprises.

Mapping between the OBO and OWL ontology languages.

PubMed

Tirmizi, Syed Hamid; Aitken, Stuart; Moreira, Dilvan A; Mungall, Chris; Sequeda, Juan; Shah, Nigam H; Miranker, Daniel P

2011-03-07

Ontologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL. We have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source. Our transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner.

Mapping between the OBO and OWL ontology languages

PubMed Central

2011-01-01

Background Ontologies are commonly used in biomedicine to organize concepts to describe domains such as anatomies, environments, experiment, taxonomies etc. NCBO BioPortal currently hosts about 180 different biomedical ontologies. These ontologies have been mainly expressed in either the Open Biomedical Ontology (OBO) format or the Web Ontology Language (OWL). OBO emerged from the Gene Ontology, and supports most of the biomedical ontology content. In comparison, OWL is a Semantic Web language, and is supported by the World Wide Web consortium together with integral query languages, rule languages and distributed infrastructure for information interchange. These features are highly desirable for the OBO content as well. A convenient method for leveraging these features for OBO ontologies is by transforming OBO ontologies to OWL. Results We have developed a methodology for translating OBO ontologies to OWL using the organization of the Semantic Web itself to guide the work. The approach reveals that the constructs of OBO can be grouped together to form a similar layer cake. Thus we were able to decompose the problem into two parts. Most OBO constructs have easy and obvious equivalence to a construct in OWL. A small subset of OBO constructs requires deeper consideration. We have defined transformations for all constructs in an effort to foster a standard common mapping between OBO and OWL. Our mapping produces OWL-DL, a Description Logics based subset of OWL with desirable computational properties for efficiency and correctness. Our Java implementation of the mapping is part of the official Gene Ontology project source. Conclusions Our transformation system provides a lossless roundtrip mapping for OBO ontologies, i.e. an OBO ontology may be translated to OWL and back without loss of knowledge. In addition, it provides a roadmap for bridging the gap between the two ontology languages in order to enable the use of ontology content in a language independent manner. PMID:21388572

Is synthetic biology mechanical biology?

PubMed

Holm, Sune

2015-12-01

A widespread and influential characterization of synthetic biology emphasizes that synthetic biology is the application of engineering principles to living systems. Furthermore, there is a strong tendency to express the engineering approach to organisms in terms of what seems to be an ontological claim: organisms are machines. In the paper I investigate the ontological and heuristic significance of the machine analogy in synthetic biology. I argue that the use of the machine analogy and the aim of producing rationally designed organisms does not necessarily imply a commitment to mechanical biology. The ideal of applying engineering principles to biology is best understood as expressing recognition of the machine-unlikeness of natural organisms and the limits of human cognition. The paper suggests an interpretation of the identification of organisms with machines in synthetic biology according to which it expresses a strategy for representing, understanding, and constructing living systems that are more machine-like than natural organisms.

On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.

PubMed

Kashyap, Vipul; Morales, Alfredo; Hongsermeier, Tonya

2006-01-01

We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.

Panacea, a semantic-enabled drug recommendations discovery framework.

PubMed

Doulaverakis, Charalampos; Nikolaidis, George; Kleontas, Athanasios; Kompatsiaris, Ioannis

2014-03-06

Personalized drug prescription can be benefited from the use of intelligent information management and sharing. International standard classifications and terminologies have been developed in order to provide unique and unambiguous information representation. Such standards can be used as the basis of automated decision support systems for providing drug-drug and drug-disease interaction discovery. Additionally, Semantic Web technologies have been proposed in earlier works, in order to support such systems. The paper presents Panacea, a semantic framework capable of offering drug-drug and drug-diseases interaction discovery. For enabling this kind of service, medical information and terminology had to be translated to ontological terms and be appropriately coupled with medical knowledge of the field. International standard classifications and terminologies, provide the backbone of the common representation of medical data while the medical knowledge of drug interactions is represented by a rule base which makes use of the aforementioned standards. Representation is based on a lightweight ontology. A layered reasoning approach is implemented where at the first layer ontological inference is used in order to discover underlying knowledge, while at the second layer a two-step rule selection strategy is followed resulting in a computationally efficient reasoning approach. Details of the system architecture are presented while also giving an outline of the difficulties that had to be overcome. Panacea is evaluated both in terms of quality of recommendations against real clinical data and performance. The quality recommendation gave useful insights regarding requirements for real world deployment and revealed several parameters that affected the recommendation results. Performance-wise, Panacea is compared to a previous published work by the authors, a service for drug recommendations named GalenOWL, and presents their differences in modeling and approach to the problem, while also pinpointing the advantages of Panacea. Overall, the paper presents a framework for providing an efficient drug recommendations service where Semantic Web technologies are coupled with traditional business rule engines.

A Lexical-Ontological Resource for Consumer Healthcare

NASA Astrophysics Data System (ADS)

Cardillo, Elena; Serafini, Luciano; Tamilin, Andrei

In Consumer Healthcare Informatics it is still difficult for laypeople to find, understand and act on health information, due to the persistent communication gap between specialized medical terminology and that used by healthcare consumers. Furthermore, existing clinically-oriented terminologies cannot provide sufficient support when integrated into consumer-oriented applications, so there is a need to create consumer-friendly terminologies reflecting the different ways healthcare consumers express and think about health topics. Following this direction, this work suggests a way to support the design of an ontology-based system that mitigates this gap, using knowledge engineering and semantic web technologies. The system is based on the development of a consumer-oriented medical terminology that will be integrated with other medical domain ontologies and terminologies into a medical ontology repository. This will support consumer-oriented healthcare systems, such as Personal Health Records, by providing many knowledge services to help users in accessing and managing their healthcare data.

A Lexical-Ontological Resource for Consumer Heathcare

NASA Astrophysics Data System (ADS)

Cardillo, Elena

In Consumer Healthcare Informatics it is still difficult for laypersons to understand and act on health information, due to the persistent communication gap between specialized medical terminology and that used by healthcare consumers. Furthermore, existing clinically-oriented terminologies cannot provide sufficient support when integrated into consumer-oriented applications, so there is a need to create consumer-friendly terminologies reflecting the different ways healthcare consumers express and think about health topics. Following this direction, this work suggests a way to support the design of an ontology-based system that mitigates this gap, using knowledge engineering and Semantic Web technologies. The system is based on the development of a consumer-oriented medical terminology which will be integrated with other existing domain ontologies/terminologies into a medical ontology repository. This will support consumer-oriented healthcare systems by providing many knowledge services to help users in accessing and managing their healthcare data.

Modeling patient safety incidents knowledge with the Categorial Structure method.

PubMed

Souvignet, Julien; Bousquet, Cédric; Lewalle, Pierre; Trombert-Paviot, Béatrice; Rodrigues, Jean Marie

2011-01-01

Following the WHO initiative named World Alliance for Patient Safety (PS) launched in 2004 a conceptual framework developed by PS national reporting experts has summarized the knowledge available. As a second step, the Department of Public Health of the University of Saint Etienne team elaborated a Categorial Structure (a semi formal structure not related to an upper level ontology) identifying the elements of the semantic structure underpinning the broad concepts contained in the framework for patient safety. This knowledge engineering method has been developed to enable modeling patient safety information as a prerequisite for subsequent full ontology development. The present article describes the semantic dissection of the concepts, the elicitation of the ontology requirements and the domain constraints of the conceptual framework. This ontology includes 134 concepts and 25 distinct relations and will serve as basis for an Information Model for Patient Safety.

SNOMED CT module-driven clinical archetype management.

PubMed

Allones, J L; Taboada, M; Martinez, D; Lozano, R; Sobrido, M J

2013-06-01

To explore semantic search to improve management and user navigation in clinical archetype repositories. In order to support semantic searches across archetypes, an automated method based on SNOMED CT modularization is implemented to transform clinical archetypes into SNOMED CT extracts. Concurrently, query terms are converted into SNOMED CT concepts using the search engine Lucene. Retrieval is then carried out by matching query concepts with the corresponding SNOMED CT segments. A test collection of the 16 clinical archetypes, including over 250 terms, and a subset of 55 clinical terms from two medical dictionaries, MediLexicon and MedlinePlus, were used to test our method. The keyword-based service supported by the OpenEHR repository offered us a benchmark to evaluate the enhancement of performance. In total, our approach reached 97.4% precision and 69.1% recall, providing a substantial improvement of recall (more than 70%) compared to the benchmark. Exploiting medical domain knowledge from ontologies such as SNOMED CT may overcome some limitations of the keyword-based systems and thus improve the search experience of repository users. An automated approach based on ontology segmentation is an efficient and feasible way for supporting modeling, management and user navigation in clinical archetype repositories. Copyright © 2013 Elsevier Inc. All rights reserved.

Complex overlapping concepts: An effective auditing methodology for families of similarly structured BioPortal ontologies.

PubMed

Zheng, Ling; Chen, Yan; Elhanan, Gai; Perl, Yehoshua; Geller, James; Ochs, Christopher

2018-05-28

In previous research, we have demonstrated for a number of ontologies that structurally complex concepts (for different definitions of "complex") in an ontology are more likely to exhibit errors than other concepts. Thus, such complex concepts often become fertile ground for quality assurance (QA) in ontologies. They should be audited first. One example of complex concepts is given by "overlapping concepts" (to be defined below.) Historically, a different auditing methodology had to be developed for every single ontology. For better scalability and efficiency, it is desirable to identify family-wide QA methodologies. Each such methodology would be applicable to a whole family of similar ontologies. In past research, we had divided the 685 ontologies of BioPortal into families of structurally similar ontologies. We showed for four ontologies of the same large family in BioPortal that "overlapping concepts" are indeed statistically significantly more likely to exhibit errors. In order to make an authoritative statement concerning the success of "overlapping concepts" as a methodology for a whole family of similar ontologies (or of large subhierarchies of ontologies), it is necessary to show that "overlapping concepts" have a higher likelihood of errors for six out of six ontologies of the family. In this paper, we are demonstrating for two more ontologies that "overlapping concepts" can successfully predict groups of concepts with a higher error rate than concepts from a control group. The fifth ontology is the Neoplasm subhierarchy of the National Cancer Institute thesaurus (NCIt). The sixth ontology is the Infectious Disease subhierarchy of SNOMED CT. We demonstrate quality assurance results for both of them. Furthermore, in this paper we observe two novel, important, and useful phenomena during quality assurance of "overlapping concepts." First, an erroneous "overlapping concept" can help with discovering other erroneous "non-overlapping concepts" in its vicinity. Secondly, correcting erroneous "overlapping concepts" may turn them into "non-overlapping concepts." We demonstrate that this may reduce the complexity of parts of the ontology, which in turn makes the ontology more comprehensible, simplifying maintenance and use of the ontology. Copyright © 2018. Published by Elsevier Inc.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

Semantic Data Integration and Ontology Use within the Global Earth Observation System of Systems (GEOSS) Global Water Cycle Data Integration System

NASA Astrophysics Data System (ADS)

Pozzi, W.; Fekete, B.; Piasecki, M.; McGuinness, D.; Fox, P.; Lawford, R.; Vorosmarty, C.; Houser, P.; Imam, B.

2008-12-01

The inadequacies of water cycle observations for monitoring long-term changes in the global water system, as well as their feedback into the climate system, poses a major constraint on sustainable development of water resources and improvement of water management practices. Hence, The Group on Earth Observations (GEO) has established Task WA-08-01, "Integration of in situ and satellite data for water cycle monitoring," an integrative initiative combining different types of satellite and in situ observations related to key variables of the water cycle with model outputs for improved accuracy and global coverage. This presentation proposes development of the Rapid, Integrated Monitoring System for the Water Cycle (Global-RIMS)--already employed by the GEO Global Terrestrial Network for Hydrology (GTN-H)--as either one of the main components or linked with the Asian system to constitute the modeling system of GEOSS for water cycle monitoring. We further propose expanded, augmented capability to run multiple grids to embrace some of the heterogeneous methods and formats of the Earth Science, Hydrology, and Hydraulic Engineering communities. Different methodologies are employed by the Earth Science (land surface modeling), the Hydrological (GIS), and the Hydraulic Engineering Communities; with each community employing models that require different input data. Data will be routed as input variables to the models through web services, allowing satellite and in situ data to be integrated together within the modeling framework. Semantic data integration will provide the automation to enable this system to operate in near-real-time. Multiple data collections for ground water, precipitation, soil moisture satellite data, such as SMAP, and lake data will require multiple low level ontologies, and an upper level ontology will permit user-friendly water management knowledge to be synthesized. These ontologies will have to have overlapping terms mapped and linked together. so that they can cover an even wider net of data sources. The goal is to develop the means to link together the upper level and lower level ontologies and to have these registered within the GEOSS Registry. Actual operational ontologies that would link to models or link to data collections containing input variables required by models would have to be nested underneath this top level ontology, analogous to the mapping that has been carried out among ontologies within GEON.

IDOMAL: an ontology for malaria.

PubMed

Topalis, Pantelis; Mitraka, Elvira; Bujila, Ioana; Deligianni, Elena; Dialynas, Emmanuel; Siden-Kiamos, Inga; Troye-Blomberg, Marita; Louis, Christos

2010-08-10

Ontologies are rapidly becoming a necessity for the design of efficient information technology tools, especially databases, because they permit the organization of stored data using logical rules and defined terms that are understood by both humans and machines. This has as consequence both an enhanced usage and interoperability of databases and related resources. It is hoped that IDOMAL, the ontology of malaria will prove a valuable instrument when implemented in both malaria research and control measures. The OBOEdit2 software was used for the construction of the ontology. IDOMAL is based on the Basic Formal Ontology (BFO) and follows the rules set by the OBO Foundry consortium. The first version of the malaria ontology covers both clinical and epidemiological aspects of the disease, as well as disease and vector biology. IDOMAL is meant to later become the nucleation site for a much larger ontology of vector borne diseases, which will itself be an extension of a large ontology of infectious diseases (IDO). The latter is currently being developed in the frame of a large international collaborative effort. IDOMAL, already freely available in its first version, will form part of a suite of ontologies that will be used to drive IT tools and databases specifically constructed to help control malaria and, later, other vector-borne diseases. This suite already consists of the ontology described here as well as the one on insecticide resistance that has been available for some time. Additional components are being developed and introduced into IDOMAL.

Microfluidic screening and whole-genome sequencing identifies mutations associated with improved protein secretion by yeast.

PubMed

Huang, Mingtao; Bai, Yunpeng; Sjostrom, Staffan L; Hallström, Björn M; Liu, Zihe; Petranovic, Dina; Uhlén, Mathias; Joensson, Haakan N; Andersson-Svahn, Helene; Nielsen, Jens

2015-08-25

There is an increasing demand for biotech-based production of recombinant proteins for use as pharmaceuticals in the food and feed industry and in industrial applications. Yeast Saccharomyces cerevisiae is among preferred cell factories for recombinant protein production, and there is increasing interest in improving its protein secretion capacity. Due to the complexity of the secretory machinery in eukaryotic cells, it is difficult to apply rational engineering for construction of improved strains. Here we used high-throughput microfluidics for the screening of yeast libraries, generated by UV mutagenesis. Several screening and sorting rounds resulted in the selection of eight yeast clones with significantly improved secretion of recombinant α-amylase. Efficient secretion was genetically stable in the selected clones. We performed whole-genome sequencing of the eight clones and identified 330 mutations in total. Gene ontology analysis of mutated genes revealed many biological processes, including some that have not been identified before in the context of protein secretion. Mutated genes identified in this study can be potentially used for reverse metabolic engineering, with the objective to construct efficient cell factories for protein secretion. The combined use of microfluidics screening and whole-genome sequencing to map the mutations associated with the improved phenotype can easily be adapted for other products and cell types to identify novel engineering targets, and this approach could broadly facilitate design of novel cell factories.

The next generation of similarity measures that fully explore the semantics in biomedical ontologies.

PubMed

Couto, Francisco M; Pinto, H Sofia

2013-10-01

There is a prominent trend to augment and improve the formality of biomedical ontologies. For example, this is shown by the current effort on adding description logic axioms, such as disjointness. One of the key ontology applications that can take advantage of this effort is the conceptual (functional) similarity measurement. The presence of description logic axioms in biomedical ontologies make the current structural or extensional approaches weaker and further away from providing sound semantics-based similarity measures. Although beneficial in small ontologies, the exploration of description logic axioms by semantics-based similarity measures is computational expensive. This limitation is critical for biomedical ontologies that normally contain thousands of concepts. Thus in the process of gaining their rightful place, biomedical functional similarity measures have to take the journey of finding how this rich and powerful knowledge can be fully explored while keeping feasible computational costs. This manuscript aims at promoting and guiding the development of compelling tools that deliver what the biomedical community will require in a near future: a next-generation of biomedical similarity measures that efficiently and fully explore the semantics present in biomedical ontologies.

OpenDMAP: An open source, ontology-driven concept analysis engine, with applications to capturing knowledge regarding protein transport, protein interactions and cell-type-specific gene expression

PubMed Central

Hunter, Lawrence; Lu, Zhiyong; Firby, James; Baumgartner, William A; Johnson, Helen L; Ogren, Philip V; Cohen, K Bretonnel

2008-01-01

Background Information extraction (IE) efforts are widely acknowledged to be important in harnessing the rapid advance of biomedical knowledge, particularly in areas where important factual information is published in a diverse literature. Here we report on the design, implementation and several evaluations of OpenDMAP, an ontology-driven, integrated concept analysis system. It significantly advances the state of the art in information extraction by leveraging knowledge in ontological resources, integrating diverse text processing applications, and using an expanded pattern language that allows the mixing of syntactic and semantic elements and variable ordering. Results OpenDMAP information extraction systems were produced for extracting protein transport assertions (transport), protein-protein interaction assertions (interaction) and assertions that a gene is expressed in a cell type (expression). Evaluations were performed on each system, resulting in F-scores ranging from .26 – .72 (precision .39 – .85, recall .16 – .85). Additionally, each of these systems was run over all abstracts in MEDLINE, producing a total of 72,460 transport instances, 265,795 interaction instances and 176,153 expression instances. Conclusion OpenDMAP advances the performance standards for extracting protein-protein interaction predications from the full texts of biomedical research articles. Furthermore, this level of performance appears to generalize to other information extraction tasks, including extracting information about predicates of more than two arguments. The output of the information extraction system is always constructed from elements of an ontology, ensuring that the knowledge representation is grounded with respect to a carefully constructed model of reality. The results of these efforts can be used to increase the efficiency of manual curation efforts and to provide additional features in systems that integrate multiple sources for information extraction. The open source OpenDMAP code library is freely available at PMID:18237434

Versioning System for Distributed Ontology Development

DTIC Science & Technology

2016-02-02

Semantic   Web   community. For example, the distributed and isolated development requirement may apply to non‐cyber  range communities of public ontology... semantic   web .” However, we observe that the  maintenance of an ontology and its reuse is not a high priority for the majority of the publicly available... Semantic )  Web . AAAI Spring Symposium: Symbiotic Relationships between  Semantic   Web  and  Knowledge Engineering. 2008.  [LHK09] Matthias Loskyll

A Knowledge-Modeling Approach to Integrate Multiple Clinical Practice Guidelines to Provide Evidence-Based Clinical Decision Support for Managing Comorbid Conditions.

PubMed

Abidi, Samina

2017-10-26

Clinical management of comorbidities is a challenge, especially in a clinical decision support setting, as it requires the safe and efficient reconciliation of multiple disease-specific clinical procedures to formulate a comorbid therapeutic plan that is both effective and safe for the patient. In this paper we pursue the integration of multiple disease-specific Clinical Practice Guidelines (CPG) in order to manage co-morbidities within a computerized Clinical Decision Support System (CDSS). We present a CPG integration framework-termed as COMET (Comorbidity Ontological Modeling & ExecuTion) that manifests a knowledge management approach to model, computerize and integrate multiple CPG to yield a comorbid CPG knowledge model that upon execution can provide evidence-based recommendations for handling comorbid patients. COMET exploits semantic web technologies to achieve (a) CPG knowledge synthesis to translate a paper-based CPG to disease-specific clinical pathways (CP) that include specialized co-morbidity management procedures based on input from domain experts; (b) CPG knowledge modeling to computerize the disease-specific CP using a Comorbidity CPG ontology; (c) CPG knowledge integration by aligning multiple ontologically-modeled CP to develop a unified comorbid CPG knowledge model; and (e) CPG knowledge execution using reasoning engines to derive CPG-mediated recommendations for managing patients with comorbidities. We present a web-accessible COMET CDSS that provides family physicians with CPG-mediated comorbidity decision support to manage Atrial Fibrillation and Chronic Heart Failure. We present our qualitative and quantitative analysis of the knowledge content and usability of COMET CDSS.

A knowledge representation view on biomedical structure and function.

PubMed Central

Schulz, Stefan; Hahn, Udo

2002-01-01

In biomedical ontologies, structural and functional considerations are of outstanding importance, and concepts which belong to these two categories are highly interdependent. At the representational level both axes must be clearly kept separate in order to support disciplined ontology engineering. Furthermore, the biaxial organization of physical structure (both by a taxonomic and partonomic order) entails intricate patterns of inference. We here propose a layered encoding of taxonomic, partonomic and functional aspects of biomedical concepts using description logics. PMID:12463912

Engineering Knowledge for Assistive Living

NASA Astrophysics Data System (ADS)

Chen, Liming; Nugent, Chris

This paper introduces a knowledge based approach to assistive living in smart homes. It proposes a system architecture that makes use of knowledge in the lifecycle of assistive living. The paper describes ontology based knowledge engineering practices and discusses mechanisms for exploiting knowledge for activity recognition and assistance. It presents system implementation and experiments, and discusses initial results.

Qualitative models for space system engineering

NASA Technical Reports Server (NTRS)

Forbus, Kenneth D.

1990-01-01

The objectives of this project were: (1) to investigate the implications of qualitative modeling techniques for problems arising in the monitoring, diagnosis, and design of Space Station subsystems and procedures; (2) to identify the issues involved in using qualitative models to enhance and automate engineering functions. These issues include representing operational criteria, fault models, alternate ontologies, and modeling continuous signals at a functional level of description; and (3) to develop a prototype collection of qualitative models for fluid and thermal systems commonly found in Space Station subsystems. Potential applications of qualitative modeling to space-systems engineering, including the notion of intelligent computer-aided engineering are summarized. Emphasis is given to determining which systems of the proposed Space Station provide the most leverage for study, given the current state of the art. Progress on using qualitative models, including development of the molecular collection ontology for reasoning about fluids, the interaction of qualitative and quantitative knowledge in analyzing thermodynamic cycles, and an experiment on building a natural language interface to qualitative reasoning is reported. Finally, some recommendations are made for future research.

NCBO Resource Index: Ontology-Based Search and Mining of Biomedical Resources

PubMed Central

Jonquet, Clement; LePendu, Paea; Falconer, Sean; Coulet, Adrien; Noy, Natalya F.; Musen, Mark A.; Shah, Nigam H.

2011-01-01

The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology (NCBO), we have developed the Resource Index—a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies, in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics “under the hood.” PMID:21918645

NCBO Resource Index: Ontology-Based Search and Mining of Biomedical Resources.

PubMed

Jonquet, Clement; Lependu, Paea; Falconer, Sean; Coulet, Adrien; Noy, Natalya F; Musen, Mark A; Shah, Nigam H

2011-09-01

The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center for Biomedical Ontology (NCBO), we have developed the Resource Index-a growing, large-scale ontology-based index of more than twenty heterogeneous biomedical resources. The resources come from a variety of repositories maintained by organizations from around the world. We use a set of over 200 publicly available ontologies contributed by researchers in various domains to annotate the elements in these resources. We use the semantics that the ontologies encode, such as different properties of classes, the class hierarchies, and the mappings between ontologies, in order to improve the search experience for the Resource Index user. Our user interface enables scientists to search the multiple resources quickly and efficiently using domain terms, without even being aware that there is semantics "under the hood."

Common IED exploitation target set ontology

NASA Astrophysics Data System (ADS)

Russomanno, David J.; Qualls, Joseph; Wowczuk, Zenovy; Franken, Paul; Robinson, William

2010-04-01

The Common IED Exploitation Target Set (CIEDETS) ontology provides a comprehensive semantic data model for capturing knowledge about sensors, platforms, missions, environments, and other aspects of systems under test. The ontology also includes representative IEDs; modeled as explosives, camouflage, concealment objects, and other background objects, which comprise an overall threat scene. The ontology is represented using the Web Ontology Language and the SPARQL Protocol and RDF Query Language, which ensures portability of the acquired knowledge base across applications. The resulting knowledge base is a component of the CIEDETS application, which is intended to support the end user sensor test and evaluation community. CIEDETS associates a system under test to a subset of cataloged threats based on the probability that the system will detect the threat. The associations between systems under test, threats, and the detection probabilities are established based on a hybrid reasoning strategy, which applies a combination of heuristics and simplified modeling techniques. Besides supporting the CIEDETS application, which is focused on efficient and consistent system testing, the ontology can be leveraged in a myriad of other applications, including serving as a knowledge source for mission planning tools.

OntoMate: a text-mining tool aiding curation at the Rat Genome Database

PubMed Central

Liu, Weisong; Laulederkind, Stanley J. F.; Hayman, G. Thomas; Wang, Shur-Jen; Nigam, Rajni; Smith, Jennifer R.; De Pons, Jeff; Dwinell, Melinda R.; Shimoyama, Mary

2015-01-01

The Rat Genome Database (RGD) is the premier repository of rat genomic, genetic and physiologic data. Converting data from free text in the scientific literature to a structured format is one of the main tasks of all model organism databases. RGD spends considerable effort manually curating gene, Quantitative Trait Locus (QTL) and strain information. The rapidly growing volume of biomedical literature and the active research in the biological natural language processing (bioNLP) community have given RGD the impetus to adopt text-mining tools to improve curation efficiency. Recently, RGD has initiated a project to use OntoMate, an ontology-driven, concept-based literature search engine developed at RGD, as a replacement for the PubMed (http://www.ncbi.nlm.nih.gov/pubmed) search engine in the gene curation workflow. OntoMate tags abstracts with gene names, gene mutations, organism name and most of the 16 ontologies/vocabularies used at RGD. All terms/ entities tagged to an abstract are listed with the abstract in the search results. All listed terms are linked both to data entry boxes and a term browser in the curation tool. OntoMate also provides user-activated filters for species, date and other parameters relevant to the literature search. Using the system for literature search and import has streamlined the process compared to using PubMed. The system was built with a scalable and open architecture, including features specifically designed to accelerate the RGD gene curation process. With the use of bioNLP tools, RGD has added more automation to its curation workflow. Database URL: http://rgd.mcw.edu PMID:25619558

Approaching the axiomatic enrichment of the Gene Ontology from a lexical perspective.

PubMed

Quesada-Martínez, Manuel; Mikroyannidi, Eleni; Fernández-Breis, Jesualdo Tomás; Stevens, Robert

2015-09-01

The main goal of this work is to measure how lexical regularities in biomedical ontology labels can be used for the automatic creation of formal relationships between classes, and to evaluate the results of applying our approach to the Gene Ontology (GO). In recent years, we have developed a method for the lexical analysis of regularities in biomedical ontology labels, and we showed that the labels can present a high degree of regularity. In this work, we extend our method with a cross-products extension (CPE) metric, which estimates the potential interest of a specific regularity for axiomatic enrichment in the lexical analysis, using information on exact matches in external ontologies. The GO consortium recently enriched the GO by using so-called cross-product extensions. Cross-products are generated by establishing axioms that relate a given GO class with classes from the GO or other biomedical ontologies. We apply our method to the GO and study how its lexical analysis can identify and reconstruct the cross-products that are defined by the GO consortium. The label of the classes of the GO are highly regular in lexical terms, and the exact matches with labels of external ontologies affect 80% of the GO classes. The CPE metric reveals that 31.48% of the classes that exhibit regularities have fragments that are classes into two external ontologies that are selected for our experiment, namely, the Cell Ontology and the Chemical Entities of Biological Interest ontology, and 18.90% of them are fully decomposable into smaller parts. Our results show that the CPE metric permits our method to detect GO cross-product extensions with a mean recall of 62% and a mean precision of 28%. The study is completed with an analysis of false positives to explain this precision value. We think that our results support the claim that our lexical approach can contribute to the axiomatic enrichment of biomedical ontologies and that it can provide new insights into the engineering of biomedical ontologies. Copyright © 2014 Elsevier B.V. All rights reserved.

Ontological Model of Business Process Management Systems

NASA Astrophysics Data System (ADS)

Manoilov, G.; Deliiska, B.

2008-10-01

The activities which constitute business process management (BPM) can be grouped into five categories: design, modeling, execution, monitoring and optimization. Dedicated software packets for business process management system (BPMS) are available on the market. But the efficiency of its exploitation depends on used ontological model in the development time and run time of the system. In the article an ontological model of BPMS in area of software industry is investigated. The model building is preceded by conceptualization of the domain and taxonomy of BPMS development. On the base of the taxonomy an simple online thesaurus is created.

Predicting activities of daily living for cancer patients using an ontology-guided machine learning methodology.

PubMed

Min, Hua; Mobahi, Hedyeh; Irvin, Katherine; Avramovic, Sanja; Wojtusiak, Janusz

2017-09-16

Bio-ontologies are becoming increasingly important in knowledge representation and in the machine learning (ML) fields. This paper presents a ML approach that incorporates bio-ontologies and its application to the SEER-MHOS dataset to discover patterns of patient characteristics that impact the ability to perform activities of daily living (ADLs). Bio-ontologies are used to provide computable knowledge for ML methods to "understand" biomedical data. This retrospective study included 723 cancer patients from the SEER-MHOS dataset. Two ML methods were applied to create predictive models for ADL disabilities for the first year after a patient's cancer diagnosis. The first method is a standard rule learning algorithm; the second is that same algorithm additionally equipped with methods for reasoning with ontologies. The models showed that a patient's race, ethnicity, smoking preference, treatment plan and tumor characteristics including histology, staging, cancer site, and morphology were predictors for ADL performance levels one year after cancer diagnosis. The ontology-guided ML method was more accurate at predicting ADL performance levels (P < 0.1) than methods without ontologies. This study demonstrated that bio-ontologies can be harnessed to provide medical knowledge for ML algorithms. The presented method demonstrates that encoding specific types of hierarchical relationships to guide rule learning is possible, and can be extended to other types of semantic relationships present in biomedical ontologies. The ontology-guided ML method achieved better performance than the method without ontologies. The presented method can also be used to promote the effectiveness and efficiency of ML in healthcare, in which use of background knowledge and consistency with existing clinical expertise is critical.

Towards natural language question generation for the validation of ontologies and mappings.

PubMed

Ben Abacha, Asma; Dos Reis, Julio Cesar; Mrabet, Yassine; Pruski, Cédric; Da Silveira, Marcos

2016-08-08

The increasing number of open-access ontologies and their key role in several applications such as decision-support systems highlight the importance of their validation. Human expertise is crucial for the validation of ontologies from a domain point-of-view. However, the growing number of ontologies and their fast evolution over time make manual validation challenging. We propose a novel semi-automatic approach based on the generation of natural language (NL) questions to support the validation of ontologies and their evolution. The proposed approach includes the automatic generation, factorization and ordering of NL questions from medical ontologies. The final validation and correction is performed by submitting these questions to domain experts and automatically analyzing their feedback. We also propose a second approach for the validation of mappings impacted by ontology changes. The method exploits the context of the changes to propose correction alternatives presented as Multiple Choice Questions. This research provides a question optimization strategy to maximize the validation of ontology entities with a reduced number of questions. We evaluate our approach for the validation of three medical ontologies. We also evaluate the feasibility and efficiency of our mappings validation approach in the context of ontology evolution. These experiments are performed with different versions of SNOMED-CT and ICD9. The obtained experimental results suggest the feasibility and adequacy of our approach to support the validation of interconnected and evolving ontologies. Results also suggest that taking into account RDFS and OWL entailment helps reducing the number of questions and validation time. The application of our approach to validate mapping evolution also shows the difficulty of adapting mapping evolution over time and highlights the importance of semi-automatic validation.

Text Mining to inform construction of Earth and Environmental Science Ontologies

NASA Astrophysics Data System (ADS)

Schildhauer, M.; Adams, B.; Rebich Hespanha, S.

2013-12-01

There is a clear need for better semantic representation of Earth and environmental concepts, to facilitate more effective discovery and re-use of information resources relevant to scientists doing integrative research. In order to develop general-purpose Earth and environmental science ontologies, however, it is necessary to represent concepts and relationships that span usage across multiple disciplines and scientific specialties. Traditional knowledge modeling through ontologies utilizes expert knowledge but inevitably favors the particular perspectives of the ontology engineers, as well as the domain experts who interacted with them. This often leads to ontologies that lack robust coverage of synonymy, while also missing important relationships among concepts that can be extremely useful for working scientists to be aware of. In this presentation we will discuss methods we have developed that utilize statistical topic modeling on a large corpus of Earth and environmental science articles, to expand coverage and disclose relationships among concepts in the Earth sciences. For our work we collected a corpus of over 121,000 abstracts from many of the top Earth and environmental science journals. We performed latent Dirichlet allocation topic modeling on this corpus to discover a set of latent topics, which consist of terms that commonly co-occur in abstracts. We match terms in the topics to concept labels in existing ontologies to reveal gaps, and we examine which terms are commonly associated in natural language discourse, to identify relationships that are important to formally model in ontologies. Our text mining methodology uncovers significant gaps in the content of some popular existing ontologies, and we show how, through a workflow involving human interpretation of topic models, we can bootstrap ontologies to have much better coverage and richer semantics. Because we base our methods directly on what working scientists are communicating about their research, it gives us an alternative bottom-up approach to populating and enriching ontologies, that complements more traditional knowledge modeling endeavors.

Development of a One-Stop Data Search and Discovery Engine using Ontologies for Semantic Mappings (HydroSeek)

NASA Astrophysics Data System (ADS)

Piasecki, M.; Beran, B.

2007-12-01

Search engines have changed the way we see the Internet. The ability to find the information by just typing in keywords was a big contribution to the overall web experience. While the conventional search engine methodology worked well for textual documents, locating scientific data remains a problem since they are stored in databases not readily accessible by search engine bots. Considering different temporal, spatial and thematic coverage of different databases, especially for interdisciplinary research it is typically necessary to work with multiple data sources. These sources can be federal agencies which generally offer national coverage or regional sources which cover a smaller area with higher detail. However for a given geographic area of interest there often exists more than one database with relevant data. Thus being able to query multiple databases simultaneously is a desirable feature that would be tremendously useful for scientists. Development of such a search engine requires dealing with various heterogeneity issues. In scientific databases, systems often impose controlled vocabularies which ensure that they are generally homogeneous within themselves but are semantically heterogeneous when moving between different databases. This defines the boundaries of possible semantic related problems making it easier to solve than with the conventional search engines that deal with free text. We have developed a search engine that enables querying multiple data sources simultaneously and returns data in a standardized output despite the aforementioned heterogeneity issues between the underlying systems. This application relies mainly on metadata catalogs or indexing databases, ontologies and webservices with virtual globe and AJAX technologies for the graphical user interface. Users can trigger a search of dozens of different parameters over hundreds of thousands of stations from multiple agencies by providing a keyword, a spatial extent, i.e. a bounding box, and a temporal bracket. As part of this development we have also added an environment that allows users to do some of the semantic tagging, i.e. the linkage of a variable name (which can be anything they desire) to defined concepts in the ontology structure which in turn provides the backbone of the search engine.

Defaults, context, and knowledge: alternatives for OWL-indexed knowledge bases.

PubMed

Rector, A

2004-01-01

The new Web Ontology Language (OWL) and its Description Logic compatible sublanguage (OWL-DL) explicitly exclude defaults and exceptions, as do all logic based formalisms for ontologies. However, many biomedical applications appear to require default reasoning, at least if they are to be engineered in a maintainable way. Default reasoning has always been one of the great strengths of Frame systems such as Protégé. Resolving this conflict requires analysis of the different uses for defaults and exceptions. In some cases, alternatives can be provided within the OWL framework; in others, it appears that hybrid reasoning about a knowledge base of contingent facts built around the core ontology is necessary. Trade-offs include both human factors and the scaling of computational performance. The analysis presented here is based on the OpenGALEN experience with large scale ontologies using a formalism, GRAIL, which explicitly incorporates constructs for hybrid reasoning, numerous experiments with OWL, and initial work on combining OWL and Protégé.

Automated software system for checking the structure and format of ACM SIG documents

NASA Astrophysics Data System (ADS)

Mirza, Arsalan Rahman; Sah, Melike

2017-04-01

Microsoft (MS) Office Word is one of the most commonly used software tools for creating documents. MS Word 2007 and above uses XML to represent the structure of MS Word documents. Metadata about the documents are automatically created using Office Open XML (OOXML) syntax. We develop a new framework, which is called ADFCS (Automated Document Format Checking System) that takes the advantage of the OOXML metadata, in order to extract semantic information from MS Office Word documents. In particular, we develop a new ontology for Association for Computing Machinery (ACM) Special Interested Group (SIG) documents for representing the structure and format of these documents by using OWL (Web Ontology Language). Then, the metadata is extracted automatically in RDF (Resource Description Framework) according to this ontology using the developed software. Finally, we generate extensive rules in order to infer whether the documents are formatted according to ACM SIG standards. This paper, introduces ACM SIG ontology, metadata extraction process, inference engine, ADFCS online user interface, system evaluation and user study evaluations.

Generic-distributed framework for cloud services marketplace based on unified ontology.

PubMed

Hasan, Samer; Valli Kumari, V

2017-11-01

Cloud computing is a pattern for delivering ubiquitous and on demand computing resources based on pay-as-you-use financial model. Typically, cloud providers advertise cloud service descriptions in various formats on the Internet. On the other hand, cloud consumers use available search engines (Google and Yahoo) to explore cloud service descriptions and find the adequate service. Unfortunately, general purpose search engines are not designed to provide a small and complete set of results, which makes the process a big challenge. This paper presents a generic-distrusted framework for cloud services marketplace to automate cloud services discovery and selection process, and remove the barriers between service providers and consumers. Additionally, this work implements two instances of generic framework by adopting two different matching algorithms; namely dominant and recessive attributes algorithm borrowed from gene science and semantic similarity algorithm based on unified cloud service ontology. Finally, this paper presents unified cloud services ontology and models the real-life cloud services according to the proposed ontology. To the best of the authors' knowledge, this is the first attempt to build a cloud services marketplace where cloud providers and cloud consumers can trend cloud services as utilities. In comparison with existing work, semantic approach reduced the execution time by 20% and maintained the same values for all other parameters. On the other hand, dominant and recessive attributes approach reduced the execution time by 57% but showed lower value for recall.

An Ontology-Based GIS for Genomic Data Management of Rumen Microbes

PubMed Central

Jelokhani-Niaraki, Saber; Minuchehr, Zarrin; Nassiri, Mohammad Reza

2015-01-01

During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and Protégé as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data. PMID:25873847

An Ontology-Based GIS for Genomic Data Management of Rumen Microbes.

PubMed

Jelokhani-Niaraki, Saber; Tahmoorespur, Mojtaba; Minuchehr, Zarrin; Nassiri, Mohammad Reza

2015-03-01

During recent years, there has been exponential growth in biological information. With the emergence of large datasets in biology, life scientists are encountering bottlenecks in handling the biological data. This study presents an integrated geographic information system (GIS)-ontology application for handling microbial genome data. The application uses a linear referencing technique as one of the GIS functionalities to represent genes as linear events on the genome layer, where users can define/change the attributes of genes in an event table and interactively see the gene events on a genome layer. Our application adopted ontology to portray and store genomic data in a semantic framework, which facilitates data-sharing among biology domains, applications, and experts. The application was developed in two steps. In the first step, the genome annotated data were prepared and stored in a MySQL database. The second step involved the connection of the database to both ArcGIS and Protégé as the GIS engine and ontology platform, respectively. We have designed this application specifically to manage the genome-annotated data of rumen microbial populations. Such a GIS-ontology application offers powerful capabilities for visualizing, managing, reusing, sharing, and querying genome-related data.

MIRO and IRbase: IT Tools for the Epidemiological Monitoring of Insecticide Resistance in Mosquito Disease Vectors

PubMed Central

Dialynas, Emmanuel; Topalis, Pantelis; Vontas, John; Louis, Christos

2009-01-01

Background Monitoring of insect vector populations with respect to their susceptibility to one or more insecticides is a crucial element of the strategies used for the control of arthropod-borne diseases. This management task can nowadays be achieved more efficiently when assisted by IT (Information Technology) tools, ranging from modern integrated databases to GIS (Geographic Information System). Here we describe an application ontology that we developed de novo, and a specially designed database that, based on this ontology, can be used for the purpose of controlling mosquitoes and, thus, the diseases that they transmit. Methodology/Principal Findings The ontology, named MIRO for Mosquito Insecticide Resistance Ontology, developed using the OBO-Edit software, describes all pertinent aspects of insecticide resistance, including specific methodology and mode of action. MIRO, then, forms the basis for the design and development of a dedicated database, IRbase, constructed using open source software, which can be used to retrieve data on mosquito populations in a temporally and spatially separate way, as well as to map the output using a Google Earth interface. The dependency of the database on the MIRO allows for a rational and efficient hierarchical search possibility. Conclusions/Significance The fact that the MIRO complies with the rules set forward by the OBO (Open Biomedical Ontologies) Foundry introduces cross-referencing with other biomedical ontologies and, thus, both MIRO and IRbase are suitable as parts of future comprehensive surveillance tools and decision support systems that will be used for the control of vector-borne diseases. MIRO is downloadable from and IRbase is accessible at VectorBase, the NIAID-sponsored open access database for arthropod vectors of disease. PMID:19547750

Ontology-based vector space model and fuzzy query expansion to retrieve knowledge on medical computational problem solutions.

PubMed

Bratsas, Charalampos; Koutkias, Vassilis; Kaimakamis, Evangelos; Bamidis, Panagiotis; Maglaveras, Nicos

2007-01-01

Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader-narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thessen, Anne E.; Bunker, Daniel E.; Buttigieg, Pier Luigi

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies aremore » well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.« less

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin

PubMed Central

Hu, Chuli; Li, Jie; Lin, Xin

2018-01-01

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors. PMID:29883425

Emerging semantics to link phenotype and environment

PubMed Central

Bunker, Daniel E.; Buttigieg, Pier Luigi; Cooper, Laurel D.; Dahdul, Wasila M.; Domisch, Sami; Franz, Nico M.; Jaiswal, Pankaj; Lawrence-Dill, Carolyn J.; Midford, Peter E.; Mungall, Christopher J.; Ramírez, Martín J.; Specht, Chelsea D.; Vogt, Lars; Vos, Rutger Aldo; Walls, Ramona L.; White, Jeffrey W.; Zhang, Guanyang; Deans, Andrew R.; Huala, Eva; Lewis, Suzanna E.; Mabee, Paula M.

2015-01-01

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments. PMID:26713234

Emerging semantics to link phenotype and environment.

PubMed

Thessen, Anne E; Bunker, Daniel E; Buttigieg, Pier Luigi; Cooper, Laurel D; Dahdul, Wasila M; Domisch, Sami; Franz, Nico M; Jaiswal, Pankaj; Lawrence-Dill, Carolyn J; Midford, Peter E; Mungall, Christopher J; Ramírez, Martín J; Specht, Chelsea D; Vogt, Lars; Vos, Rutger Aldo; Walls, Ramona L; White, Jeffrey W; Zhang, Guanyang; Deans, Andrew R; Huala, Eva; Lewis, Suzanna E; Mabee, Paula M

2015-01-01

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies are well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. In this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.

Emerging semantics to link phenotype and environment

DOE PAGES

Thessen, Anne E.; Bunker, Daniel E.; Buttigieg, Pier Luigi; ...

2015-12-14

Understanding the interplay between environmental conditions and phenotypes is a fundamental goal of biology. Unfortunately, data that include observations on phenotype and environment are highly heterogeneous and thus difficult to find and integrate. One approach that is likely to improve the status quo involves the use of ontologies to standardize and link data about phenotypes and environments. Specifying and linking data through ontologies will allow researchers to increase the scope and flexibility of large-scale analyses aided by modern computing methods. Investments in this area would advance diverse fields such as ecology, phylogenetics, and conservation biology. While several biological ontologies aremore » well-developed, using them to link phenotypes and environments is rare because of gaps in ontological coverage and limits to interoperability among ontologies and disciplines. Lastly, in this manuscript, we present (1) use cases from diverse disciplines to illustrate questions that could be answered more efficiently using a robust linkage between phenotypes and environments, (2) two proof-of-concept analyses that show the value of linking phenotypes to environments in fishes and amphibians, and (3) two proposed example data models for linking phenotypes and environments using the extensible observation ontology (OBOE) and the Biological Collections Ontology (BCO); these provide a starting point for the development of a data model linking phenotypes and environments.« less

An Observation Capability Semantic-Associated Approach to the Selection of Remote Sensing Satellite Sensors: A Case Study of Flood Observations in the Jinsha River Basin.

PubMed

Hu, Chuli; Li, Jie; Lin, Xin; Chen, Nengcheng; Yang, Chao

2018-05-21

Observation schedules depend upon the accurate understanding of a single sensor’s observation capability and the interrelated observation capability information on multiple sensors. The general ontologies for sensors and observations are abundant. However, few observation capability ontologies for satellite sensors are available, and no study has described the dynamic associations among the observation capabilities of multiple sensors used for integrated observational planning. This limitation results in a failure to realize effective sensor selection. This paper develops a sensor observation capability association (SOCA) ontology model that is resolved around the task-sensor-observation capability (TSOC) ontology pattern. The pattern is developed considering the stimulus-sensor-observation (SSO) ontology design pattern, which focuses on facilitating sensor selection for one observation task. The core aim of the SOCA ontology model is to achieve an observation capability semantic association. A prototype system called SemOCAssociation was developed, and an experiment was conducted for flood observations in the Jinsha River basin in China. The results of this experiment verified that the SOCA ontology based association method can help sensor planners intuitively and accurately make evidence-based sensor selection decisions for a given flood observation task, which facilitates efficient and effective observational planning for flood satellite sensors.

A Probabilistic Ontology Development Methodology

DTIC Science & Technology

2014-06-01

Test, and Evaluation; Acquisition; and Planning and Marketing ," in Handbook of Systems Engineering and Management .: John Wiley & Sons, 2009, pp...Intelligence and knowledge management . However, many real world problems in these disciplines are burdened by incomplete information and other sources...knowledge engineering, Artificial Intelligence and knowledge management . However, many real world problems in these disciplines are burdened by

Is the crowd better as an assistant or a replacement in ontology engineering? An exploration through the lens of the Gene Ontology.

PubMed

Mortensen, Jonathan M; Telis, Natalie; Hughey, Jacob J; Fan-Minogue, Hua; Van Auken, Kimberly; Dumontier, Michel; Musen, Mark A

2016-04-01

Biomedical ontologies contain errors. Crowdsourcing, defined as taking a job traditionally performed by a designated agent and outsourcing it to an undefined large group of people, provides scalable access to humans. Therefore, the crowd has the potential to overcome the limited accuracy and scalability found in current ontology quality assurance approaches. Crowd-based methods have identified errors in SNOMED CT, a large, clinical ontology, with an accuracy similar to that of experts, suggesting that crowdsourcing is indeed a feasible approach for identifying ontology errors. This work uses that same crowd-based methodology, as well as a panel of experts, to verify a subset of the Gene Ontology (200 relationships). Experts identified 16 errors, generally in relationships referencing acids and metals. The crowd performed poorly in identifying those errors, with an area under the receiver operating characteristic curve ranging from 0.44 to 0.73, depending on the methods configuration. However, when the crowd verified what experts considered to be easy relationships with useful definitions, they performed reasonably well. Notably, there are significantly fewer Google search results for Gene Ontology concepts than SNOMED CT concepts. This disparity may account for the difference in performance - fewer search results indicate a more difficult task for the worker. The number of Internet search results could serve as a method to assess which tasks are appropriate for the crowd. These results suggest that the crowd fits better as an expert assistant, helping experts with their verification by completing the easy tasks and allowing experts to focus on the difficult tasks, rather than an expert replacement. Copyright © 2016 Elsevier Inc. All rights reserved.

OntoStudyEdit: a new approach for ontology-based representation and management of metadata in clinical and epidemiological research.

PubMed

Uciteli, Alexandr; Herre, Heinrich

2015-01-01

The specification of metadata in clinical and epidemiological study projects absorbs significant expense. The validity and quality of the collected data depend heavily on the precise and semantical correct representation of their metadata. In various research organizations, which are planning and coordinating studies, the required metadata are specified differently, depending on many conditions, e.g., on the used study management software. The latter does not always meet the needs of a particular research organization, e.g., with respect to the relevant metadata attributes and structuring possibilities. The objective of the research, set forth in this paper, is the development of a new approach for ontology-based representation and management of metadata. The basic features of this approach are demonstrated by the software tool OntoStudyEdit (OSE). The OSE is designed and developed according to the three ontology method. This method for developing software is based on the interactions of three different kinds of ontologies: a task ontology, a domain ontology and a top-level ontology. The OSE can be easily adapted to different requirements, and it supports an ontologically founded representation and efficient management of metadata. The metadata specifications can by imported from various sources; they can be edited with the OSE, and they can be exported in/to several formats, which are used, e.g., by different study management software. Advantages of this approach are the adaptability of the OSE by integrating suitable domain ontologies, the ontological specification of mappings between the import/export formats and the DO, the specification of the study metadata in a uniform manner and its reuse in different research projects, and an intuitive data entry for non-expert users.

Knowledge engineering as a support for building an actor profile ontology for integrating Home-Care systems.

PubMed

Gibert, Karina; Valls, Aida; Riaño, David

2008-01-01

One of the tasks towards the definition of a knowledge model for home care is the definition of the different roles of the users involved in the system. The roles determine the actions and services that can or must be performed by each type of user. In this paper the experience of building an ontology to represent the home-care users and their associated information is presented, in a proposal for a standard model of a Home-Care support system to the European Community.

MedSynDiKATe--design considerations for an ontology-based medical text understanding system.

PubMed Central

Hahn, U.; Romacker, M.; Schulz, S.

2000-01-01

MedSynDiKATe is a natural language processor for automatically acquiring knowledge from medical finding reports. The content of these documents is transferred to formal representation structures which constitute a corresponding text knowledge base. The general system architecture we present integrates requirements from the analysis of single sentences, as well as those of referentially linked sentences forming cohesive texts. The strong demands MedSynDiKATe poses to the availability of expressive knowledge sources are accounted for by two alternative approaches to (semi)automatic ontology engineering. PMID:11079899

Ontology based log content extraction engine for a posteriori security control.

PubMed

Azkia, Hanieh; Cuppens-Boulahia, Nora; Cuppens, Frédéric; Coatrieux, Gouenou

2012-01-01

In a posteriori access control, users are accountable for actions they performed and must provide evidence, when required by some legal authorities for instance, to prove that these actions were legitimate. Generally, log files contain the needed data to achieve this goal. This logged data can be recorded in several formats; we consider here IHE-ATNA (Integrating the healthcare enterprise-Audit Trail and Node Authentication) as log format. The difficulty lies in extracting useful information regardless of the log format. A posteriori access control frameworks often include a log filtering engine that provides this extraction function. In this paper we define and enforce this function by building an IHE-ATNA based ontology model, which we query using SPARQL, and show how the a posteriori security controls are made effective and easier based on this function.

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

PubMed Central

2014-01-01

Background Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. Methods G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Results Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. Conclusions G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user. PMID:25474588

G-Bean: an ontology-graph based web tool for biomedical literature retrieval.

PubMed

Wang, James Z; Zhang, Yuanyuan; Dong, Liang; Li, Lin; Srimani, Pradip K; Yu, Philip S

2014-01-01

Currently, most people use NCBI's PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently. G-Bean addresses PubMed's limitations with three innovations: (1) Parallel document index creation: a multithreaded index creation strategy is employed to generate the document index for G-Bean in parallel; (2) Ontology-graph based query expansion: an ontology graph is constructed by merging four major UMLS (Version 2013AA) vocabularies, MeSH, SNOMEDCT, CSP and AOD, to cover all concepts in National Library of Medicine (NLM) database; a Personalized PageRank algorithm is used to compute concept relevance in this ontology graph and the Term Frequency - Inverse Document Frequency (TF-IDF) weighting scheme is used to re-rank the concepts. The top 500 ranked concepts are selected for expanding the initial query to retrieve more accurate and relevant information; (3) Retrieval and re-ranking of documents based on user's search intention: after the user selects any article from the existing search results, G-Bean analyzes user's selections to determine his/her true search intention and then uses more relevant and more specific terms to retrieve additional related articles. The new articles are presented to the user in the order of their relevance to the already selected articles. Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php. G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user.

Knowledge Evolution in Distributed Geoscience Datasets and the Role of Semantic Technologies

NASA Astrophysics Data System (ADS)

Ma, X.

2014-12-01

Knowledge evolves in geoscience, and the evolution is reflected in datasets. In a context with distributed data sources, the evolution of knowledge may cause considerable challenges to data management and re-use. For example, a short news published in 2009 (Mascarelli, 2009) revealed the geoscience community's concern that the International Commission on Stratigraphy's change to the definition of Quaternary may bring heavy reworking of geologic maps. Now we are in the era of the World Wide Web, and geoscience knowledge is increasingly modeled and encoded in the form of ontologies and vocabularies by using semantic technologies. Accordingly, knowledge evolution leads to a consequence called ontology dynamics. Flouris et al. (2008) summarized 10 topics of general ontology changes/dynamics such as: ontology mapping, morphism, evolution, debugging and versioning, etc. Ontology dynamics makes impacts at several stages of a data life cycle and causes challenges, such as: the request for reworking of the extant data in a data center, semantic mismatch among data sources, differentiated understanding of a same piece of dataset between data providers and data users, as well as error propagation in cross-discipline data discovery and re-use (Ma et al., 2014). This presentation will analyze the best practices in the geoscience community so far and summarize a few recommendations to reduce the negative impacts of ontology dynamics in a data life cycle, including: communities of practice and collaboration on ontology and vocabulary building, link data records to standardized terms, and methods for (semi-)automatic reworking of datasets using semantic technologies. References: Flouris, G., Manakanatas, D., Kondylakis, H., Plexousakis, D., Antoniou, G., 2008. Ontology change: classification and survey. The Knowledge Engineering Review 23 (2), 117-152. Ma, X., Fox, P., Rozell, E., West, P., Zednik, S., 2014. Ontology dynamics in a data life cycle: Challenges and recommendations from a Geoscience Perspective. Journal of Earth Science 25 (2), 407-412. Mascarelli, A.L., 2009. Quaternary geologists win timescale vote. Nature 459, 624.

Towards symbiosis in knowledge representation and natural language processing for structuring clinical practice guidelines.

PubMed

Weng, Chunhua; Payne, Philip R O; Velez, Mark; Johnson, Stephen B; Bakken, Suzanne

2014-01-01

The successful adoption by clinicians of evidence-based clinical practice guidelines (CPGs) contained in clinical information systems requires efficient translation of free-text guidelines into computable formats. Natural language processing (NLP) has the potential to improve the efficiency of such translation. However, it is laborious to develop NLP to structure free-text CPGs using existing formal knowledge representations (KR). In response to this challenge, this vision paper discusses the value and feasibility of supporting symbiosis in text-based knowledge acquisition (KA) and KR. We compare two ontologies: (1) an ontology manually created by domain experts for CPG eligibility criteria and (2) an upper-level ontology derived from a semantic pattern-based approach for automatic KA from CPG eligibility criteria text. Then we discuss the strengths and limitations of interweaving KA and NLP for KR purposes and important considerations for achieving the symbiosis of KR and NLP for structuring CPGs to achieve evidence-based clinical practice.

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project: Phase 5

DTIC Science & Technology

2017-04-30

Principal Investigator: Dr. Barry Boehm, University of Southern California Research Team: Organizations 1: Air force Institute of Technology...Date April 30, 2017 Copyright © 2017 Stevens Institute of Technology, Systems Engineering Research Center The Systems Engineering Research ...Center (SERC) is a federally funded University Affiliated Research Center managed by Stevens Institute of Technology. This material is based upon

Web-based Traffic Noise Control Support System for Sustainable Transportation

NASA Astrophysics Data System (ADS)

Fan, Lisa; Dai, Liming; Li, Anson

Traffic noise is considered as one of the major pollutions that will affect our communities in the future. This paper presents a framework of web-based traffic noise control support system (WTNCSS) for a sustainable transportation. WTNCSS is to provide the decision makers, engineers and publics a platform to efficiently access the information, and effectively making decisions related to traffic control. The system is based on a Service Oriented Architecture (SOA) which takes the advantages of the convenience of World Wide Web system with the data format of XML. The whole system is divided into different modules such as the prediction module, ontology-based expert module and dynamic online survey module. Each module of the system provides a distinct information service to the decision support center through the HTTP protocol.

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

DOE Office of Scientific and Technical Information (OSTI.GOV)

McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks thatmore » are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a predictive model of transcriptional expression levels.« less

Methodology for identifying and representing knowledge in the scope of CMM inspection resource selection

NASA Astrophysics Data System (ADS)

Martínez, S.; Barreiro, J.; Cuesta, E.; Álvarez, B. J.; González, D.

2012-04-01

This paper is focused on the task of elicitation and structuring of knowledge related to selection of inspection resources. The final goal is to obtain an informal model of knowledge oriented to the inspection planning in coordinate measuring machines. In the first tasks, where knowledge is captured, it is necessary to use tools that make easier the analysis and structuring of knowledge, so that rules of selection can be easily stated to configure the inspection resources. In order to store the knowledge a so-called Onto-Process ontology has been developed. This ontology may be of application to diverse processes in manufacturing engineering. This paper describes the decomposition of the ontology in terms of general units of knowledge and others more specific for selection of sensor assemblies in inspection planning with touch sensors.

Software-engineering challenges of building and deploying reusable problem solvers.

PubMed

O'Connor, Martin J; Nyulas, Csongor; Tu, Samson; Buckeridge, David L; Okhmatovskaia, Anna; Musen, Mark A

2009-11-01

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task-method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach.

Software-engineering challenges of building and deploying reusable problem solvers

PubMed Central

O’CONNOR, MARTIN J.; NYULAS, CSONGOR; TU, SAMSON; BUCKERIDGE, DAVID L.; OKHMATOVSKAIA, ANNA; MUSEN, MARK A.

2012-01-01

Problem solving methods (PSMs) are software components that represent and encode reusable algorithms. They can be combined with representations of domain knowledge to produce intelligent application systems. A goal of research on PSMs is to provide principled methods and tools for composing and reusing algorithms in knowledge-based systems. The ultimate objective is to produce libraries of methods that can be easily adapted for use in these systems. Despite the intuitive appeal of PSMs as conceptual building blocks, in practice, these goals are largely unmet. There are no widely available tools for building applications using PSMs and no public libraries of PSMs available for reuse. This paper analyzes some of the reasons for the lack of widespread adoptions of PSM techniques and illustrate our analysis by describing our experiences developing a complex, high-throughput software system based on PSM principles. We conclude that many fundamental principles in PSM research are useful for building knowledge-based systems. In particular, the task–method decomposition process, which provides a means for structuring knowledge-based tasks, is a powerful abstraction for building systems of analytic methods. However, despite the power of PSMs in the conceptual modeling of knowledge-based systems, software engineering challenges have been seriously underestimated. The complexity of integrating control knowledge modeled by developers using PSMs with the domain knowledge that they model using ontologies creates a barrier to widespread use of PSM-based systems. Nevertheless, the surge of recent interest in ontologies has led to the production of comprehensive domain ontologies and of robust ontology-authoring tools. These developments present new opportunities to leverage the PSM approach. PMID:23565031

Annotation of phenotypic diversity: decoupling data curation and ontology curation using Phenex.

PubMed

Balhoff, James P; Dahdul, Wasila M; Dececchi, T Alexander; Lapp, Hilmar; Mabee, Paula M; Vision, Todd J

2014-01-01

Phenex (http://phenex.phenoscape.org/) is a desktop application for semantically annotating the phenotypic character matrix datasets common in evolutionary biology. Since its initial publication, we have added new features that address several major bottlenecks in the efficiency of the phenotype curation process: allowing curators during the data curation phase to provisionally request terms that are not yet available from a relevant ontology; supporting quality control against annotation guidelines to reduce later manual review and revision; and enabling the sharing of files for collaboration among curators. We decoupled data annotation from ontology development by creating an Ontology Request Broker (ORB) within Phenex. Curators can use the ORB to request a provisional term for use in data annotation; the provisional term can be automatically replaced with a permanent identifier once the term is added to an ontology. We added a set of annotation consistency checks to prevent common curation errors, reducing the need for later correction. We facilitated collaborative editing by improving the reliability of Phenex when used with online folder sharing services, via file change monitoring and continual autosave. With the addition of these new features, and in particular the Ontology Request Broker, Phenex users have been able to focus more effectively on data annotation. Phenoscape curators using Phenex have reported a smoother annotation workflow, with much reduced interruptions from ontology maintenance and file management issues.

OMIT: dynamic, semi-automated ontology development for the microRNA domain.

PubMed

Huang, Jingshan; Dang, Jiangbo; Borchert, Glen M; Eilbeck, Karen; Zhang, He; Xiong, Min; Jiang, Weijian; Wu, Hao; Blake, Judith A; Natale, Darren A; Tan, Ming

2014-01-01

As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology.

OMIT: Dynamic, Semi-Automated Ontology Development for the microRNA Domain

PubMed Central

Huang, Jingshan; Dang, Jiangbo; Borchert, Glen M.; Eilbeck, Karen; Zhang, He; Xiong, Min; Jiang, Weijian; Wu, Hao; Blake, Judith A.; Natale, Darren A.; Tan, Ming

2014-01-01

As a special class of short non-coding RNAs, microRNAs (a.k.a. miRNAs or miRs) have been reported to perform important roles in various biological processes by regulating respective target genes. However, significant barriers exist during biologists' conventional miR knowledge discovery. Emerging semantic technologies, which are based upon domain ontologies, can render critical assistance to this problem. Our previous research has investigated the construction of a miR ontology, named Ontology for MIcroRNA Target Prediction (OMIT), the very first of its kind that formally encodes miR domain knowledge. Although it is unavoidable to have a manual component contributed by domain experts when building ontologies, many challenges have been identified for a completely manual development process. The most significant issue is that a manual development process is very labor-intensive and thus extremely expensive. Therefore, we propose in this paper an innovative ontology development methodology. Our contributions can be summarized as: (i) We have continued the development and critical improvement of OMIT, solidly based on our previous research outcomes. (ii) We have explored effective and efficient algorithms with which the ontology development can be seamlessly combined with machine intelligence and be accomplished in a semi-automated manner, thus significantly reducing large amounts of human efforts. A set of experiments have been conducted to thoroughly evaluate our proposed methodology. PMID:25025130

A Semantic Approach for Knowledge Discovery to Help Mitigate Habitat Loss in the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Ramachandran, R.; Maskey, M.; Graves, S.; Hardin, D.

2008-12-01

Noesis is a meta-search engine and a resource aggregator that uses domain ontologies to provide scoped search capabilities. Ontologies enable Noesis to help users refine their searches for information on the open web and in hidden web locations such as data catalogues with standardized, but discipline specific vocabularies. Through its ontologies Noesis provides a guided refinement of search queries which produces complete and accurate searches while reducing the user's burden to experiment with different search strings. All search results are organized by categories (e. g. all results from Google are grouped together) which may be selected or omitted according to the desire of the user. During the past two years ontologies were developed for sea grasses in the Gulf of Mexico and were used to support a habitat restoration demonstration project. Currently these ontologies are being augmented to address the special characteristics of mangroves. These new ontologies will extend the demonstration project to broader regions of the Gulf including protected mangrove locations in coastal Mexico. Noesis contributes to the decision making process by producing a comprehensive list of relevant resources based on the semantic information contained in the ontologies. Ontologies are organized in a tree like taxonomies, where the child nodes represent the Specializations and the parent nodes represent the Generalizations of a node or concept. Specializations can be used to provide more detailed search, while generalizations are used to make the search broader. Ontologies are also used to link two syntactically different terms to one semantic concept (synonyms). Appending a synonym to the query expands the search, thus providing better search coverage. Every concept has a set of properties that are neither in the same inheritance hierarchy (Specializations / Generalizations) nor equivalent (synonyms). These are called Related Concepts and they are captured in the ontology through property relationships. By using Related Concepts users can search for resources with respect to a particular property. Noesis automatically generates searches that include all of these capabilities, removing the burden from the user and producing broader and more accurate search results. This presentation will demonstrate the features of Noesis and describe its application to habitat studies in the Gulf of Mexico.

DEVA: An extensible ontology-based annotation model for visual document collections

NASA Astrophysics Data System (ADS)

Jelmini, Carlo; Marchand-Maillet, Stephane

2003-01-01

The description of visual documents is a fundamental aspect of any efficient information management system, but the process of manually annotating large collections of documents is tedious and far from being perfect. The need for a generic and extensible annotation model therefore arises. In this paper, we present DEVA, an open, generic and expressive multimedia annotation framework. DEVA is an extension of the Dublin Core specification. The model can represent the semantic content of any visual document. It is described in the ontology language DAML+OIL and can easily be extended with external specialized ontologies, adapting the vocabulary to the given application domain. In parallel, we present the Magritte annotation tool, which is an early prototype that validates the DEVA features. Magritte allows to manually annotating image collections. It is designed with a modular and extensible architecture, which enables the user to dynamically adapt the user interface to specialized ontologies merged into DEVA.

Ontology-Driven Knowledge-Based Health-Care System, An Emerging Area - Challenges And Opportunities - Indian Scenario

NASA Astrophysics Data System (ADS)

Sunitha, A.; Babu, G. Suresh

2014-11-01

Recent studies in the decision making efforts in the area of public healthcare systems have been tremendously inspired and influenced by the entry of ontology. Ontology driven systems results in the effective implementation of healthcare strategies for the policy makers. The central source of knowledge is the ontology containing all the relevant domain concepts such as locations, diseases, environments and their domain sensitive inter-relationships which is the prime objective, concern and the motivation behind this paper. The paper further focuses on the development of a semantic knowledge-base for public healthcare system. This paper describes the approach and methodologies in bringing out a novel conceptual theme in establishing a firm linkage between three different ontologies related to diseases, places and environments in one integrated platform. This platform correlates the real-time mechanisms prevailing within the semantic knowledgebase and establishing their inter-relationships for the first time in India. This is hoped to formulate a strong foundation for establishing a much awaited basic need for a meaningful healthcare decision making system in the country. Introduction through a wide range of best practices facilitate the adoption of this approach for better appreciation, understanding and long term outcomes in the area. The methods and approach illustrated in the paper relate to health mapping methods, reusability of health applications, and interoperability issues based on mapping of the data attributes with ontology concepts in generating semantic integrated data driving an inference engine for user-interfaced semantic queries.

Common Criteria Related Security Design Patterns for Intelligent Sensors—Knowledge Engineering-Based Implementation

PubMed Central

Bialas, Andrzej

2011-01-01

Intelligent sensors experience security problems very similar to those inherent to other kinds of IT products or systems. The assurance for these products or systems creation methodologies, like Common Criteria (ISO/IEC 15408) can be used to improve the robustness of the sensor systems in high risk environments. The paper presents the background and results of the previous research on patterns-based security specifications and introduces a new ontological approach. The elaborated ontology and knowledge base were validated on the IT security development process dealing with the sensor example. The contribution of the paper concerns the application of the knowledge engineering methodology to the previously developed Common Criteria compliant and pattern-based method for intelligent sensor security development. The issue presented in the paper has a broader significance in terms that it can solve information security problems in many application domains. PMID:22164064

Common criteria related security design patterns for intelligent sensors--knowledge engineering-based implementation.

PubMed

Bialas, Andrzej

2011-01-01

Intelligent sensors experience security problems very similar to those inherent to other kinds of IT products or systems. The assurance for these products or systems creation methodologies, like Common Criteria (ISO/IEC 15408) can be used to improve the robustness of the sensor systems in high risk environments. The paper presents the background and results of the previous research on patterns-based security specifications and introduces a new ontological approach. The elaborated ontology and knowledge base were validated on the IT security development process dealing with the sensor example. The contribution of the paper concerns the application of the knowledge engineering methodology to the previously developed Common Criteria compliant and pattern-based method for intelligent sensor security development. The issue presented in the paper has a broader significance in terms that it can solve information security problems in many application domains.

Mining and Utilizing Dataset Relevancy from Oceanographic Dataset (MUDROD) Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access

NASA Astrophysics Data System (ADS)

Li, Y.; Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.

2016-12-01

Big oceanographic data have been produced, archived and made available online, but finding the right data for scientific research and application development is still a significant challenge. A long-standing problem in data discovery is how to find the interrelationships between keywords and data, as well as the intrarelationships of the two individually. Most previous research attempted to solve this problem by building domain-specific ontology either manually or through automatic machine learning techniques. The former is costly, labor intensive and hard to keep up-to-date, while the latter is prone to noise and may be difficult for human to understand. Large-scale user behavior data modelling represents a largely untapped, unique, and valuable source for discovering semantic relationships among domain-specific vocabulary. In this article, we propose a search engine framework for mining and utilizing dataset relevancy from oceanographic dataset metadata, user behaviors, and existing ontology. The objective is to improve discovery accuracy of oceanographic data and reduce time for scientist to discover, download and reformat data for their projects. Experiments and a search example show that the proposed search engine helps both scientists and general users search with better ranking results, recommendation, and ontology navigation.

Evolutionary characters, phenotypes and ontologies: curating data from the systematic biology literature.

PubMed

Dahdul, Wasila M; Balhoff, James P; Engeman, Jeffrey; Grande, Terry; Hilton, Eric J; Kothari, Cartik; Lapp, Hilmar; Lundberg, John G; Midford, Peter E; Vision, Todd J; Westerfield, Monte; Mabee, Paula M

2010-05-20

The wealth of phenotypic descriptions documented in the published articles, monographs, and dissertations of phylogenetic systematics is traditionally reported in a free-text format, and it is therefore largely inaccessible for linkage to biological databases for genetics, development, and phenotypes, and difficult to manage for large-scale integrative work. The Phenoscape project aims to represent these complex and detailed descriptions with rich and formal semantics that are amenable to computation and integration with phenotype data from other fields of biology. This entails reconceptualizing the traditional free-text characters into the computable Entity-Quality (EQ) formalism using ontologies. We used ontologies and the EQ formalism to curate a collection of 47 phylogenetic studies on ostariophysan fishes (including catfishes, characins, minnows, knifefishes) and their relatives with the goal of integrating these complex phenotype descriptions with information from an existing model organism database (zebrafish, http://zfin.org). We developed a curation workflow for the collection of character, taxonomic and specimen data from these publications. A total of 4,617 phenotypic characters (10,512 states) for 3,449 taxa, primarily species, were curated into EQ formalism (for a total of 12,861 EQ statements) using anatomical and taxonomic terms from teleost-specific ontologies (Teleost Anatomy Ontology and Teleost Taxonomy Ontology) in combination with terms from a quality ontology (Phenotype and Trait Ontology). Standards and guidelines for consistently and accurately representing phenotypes were developed in response to the challenges that were evident from two annotation experiments and from feedback from curators. The challenges we encountered and many of the curation standards and methods for improving consistency that we developed are generally applicable to any effort to represent phenotypes using ontologies. This is because an ontological representation of the detailed variations in phenotype, whether between mutant or wildtype, among individual humans, or across the diversity of species, requires a process by which a precise combination of terms from domain ontologies are selected and organized according to logical relations. The efficiencies that we have developed in this process will be useful for any attempt to annotate complex phenotypic descriptions using ontologies. We also discuss some ramifications of EQ representation for the domain of systematics.

An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies.

PubMed

Zhang, Guo-Qiang; Xing, Guangming; Cui, Licong

2018-04-01

One of the basic challenges in developing structural methods for systematic audition on the quality of biomedical ontologies is the computational cost usually involved in exhaustive sub-graph analysis. We introduce ANT-LCA, a new algorithm for computing all non-trivial lowest common ancestors (LCA) of each pair of concepts in the hierarchical order induced by an ontology. The computation of LCA is a fundamental step for non-lattice approach for ontology quality assurance. Distinct from existing approaches, ANT-LCA only computes LCAs for non-trivial pairs, those having at least one common ancestor. To skip all trivial pairs that may be of no practical interest, ANT-LCA employs a simple but innovative algorithmic strategy combining topological order and dynamic programming to keep track of non-trivial pairs. We provide correctness proofs and demonstrate a substantial reduction in computational time for two largest biomedical ontologies: SNOMED CT and Gene Ontology (GO). ANT-LCA achieved an average computation time of 30 and 3 sec per version for SNOMED CT and GO, respectively, about 2 orders of magnitude faster than the best known approaches. Our algorithm overcomes a fundamental computational barrier in sub-graph based structural analysis of large ontological systems. It enables the implementation of a new breed of structural auditing methods that not only identifies potential problematic areas, but also automatically suggests changes to fix the issues. Such structural auditing methods can lead to more effective tools supporting ontology quality assurance work. Copyright © 2018 Elsevier Inc. All rights reserved.

Ontological Engineering and Mapping in Multiagent Systems Development

DTIC Science & Technology

2002-03-01

for knowledge engineering or artificial intelligence . Nicola Guarino compares the various definitions and the differences in their meaning in...act upon the environment through effectors [Russel and Norvig 1995]. An intelligent agent is an agent that takes the best possible action in a...situation in order to accomplish its goals. Determining what exactly characterizes the best possible action splits the field of artificial intelligence

Federated ontology-based queries over cancer data

PubMed Central

2012-01-01

Background Personalised medicine provides patients with treatments that are specific to their genetic profiles. It requires efficient data sharing of disparate data types across a variety of scientific disciplines, such as molecular biology, pathology, radiology and clinical practice. Personalised medicine aims to offer the safest and most effective therapeutic strategy based on the gene variations of each subject. In particular, this is valid in oncology, where knowledge about genetic mutations has already led to new therapies. Current molecular biology techniques (microarrays, proteomics, epigenetic technology and improved DNA sequencing technology) enable better characterisation of cancer tumours. The vast amounts of data, however, coupled with the use of different terms - or semantic heterogeneity - in each discipline makes the retrieval and integration of information difficult. Results Existing software infrastructures for data-sharing in the cancer domain, such as caGrid, support access to distributed information. caGrid follows a service-oriented model-driven architecture. Each data source in caGrid is associated with metadata at increasing levels of abstraction, including syntactic, structural, reference and domain metadata. The domain metadata consists of ontology-based annotations associated with the structural information of each data source. However, caGrid's current querying functionality is given at the structural metadata level, without capitalising on the ontology-based annotations. This paper presents the design of and theoretical foundations for distributed ontology-based queries over cancer research data. Concept-based queries are reformulated to the target query language, where join conditions between multiple data sources are found by exploiting the semantic annotations. The system has been implemented, as a proof of concept, over the caGrid infrastructure. The approach is applicable to other model-driven architectures. A graphical user interface has been developed, supporting ontology-based queries over caGrid data sources. An extensive evaluation of the query reformulation technique is included. Conclusions To support personalised medicine in oncology, it is crucial to retrieve and integrate molecular, pathology, radiology and clinical data in an efficient manner. The semantic heterogeneity of the data makes this a challenging task. Ontologies provide a formal framework to support querying and integration. This paper provides an ontology-based solution for querying distributed databases over service-oriented, model-driven infrastructures. PMID:22373043

Spatial information semantic query based on SPARQL

NASA Astrophysics Data System (ADS)

Xiao, Zhifeng; Huang, Lei; Zhai, Xiaofang

2009-10-01

How can the efficiency of spatial information inquiries be enhanced in today's fast-growing information age? We are rich in geospatial data but poor in up-to-date geospatial information and knowledge that are ready to be accessed by public users. This paper adopts an approach for querying spatial semantic by building an Web Ontology language(OWL) format ontology and introducing SPARQL Protocol and RDF Query Language(SPARQL) to search spatial semantic relations. It is important to establish spatial semantics that support for effective spatial reasoning for performing semantic query. Compared to earlier keyword-based and information retrieval techniques that rely on syntax, we use semantic approaches in our spatial queries system. Semantic approaches need to be developed by ontology, so we use OWL to describe spatial information extracted by the large-scale map of Wuhan. Spatial information expressed by ontology with formal semantics is available to machines for processing and to people for understanding. The approach is illustrated by introducing a case study for using SPARQL to query geo-spatial ontology instances of Wuhan. The paper shows that making use of SPARQL to search OWL ontology instances can ensure the result's accuracy and applicability. The result also indicates constructing a geo-spatial semantic query system has positive efforts on forming spatial query and retrieval.

Constructing Adverse Outcome Pathways: a Demonstration of ...

EPA Pesticide Factsheets

Adverse outcome pathway (AOP) provides a conceptual framework to evaluate and integrate chemical toxicity and its effects across the levels of biological organization. As such, it is essential to develop a resource-efficient and effective approach to extend molecular initiating events (MIEs) of chemicals to their downstream phenotypes of a greater regulatory relevance. A number of ongoing public phenomics (high throughput phenotyping) efforts have been generating abundant phenotypic data annotated with ontology terms. These phenotypes can be analyzed semantically and linked to MIEs of interest, all in the context of a knowledge base integrated from a variety of ontologies for various species and knowledge domains. In such analyses, two phenotypic profiles (PPs; anchored by genes or diseases) each characterized by multiple ontology terms are compared for their semantic similarities within a common ontology graph, but across boundaries of species and knowledge domains. Taking advantage of publicly available ontologies and software tool kits, we have implemented an OS-Mapping (Ontology-based Semantics Mapping) approach as a Java application, and constructed a network of 19383 PPs as nodes with edges weighed by their pairwise semantic similarity scores. Individual PPs were assembled from public phenomics data. Out of possible 1.87×108 pairwise connections among these nodes, about 71% of them have similarity scores between 0.2 and the maximum possible of 1.0.

Constructing a Geology Ontology Using a Relational Database

NASA Astrophysics Data System (ADS)

Hou, W.; Yang, L.; Yin, S.; Ye, J.; Clarke, K.

2013-12-01

In geology community, the creation of a common geology ontology has become a useful means to solve problems of data integration, knowledge transformation and the interoperation of multi-source, heterogeneous and multiple scale geological data. Currently, human-computer interaction methods and relational database-based methods are the primary ontology construction methods. Some human-computer interaction methods such as the Geo-rule based method, the ontology life cycle method and the module design method have been proposed for applied geological ontologies. Essentially, the relational database-based method is a reverse engineering of abstracted semantic information from an existing database. The key is to construct rules for the transformation of database entities into the ontology. Relative to the human-computer interaction method, relational database-based methods can use existing resources and the stated semantic relationships among geological entities. However, two problems challenge the development and application. One is the transformation of multiple inheritances and nested relationships and their representation in an ontology. The other is that most of these methods do not measure the semantic retention of the transformation process. In this study, we focused on constructing a rule set to convert the semantics in a geological database into a geological ontology. According to the relational schema of a geological database, a conversion approach is presented to convert a geological spatial database to an OWL-based geological ontology, which is based on identifying semantics such as entities, relationships, inheritance relationships, nested relationships and cluster relationships. The semantic integrity of the transformation was verified using an inverse mapping process. In a geological ontology, an inheritance and union operations between superclass and subclass were used to present the nested relationship in a geochronology and the multiple inheritances relationship. Based on a Quaternary database of downtown of Foshan city, Guangdong Province, in Southern China, a geological ontology was constructed using the proposed method. To measure the maintenance of semantics in the conversation process and the results, an inverse mapping from the ontology to a relational database was tested based on a proposed conversation rule. The comparison of schema and entities and the reduction of tables between the inverse database and the original database illustrated that the proposed method retains the semantic information well during the conversation process. An application for abstracting sandstone information showed that semantic relationships among concepts in the geological database were successfully reorganized in the constructed ontology. Key words: geological ontology; geological spatial database; multiple inheritance; OWL Acknowledgement: This research is jointly funded by the Specialized Research Fund for the Doctoral Program of Higher Education of China (RFDP) (20100171120001), NSFC (41102207) and the Fundamental Research Funds for the Central Universities (12lgpy19).

Using ontological inference and hierarchical matchmaking to overcome semantic heterogeneity in remote sensing-based biodiversity monitoring

NASA Astrophysics Data System (ADS)

Nieland, Simon; Kleinschmit, Birgit; Förster, Michael

2015-05-01

Ontology-based applications hold promise in improving spatial data interoperability. In this work we use remote sensing-based biodiversity information and apply semantic formalisation and ontological inference to show improvements in data interoperability/comparability. The proposed methodology includes an observation-based, "bottom-up" engineering approach for remote sensing applications and gives a practical example of semantic mediation of geospatial products. We apply the methodology to three different nomenclatures used for remote sensing-based classification of two heathland nature conservation areas in Belgium and Germany. We analysed sensor nomenclatures with respect to their semantic formalisation and their bio-geographical differences. The results indicate that a hierarchical and transparent nomenclature is far more important for transferability than the sensor or study area. The inclusion of additional information, not necessarily belonging to a vegetation class description, is a key factor for the future success of using semantics for interoperability in remote sensing.

An Ontology-Based Conceptual Model For Accumulating And Reusing Knowledge In A DMAIC Process

NASA Astrophysics Data System (ADS)

Nguyen, ThanhDat; Kifor, Claudiu Vasile

2015-09-01

DMAIC (Define, Measure, Analyze, Improve, and Control) is an important process used to enhance quality of processes basing on knowledge. However, it is difficult to access DMAIC knowledge. Conventional approaches meet a problem arising from structuring and reusing DMAIC knowledge. The main reason is that DMAIC knowledge is not represented and organized systematically. In this article, we overcome the problem basing on a conceptual model that is a combination of DMAIC process, knowledge management, and Ontology engineering. The main idea of our model is to utilizing Ontologies to represent knowledge generated by each of DMAIC phases. We build five different knowledge bases for storing all knowledge of DMAIC phases with the support of necessary tools and appropriate techniques in Information Technology area. Consequently, these knowledge bases provide knowledge available to experts, managers, and web users during or after DMAIC execution in order to share and reuse existing knowledge.

Semantically optiMize the dAta seRvice operaTion (SMART) system for better data discovery and access

NASA Astrophysics Data System (ADS)

Yang, C.; Huang, T.; Armstrong, E. M.; Moroni, D. F.; Liu, K.; Gui, Z.

2013-12-01

Abstract: We present a Semantically optiMize the dAta seRvice operaTion (SMART) system for better data discovery and access across the NASA data systems, Global Earth Observation System of Systems (GEOSS) Clearinghouse and Data.gov to facilitate scientists to select Earth observation data that fit better their needs in four aspects: 1. Integrating and interfacing the SMART system to include the functionality of a) semantic reasoning based on Jena, an open source semantic reasoning engine, b) semantic similarity calculation, c) recommendation based on spatiotemporal, semantic, and user workflow patterns, and d) ranking results based on similarity between search terms and data ontology. 2. Collaborating with data user communities to a) capture science data ontology and record relevant ontology triple stores, b) analyze and mine user search and download patterns, c) integrate SMART into metadata-centric discovery system for community-wide usage and feedback, and d) customizing data discovery, search and access user interface to include the ranked results, recommendation components, and semantic based navigations. 3. Laying the groundwork to interface the SMART system with other data search and discovery systems as an open source data search and discovery solution. The SMART systems leverages NASA, GEO, FGDC data discovery, search and access for the Earth science community by enabling scientists to readily discover and access data appropriate to their endeavors, increasing the efficiency of data exploration and decreasing the time that scientists must spend on searching, downloading, and processing the datasets most applicable to their research. By incorporating the SMART system, it is a likely aim that the time being devoted to discovering the most applicable dataset will be substantially reduced, thereby reducing the number of user inquiries and likewise reducing the time and resources expended by a data center in addressing user inquiries. Keywords: EarthCube; ECHO, DAACs, GeoPlatform; Geospatial Cyberinfrastructure References: 1. Yang, P., Evans, J., Cole, M., Alameh, N., Marley, S., & Bambacus, M., (2007). The Emerging Concepts and Applications of the Spatial Web Portal. Photogrammetry Engineering &Remote Sensing,73(6):691-698. 2. Zhang, C, Zhao, T. and W. Li. (2010). The Framework of a Geospatial Semantic Web based Spatial Decision Support System for Digital Earth. International Journal of Digital Earth. 3(2):111-134. 3. Yang C., Raskin R., Goodchild M.F., Gahegan M., 2010, Geospatial Cyberinfrastructure: Past, Present and Future,Computers, Environment, and Urban Systems, 34(4):264-277. 4. Liu K., Yang C., Li W., Gui Z., Xu C., Xia J., 2013. Using ontology and similarity calculations to rank Earth science data searching results, International Journal of Geospatial Information Applications. (in press)

SAFOD Brittle Microstructure and Mechanics Knowledge Base (BM2KB)

NASA Astrophysics Data System (ADS)

Babaie, Hassan A.; Broda Cindi, M.; Hadizadeh, Jafar; Kumar, Anuj

2013-07-01

Scientific drilling near Parkfield, California has established the San Andreas Fault Observatory at Depth (SAFOD), which provides the solid earth community with short range geophysical and fault zone material data. The BM2KB ontology was developed in order to formalize the knowledge about brittle microstructures in the fault rocks sampled from the SAFOD cores. A knowledge base, instantiated from this domain ontology, stores and presents the observed microstructural and analytical data with respect to implications for brittle deformation and mechanics of faulting. These data can be searched on the knowledge base‧s Web interface by selecting a set of terms (classes, properties) from different drop-down lists that are dynamically populated from the ontology. In addition to this general search, a query can also be conducted to view data contributed by a specific investigator. A search by sample is done using the EarthScope SAFOD Core Viewer that allows a user to locate samples on high resolution images of core sections belonging to different runs and holes. The class hierarchy of the BM2KB ontology was initially designed using the Unified Modeling Language (UML), which was used as a visual guide to develop the ontology in OWL applying the Protégé ontology editor. Various Semantic Web technologies such as the RDF, RDFS, and OWL ontology languages, SPARQL query language, and Pellet reasoning engine, were used to develop the ontology. An interactive Web application interface was developed through Jena, a java based framework, with AJAX technology, jsp pages, and java servlets, and deployed via an Apache tomcat server. The interface allows the registered user to submit data related to their research on a sample of the SAFOD core. The submitted data, after initial review by the knowledge base administrator, are added to the extensible knowledge base and become available in subsequent queries to all types of users. The interface facilitates inference capabilities in the ontology, supports SPARQL queries, allows for modifications based on successive discoveries, and provides an accessible knowledge base on the Web.

Ontology for assessment studies of human-computer-interaction in surgery.

PubMed

Machno, Andrej; Jannin, Pierre; Dameron, Olivier; Korb, Werner; Scheuermann, Gerik; Meixensberger, Jürgen

2015-02-01

New technologies improve modern medicine, but may result in unwanted consequences. Some occur due to inadequate human-computer-interactions (HCI). To assess these consequences, an investigation model was developed to facilitate the planning, implementation and documentation of studies for HCI in surgery. The investigation model was formalized in Unified Modeling Language and implemented as an ontology. Four different top-level ontologies were compared: Object-Centered High-level Reference, Basic Formal Ontology, General Formal Ontology (GFO) and Descriptive Ontology for Linguistic and Cognitive Engineering, according to the three major requirements of the investigation model: the domain-specific view, the experimental scenario and the representation of fundamental relations. Furthermore, this article emphasizes the distinction of "information model" and "model of meaning" and shows the advantages of implementing the model in an ontology rather than in a database. The results of the comparison show that GFO fits the defined requirements adequately: the domain-specific view and the fundamental relations can be implemented directly, only the representation of the experimental scenario requires minor extensions. The other candidates require wide-ranging extensions, concerning at least one of the major implementation requirements. Therefore, the GFO was selected to realize an appropriate implementation of the developed investigation model. The ensuing development considered the concrete implementation of further model aspects and entities: sub-domains, space and time, processes, properties, relations and functions. The investigation model and its ontological implementation provide a modular guideline for study planning, implementation and documentation within the area of HCI research in surgery. This guideline helps to navigate through the whole study process in the form of a kind of standard or good clinical practice, based on the involved foundational frameworks. Furthermore, it allows to acquire the structured description of the applied assessment methods within a certain surgical domain and to consider this information for own study design or to perform a comparison of different studies. The investigation model and the corresponding ontology can be used further to create new knowledge bases of HCI assessment in surgery. Copyright © 2014 Elsevier B.V. All rights reserved.

Engineering genders: A spatial analysis of engineering, gender, and learning

NASA Astrophysics Data System (ADS)

Weidler-Lewis, Joanna R.

This three article dissertation is an investigation into the ontology of learning insofar as learning is a process of becoming. In each article I explore the general questions of who is learning, in what ways, and with what consequences. The context for this research is undergraduate engineering education with particular attention to the construction of gender in this context. The first article is an examination of the organization of freshman engineering design. The second article draws on Lefebvre's spatial triad as both a theory and method for studying learning. The third article is an interview study of LGBTQA students creating their futures as engineers.

Facilitating Cohort Discovery by Enhancing Ontology Exploration, Query Management and Query Sharing for Large Clinical Data Repositories.

PubMed

Tao, Shiqiang; Cui, Licong; Wu, Xi; Zhang, Guo-Qiang

2017-01-01

To help researchers better access clinical data, we developed a prototype query engine called DataSphere for exploring large-scale integrated clinical data repositories. DataSphere expedites data importing using a NoSQL data management system and dynamically renders its user interface for concept-based querying tasks. DataSphere provides an interactive query-building interface together with query translation and optimization strategies, which enable users to build and execute queries effectively and efficiently. We successfully loaded a dataset of one million patients for University of Kentucky (UK) Healthcare into DataSphere with more than 300 million clinical data records. We evaluated DataSphere by comparing it with an instance of i2b2 deployed at UK Healthcare, demonstrating that DataSphere provides enhanced user experience for both query building and execution.

Facilitating Cohort Discovery by Enhancing Ontology Exploration, Query Management and Query Sharing for Large Clinical Data Repositories

PubMed Central

Tao, Shiqiang; Cui, Licong; Wu, Xi; Zhang, Guo-Qiang

2017-01-01

To help researchers better access clinical data, we developed a prototype query engine called DataSphere for exploring large-scale integrated clinical data repositories. DataSphere expedites data importing using a NoSQL data management system and dynamically renders its user interface for concept-based querying tasks. DataSphere provides an interactive query-building interface together with query translation and optimization strategies, which enable users to build and execute queries effectively and efficiently. We successfully loaded a dataset of one million patients for University of Kentucky (UK) Healthcare into DataSphere with more than 300 million clinical data records. We evaluated DataSphere by comparing it with an instance of i2b2 deployed at UK Healthcare, demonstrating that DataSphere provides enhanced user experience for both query building and execution. PMID:29854239

Feature-opinion pair identification of product reviews in Chinese: a domain ontology modeling method

NASA Astrophysics Data System (ADS)

Yin, Pei; Wang, Hongwei; Guo, Kaiqiang

2013-03-01

With the emergence of the new economy based on social media, a great amount of consumer feedback on particular products are conveyed through wide-spreading product online reviews, making opinion mining a growing interest for both academia and industry. According to the characteristic mode of expression in Chinese, this research proposes an ontology-based linguistic model to identify the basic appraisal expression in Chinese product reviews-"feature-opinion pair (FOP)." The product-oriented domain ontology is constructed automatically at first, then algorithms to identify FOP are designed by mapping product features and opinions to the conceptual space of the domain ontology, and finally comparative experiments are conducted to evaluate the model. Experimental results indicate that the performance of the proposed approach in this paper is efficient in obtaining a more accurate result compared to the state-of-art algorithms. Furthermore, through identifying and analyzing FOPs, the unstructured product reviews are converted into structured and machine-sensible expression, which provides valuable information for business application. This paper contributes to the related research in opinion mining by developing a solid foundation for further sentiment analysis at a fine-grained level and proposing a general way for automatic ontology construction.

Ontology-guided data preparation for discovering genotype-phenotype relationships.

PubMed

Coulet, Adrien; Smaïl-Tabbone, Malika; Benlian, Pascale; Napoli, Amedeo; Devignes, Marie-Dominique

2008-04-25

Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning. This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results. The method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.

Cyber Forensics Ontology for Cyber Criminal Investigation

NASA Astrophysics Data System (ADS)

Park, Heum; Cho, Sunho; Kwon, Hyuk-Chul

We developed Cyber Forensics Ontology for the criminal investigation in cyber space. Cyber crime is classified into cyber terror and general cyber crime, and those two classes are connected with each other. The investigation of cyber terror requires high technology, system environment and experts, and general cyber crime is connected with general crime by evidence from digital data and cyber space. Accordingly, it is difficult to determine relational crime types and collect evidence. Therefore, we considered the classifications of cyber crime, the collection of evidence in cyber space and the application of laws to cyber crime. In order to efficiently investigate cyber crime, it is necessary to integrate those concepts for each cyber crime-case. Thus, we constructed a cyber forensics domain ontology for criminal investigation in cyber space, according to the categories of cyber crime, laws, evidence and information of criminals. This ontology can be used in the process of investigating of cyber crime-cases, and for data mining of cyber crime; classification, clustering, association and detection of crime types, crime cases, evidences and criminals.

Phenex: ontological annotation of phenotypic diversity.

PubMed

Balhoff, James P; Dahdul, Wasila M; Kothari, Cartik R; Lapp, Hilmar; Lundberg, John G; Mabee, Paula; Midford, Peter E; Westerfield, Monte; Vision, Todd J

2010-05-05

Phenotypic differences among species have long been systematically itemized and described by biologists in the process of investigating phylogenetic relationships and trait evolution. Traditionally, these descriptions have been expressed in natural language within the context of individual journal publications or monographs. As such, this rich store of phenotype data has been largely unavailable for statistical and computational comparisons across studies or integration with other biological knowledge. Here we describe Phenex, a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic similarities and differences using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Phenex can be configured to load only those ontologies pertinent to a taxonomic group of interest. The graphical user interface was optimized for evolutionary biologists accustomed to working with lists of taxa, characters, character states, and character-by-taxon matrices. Annotation of phenotypic data using ontologies and globally unique taxonomic identifiers will allow biologists to integrate phenotypic data from different organisms and studies, leveraging decades of work in systematics and comparative morphology.

Semantic Similarity between Web Documents Using Ontology

NASA Astrophysics Data System (ADS)

Chahal, Poonam; Singh Tomer, Manjeet; Kumar, Suresh

2018-06-01

The World Wide Web is the source of information available in the structure of interlinked web pages. However, the procedure of extracting significant information with the assistance of search engine is incredibly critical. This is for the reason that web information is written mainly by using natural language, and further available to individual human. Several efforts have been made in semantic similarity computation between documents using words, concepts and concepts relationship but still the outcome available are not as per the user requirements. This paper proposes a novel technique for computation of semantic similarity between documents that not only takes concepts available in documents but also relationships that are available between the concepts. In our approach documents are being processed by making ontology of the documents using base ontology and a dictionary containing concepts records. Each such record is made up of the probable words which represents a given concept. Finally, document ontology's are compared to find their semantic similarity by taking the relationships among concepts. Relevant concepts and relations between the concepts have been explored by capturing author and user intention. The proposed semantic analysis technique provides improved results as compared to the existing techniques.

Semantic Similarity between Web Documents Using Ontology

NASA Astrophysics Data System (ADS)

Chahal, Poonam; Singh Tomer, Manjeet; Kumar, Suresh

2018-03-01

The World Wide Web is the source of information available in the structure of interlinked web pages. However, the procedure of extracting significant information with the assistance of search engine is incredibly critical. This is for the reason that web information is written mainly by using natural language, and further available to individual human. Several efforts have been made in semantic similarity computation between documents using words, concepts and concepts relationship but still the outcome available are not as per the user requirements. This paper proposes a novel technique for computation of semantic similarity between documents that not only takes concepts available in documents but also relationships that are available between the concepts. In our approach documents are being processed by making ontology of the documents using base ontology and a dictionary containing concepts records. Each such record is made up of the probable words which represents a given concept. Finally, document ontology's are compared to find their semantic similarity by taking the relationships among concepts. Relevant concepts and relations between the concepts have been explored by capturing author and user intention. The proposed semantic analysis technique provides improved results as compared to the existing techniques.

Proteomic analysis of a decellularized human vocal fold mucosa scaffold using 2D electrophoresis and high-resolution mass spectrometry.

PubMed

Welham, Nathan V; Chang, Zhen; Smith, Lloyd M; Frey, Brian L

2013-01-01

Natural biologic scaffolds for tissue engineering are commonly generated by decellularization of tissues and organs. Despite some preclinical and clinical success, in vivo scaffold remodeling and functional outcomes remain variable, presumably due to the influence of unidentified bioactive molecules on the scaffold-host interaction. Here, we used 2D electrophoresis and high-resolution mass spectrometry-based proteomic analyses to evaluate decellularization effectiveness and identify potentially bioactive protein remnants in a human vocal fold mucosa model. We noted proteome, phosphoproteome and O-glycoproteome depletion post-decellularization, and identified >200 unique protein species within the decellularized scaffold. Gene ontology-based enrichment analysis revealed a dominant set of functionally-related ontology terms associated with extracellular matrix assembly, organization, morphology and patterning, consistent with preservation of a tissue-specific niche for later cell seeding and infiltration. We further identified a subset of ontology terms associated with bioactive (some of which are antigenic) cellular proteins, despite histological and immunohistochemical data indicating complete decellularization. These findings demonstrate the value of mass spectrometry-based proteomics in identifying agents potentially responsible for variation in host response to engineered tissues derived from decellularized scaffolds. This work has implications for the manufacturing of biologic scaffolds from any tissue or organ, as well as for prediction and monitoring of the scaffold-host interaction in vivo. Copyright © 2012 Elsevier Ltd. All rights reserved.

PageMan: an interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments.

PubMed

Usadel, Björn; Nagel, Axel; Steinhauser, Dirk; Gibon, Yves; Bläsing, Oliver E; Redestig, Henning; Sreenivasulu, Nese; Krall, Leonard; Hannah, Matthew A; Poree, Fabien; Fernie, Alisdair R; Stitt, Mark

2006-12-18

Microarray technology has become a widely accepted and standardized tool in biology. The first microarray data analysis programs were developed to support pair-wise comparison. However, as microarray experiments have become more routine, large scale experiments have become more common, which investigate multiple time points or sets of mutants or transgenics. To extract biological information from such high-throughput expression data, it is necessary to develop efficient analytical platforms, which combine manually curated gene ontologies with efficient visualization and navigation tools. Currently, most tools focus on a few limited biological aspects, rather than offering a holistic, integrated analysis. Here we introduce PageMan, a multiplatform, user-friendly, and stand-alone software tool that annotates, investigates, and condenses high-throughput microarray data in the context of functional ontologies. It includes a GUI tool to transform different ontologies into a suitable format, enabling the user to compare and choose between different ontologies. It is equipped with several statistical modules for data analysis, including over-representation analysis and Wilcoxon statistical testing. Results are exported in a graphical format for direct use, or for further editing in graphics programs.PageMan provides a fast overview of single treatments, allows genome-level responses to be compared across several microarray experiments covering, for example, stress responses at multiple time points. This aids in searching for trait-specific changes in pathways using mutants or transgenics, analyzing development time-courses, and comparison between species. In a case study, we analyze the results of publicly available microarrays of multiple cold stress experiments using PageMan, and compare the results to a previously published meta-analysis.PageMan offers a complete user's guide, a web-based over-representation analysis as well as a tutorial, and is freely available at http://mapman.mpimp-golm.mpg.de/pageman/. PageMan allows multiple microarray experiments to be efficiently condensed into a single page graphical display. The flexible interface allows data to be quickly and easily visualized, facilitating comparisons within experiments and to published experiments, thus enabling researchers to gain a rapid overview of the biological responses in the experiments.

Federated provenance of oceanographic research cruises: from metadata to data

NASA Astrophysics Data System (ADS)

Thomas, Rob; Leadbetter, Adam; Shepherd, Adam

2016-04-01

The World Wide Web Consortium's Provenance Data Model and associated Semantic Web ontology (PROV-O) have created much interest in the Earth and Space Science Informatics community (Ma et al., 2014). Indeed, PROV-O has recently been posited as an upper ontology for the alignment of various data models (Cox, 2015). Similarly, PROV-O has been used as the building blocks of a data release lifecycle ontology (Leadbetter & Buck, 2015). In this presentation we show that the alignment between different local data descriptions of an oceanographic research cruise can be achieved through alignment with PROV-O and that descriptions of the funding bodies, organisations and researchers involved in a cruise and its associated data release lifecycle can be modelled within a PROV-O based environment. We show that, at a first-order, this approach is scalable by presenting results from three endpoints (the Biological and Chemical Oceanography Data Management Office at Woods Hole Oceanographic Institution, USA; the British Oceanographic Data Centre at the National Oceanography Centre, UK; and the Marine Institute, Ireland). Current advances in ontology engineering, provide pathways to resolving reasoning issues from varying perspectives on implementing PROV-O. This includes the use of the Information Object design pattern where such edge cases as research cruise scheduling efforts are considered. PROV-O describes only things which have happened, but the Information Object design pattern allows for the description of planned research cruises through its statement that the local data description is not the the entity itself (in this case the planned research cruise) and therefore the local data description itself can be described using the PROV-O model. In particular, we present the use of the data lifecycle ontology to show the connection between research cruise activities and their associated datasets, and the publication of those data sets online with Digital Object Identifiers and more formally in data journals. Use of the SPARQL 1.1 standard allows queries to be federated across these endpoints to create a distributed network of provenance documents. Future research directions will add further nodes to the federated network of oceanographic research cruise provenance to determine the true scalability of this approach, and will involve analysis of and possible evolution of the data release lifecycle ontology. References Nitin Arora et al., 2006. Information object design pattern for modeling domain specific knowledge. 1st ECOOP Workshop on Domain-Specific Program Development. Simon Cox, 2015. Pitfalls in alignment of observation models resolved using PROV as an upper ontology. Abstract IN33F-07 presented at the American Geophysical Union Fall Meeting, 14-18 December, San Francisco. Adam Leadbetter & Justin Buck, 2015. Where did my data layer come from?" The semantics of data release. Geophysical Research Abstracts 17, EGU2015-3746-1. Xiaogang Ma et al., 2014. Ontology engineering in provenance enablement for the National Climate Assessment. Environmental Modelling & Software 61, 191-205. http://dx.doi.org/10.1016/j.envsoft.2014.08.002

Building an efficient curation workflow for the Arabidopsis literature corpus

PubMed Central

Li, Donghui; Berardini, Tanya Z.; Muller, Robert J.; Huala, Eva

2012-01-01

TAIR (The Arabidopsis Information Resource) is the model organism database (MOD) for Arabidopsis thaliana, a model plant with a literature corpus of about 39 000 articles in PubMed, with over 4300 new articles added in 2011. We have developed a literature curation workflow incorporating both automated and manual elements to cope with this flood of new research articles. The current workflow can be divided into two phases: article selection and curation. Structured controlled vocabularies, such as the Gene Ontology and Plant Ontology are used to capture free text information in the literature as succinct ontology-based annotations suitable for the application of computational analysis methods. We also describe our curation platform and the use of text mining tools in our workflow. Database URL: www.arabidopsis.org PMID:23221298

Provenance Usage in the OceanLink Project

NASA Astrophysics Data System (ADS)

Narock, T.; Arko, R. A.; Carbotte, S. M.; Chandler, C. L.; Cheatham, M.; Fils, D.; Finin, T.; Hitzler, P.; Janowicz, K.; Jones, M.; Krisnadhi, A.; Lehnert, K. A.; Mickle, A.; Raymond, L. M.; Schildhauer, M.; Shepherd, A.; Wiebe, P. H.

2014-12-01

A wide spectrum of maturing methods and tools, collectively characterized as the Semantic Web, is helping to vastly improve thedissemination of scientific research. The OceanLink project, an NSF EarthCube Building Block, is utilizing semantic technologies tointegrate geoscience data repositories, library holdings, conference abstracts, and funded research awards. Provenance is a vital componentin meeting both the scientific and engineering requirements of OceanLink. Provenance plays a key role in justification and understanding when presenting users with results aggregated from multiple sources. In the engineering sense, provenance enables the identification of new data and the ability to determine which data sources to query. Additionally, OceanLink will leverage human and machine computation for crowdsourcing, text mining, and co-reference resolution. The results of these computations, and their associated provenance, will be folded back into the constituent systems to continually enhance precision and utility. We will touch on the various roles provenance is playing in OceanLink as well as present our use of the PROV Ontology and associated Ontology Design Patterns.

IntegromeDB: an integrated system and biological search engine.

PubMed

Baitaluk, Michael; Kozhenkov, Sergey; Dubinina, Yulia; Ponomarenko, Julia

2012-01-19

With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general-purpose search engines are not specialized for the search of biological data. Here, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene- and protein-centered knowledge. The engine is available at http://www.integromedb.org. The IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and other gene- and protein-related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback.

A Software Engineering Approach based on WebML and BPMN to the Mediation Scenario of the SWS Challenge

NASA Astrophysics Data System (ADS)

Brambilla, Marco; Ceri, Stefano; Valle, Emanuele Della; Facca, Federico M.; Tziviskou, Christina

Although Semantic Web Services are expected to produce a revolution in the development of Web-based systems, very few enterprise-wide design experiences are available; one of the main reasons is the lack of sound Software Engineering methods and tools for the deployment of Semantic Web applications. In this chapter, we present an approach to software development for the Semantic Web based on classical Software Engineering methods (i.e., formal business process development, computer-aided and component-based software design, and automatic code generation) and on semantic methods and tools (i.e., ontology engineering, semantic service annotation and discovery).

Guidelines for managing data and processes in bone and cartilage tissue engineering.

PubMed

Viti, Federica; Scaglione, Silvia; Orro, Alessandro; Milanesi, Luciano

2014-01-01

In the last decades, a wide number of researchers/clinicians involved in tissue engineering field published several works about the possibility to induce a tissue regeneration guided by the use of biomaterials. To this aim, different scaffolds have been proposed, and their effectiveness tested through in vitro and/or in vivo experiments. In this context, integration and meta-analysis approaches are gaining importance for analyses and reuse of data as, for example, those concerning the bone and cartilage biomarkers, the biomolecular factors intervening in cell differentiation and growth, the morphology and the biomechanical performance of a neo-formed tissue, and, in general, the scaffolds' ability to promote tissue regeneration. Therefore standards and ontologies are becoming crucial, to provide a unifying knowledge framework for annotating data and supporting the semantic integration and the unambiguous interpretation of novel experimental results. In this paper a conceptual framework has been designed for bone/cartilage tissue engineering domain, by now completely lacking standardized methods. A set of guidelines has been provided, defining the minimum information set necessary for describing an experimental study involved in bone and cartilage regenerative medicine field. In addition, a Bone/Cartilage Tissue Engineering Ontology (BCTEO) has been developed to provide a representation of the domain's concepts, specifically oriented to cells, and chemical composition, morphology, physical characterization of biomaterials involved in bone/cartilage tissue engineering research. Considering that tissue engineering is a discipline that traverses different semantic fields and employs many data types, the proposed instruments represent a first attempt to standardize the domain knowledge and can provide a suitable means to integrate data across the field.

The agent-based spatial information semantic grid

NASA Astrophysics Data System (ADS)

Cui, Wei; Zhu, YaQiong; Zhou, Yong; Li, Deren

2006-10-01

Analyzing the characteristic of multi-Agent and geographic Ontology, The concept of the Agent-based Spatial Information Semantic Grid (ASISG) is defined and the architecture of the ASISG is advanced. ASISG is composed with Multi-Agents and geographic Ontology. The Multi-Agent Systems are composed with User Agents, General Ontology Agent, Geo-Agents, Broker Agents, Resource Agents, Spatial Data Analysis Agents, Spatial Data Access Agents, Task Execution Agent and Monitor Agent. The architecture of ASISG have three layers, they are the fabric layer, the grid management layer and the application layer. The fabric layer what is composed with Data Access Agent, Resource Agent and Geo-Agent encapsulates the data of spatial information system so that exhibits a conceptual interface for the Grid management layer. The Grid management layer, which is composed with General Ontology Agent, Task Execution Agent and Monitor Agent and Data Analysis Agent, used a hybrid method to manage all resources that were registered in a General Ontology Agent that is described by a General Ontology System. The hybrid method is assembled by resource dissemination and resource discovery. The resource dissemination push resource from Local Ontology Agent to General Ontology Agent and the resource discovery pull resource from the General Ontology Agent to Local Ontology Agents. The Local Ontology Agent is derived from special domain and describes the semantic information of local GIS. The nature of the Local Ontology Agents can be filtrated to construct a virtual organization what could provides a global scheme. The virtual organization lightens the burdens of guests because they need not search information site by site manually. The application layer what is composed with User Agent, Geo-Agent and Task Execution Agent can apply a corresponding interface to a domain user. The functions that ASISG should provide are: 1) It integrates different spatial information systems on the semantic The Grid management layer establishes a virtual environment that integrates seamlessly all GIS notes. 2) When the resource management system searches data on different spatial information systems, it transfers the meaning of different Local Ontology Agents rather than access data directly. So the ability of search and query can be said to be on the semantic level. 3) The data access procedure is transparent to guests, that is, they could access the information from remote site as current disk because the General Ontology Agent could automatically link data by the Data Agents that link the Ontology concept to GIS data. 4) The capability of processing massive spatial data. Storing, accessing and managing massive spatial data from TB to PB; efficiently analyzing and processing spatial data to produce model, information and knowledge; and providing 3D and multimedia visualization services. 5) The capability of high performance computing and processing on spatial information. Solving spatial problems with high precision, high quality, and on a large scale; and process spatial information in real time or on time, with high-speed and high efficiency. 6) The capability of sharing spatial resources. The distributed heterogeneous spatial information resources are Shared and realizing integrated and inter-operated on semantic level, so as to make best use of spatial information resources,such as computing resources, storage devices, spatial data (integrating from GIS, RS and GPS), spatial applications and services, GIS platforms, 7) The capability of integrating legacy GIS system. A ASISG can not only be used to construct new advanced spatial application systems, but also integrate legacy GIS system, so as to keep extensibility and inheritance and guarantee investment of users. 8) The capability of collaboration. Large-scale spatial information applications and services always involve different departments in different geographic places, so remote and uniform services are needed. 9) The capability of supporting integration of heterogeneous systems. Large-scale spatial information systems are always synthetically applications, so ASISG should provide interoperation and consistency through adopting open and applied technology standards. 10) The capability of adapting dynamic changes. Business requirements, application patterns, management strategies, and IT products always change endlessly for any departments, so ASISG should be self-adaptive. Two examples are provided in this paper, those examples provide a detailed way on how you design your semantic grid based on Multi-Agent systems and Ontology. In conclusion, the semantic grid of spatial information system could improve the ability of the integration and interoperability of spatial information grid.

Building and evaluating an ontology-based tool for reasoning about consent permission

PubMed Central

Grando, Adela; Schwab, Richard

2013-01-01

Given the lack of mechanisms for specifying, sharing and checking the compliance of consent permissions, we focus on building and testing novel approaches to address this gap. In our previous work, we introduced a “permission ontology” to capture in a precise, machine-interpretable form informed consent permissions in research studies. Here we explain how we built and evaluated a framework for specifying subject’s permissions and checking researcher’s resource request in compliance with those permissions. The framework is proposed as an extension of an existing policy engine based on the eXtensible Access Control Markup Language (XACML), incorporating ontology-based reasoning. The framework is evaluated in the context of the UCSD Moores Cancer Center biorepository, modeling permissions from an informed consent and a HIPAA form. The resulting permission ontology and mechanisms to check subject’s permission are implementation and institution independent, and therefore offer the potential to be reusable in other biorepositories and data warehouses. PMID:24551354

Multi-source and ontology-based retrieval engine for maize mutant phenotypes

USDA-ARS?s Scientific Manuscript database

In the midst of this genomics era, major plant genome databases are collecting massive amounts of heterogeneous information, including sequence data, gene product information, images of mutant phenotypes, etc., as well as textual descriptions of many of these entities. While basic browsing and sear...

Implementation of Integrated System Fault Management Capability

NASA Technical Reports Server (NTRS)

Figueroa, Fernando; Schmalzel, John; Morris, Jon; Smith, Harvey; Turowski, Mark

2008-01-01

Fault Management to support rocket engine test mission with highly reliable and accurate measurements; while improving availability and lifecycle costs. CORE ELEMENTS: Architecture, taxonomy, and ontology (ATO) for DIaK management. Intelligent Sensor Processes; Intelligent Element Processes; Intelligent Controllers; Intelligent Subsystem Processes; Intelligent System Processes; Intelligent Component Processes.

System Maturity Indices for Decision Support in the Defense Acquisition Process

DTIC Science & Technology

2008-04-23

technologies, but was to be used as ontology for contracting support (Sadin, Povinelli , & Rosen, 1989), thus TRL does not address: A complete...via probabilistic solution discovery. Reliability Engineering & System Safety. In press. Sadin, S.R., Povinelli , F.P., & Rosen, R. (1989). The NASA

Evaluating the Emotion Ontology through use in the self-reporting of emotional responses at an academic conference.

PubMed

Hastings, Janna; Brass, Andy; Caine, Colin; Jay, Caroline; Stevens, Robert

2014-01-01

We evaluate the application of the Emotion Ontology (EM) to the task of self-reporting of emotional experience in the context of audience response to academic presentations at the International Conference on Biomedical Ontology (ICBO). Ontology evaluation is regarded as a difficult task. Types of ontology evaluation range from gauging adherence to some philosophical principles, following some engineering method, to assessing fitness for purpose. The Emotion Ontology (EM) represents emotions and all related affective phenomena, and should enable self-reporting or articulation of emotional states and responses; how do we know if this is the case? Here we use the EM 'in the wild' in order to evaluate the EM's ability to capture people's self-reported emotional responses to a situation through use of the vocabulary provided by the EM. To achieve this evaluation we developed a tool, EmOntoTag, in which audience members were able to capture their self-reported emotional responses to scientific presentations using the vocabulary offered by the EM. We furthermore asked participants using the tool to rate the appropriateness of an EM vocabulary term for capturing their self-assessed emotional response. Participants were also able to suggest improvements to the EM using a free-text feedback facility. Here, we present the data captured and analyse the EM's fitness for purpose in reporting emotional responses to conference talks. Based on our analysis of this data set, our primary finding is that the audience are able to articulate their emotional response to a talk via the EM, and reporting via the EM ontology is able to draw distinctions between the audience's response to a speaker and between the speakers (or talks) themselves. Thus we can conclude that the vocabulary provided at the leaves of the EM are fit for purpose in this setting. We additionally obtained interesting observations from the experiment as a whole, such as that the majority of emotions captured had positive valence, and the free-form feedback supplied new terms for the EM. EmOntoTag can be seen at http://www.bioontology.ch/emontotag; source code can be downloaded from http://emotion-ontology.googlecode.com/svn/trunk/apps/emontotag/and the ontology is available at http://purl.obolibrary.org/obo/MFOEM.owl.

IEA Wind Task 37: Systems Modeling Framework and Ontology for Wind Turbines and Plants

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dykes, Katherine L; Zahle, Frederik; Merz, Karl

This presentation will provide an overview of progress to date in the development of a system modeling framework and ontology for wind turbines and plants as part of the larger IEA Wind Task 37 on wind energy systems engineering. The goals of the effort are to create a set of guidelines for a common conceptual architecture for wind turbines and plants so that practitioners can more easily share descriptions of wind turbines and plants across multiple parties and reduce the effort for translating descriptions between models; integrate different models together and collaborate on model development; and translate models among differentmore » levels of fidelity in the system.« less

Real life identification of partially occluded weapons in video frames

NASA Astrophysics Data System (ADS)

Hempelmann, Christian F.; Arslan, Abdullah N.; Attardo, Salvatore; Blount, Grady P.; Sirakov, Nikolay M.

2016-05-01

We empirically test the capacity of an improved system to identify not just images of individual guns, but partially occluded guns and their parts appearing in a videoframe. This approach combines low-level geometrical information gleaned from the visual images and high-level semantic information stored in an ontology enriched with meronymic part-whole relations. The main improvements of the system are handling occlusion, new algorithms, and an emerging meronomy. Well-known and commonly deployed in ontologies, actual meronomies need to be engineered and populated with unique solutions. Here, this includes adjacency of weapon parts and essentiality of parts to the threat of and the diagnosticity for a weapon. In this study video sequences are processed frame by frame. The extraction method separates colors and removes the background. Then image subtraction of the next frame determines moving targets, before morphological closing is applied to the current frame in order to clean up noise and fill gaps. Next, the method calculates for each object the boundary coordinates and uses them to create a finite numerical sequence as a descriptor. Parts identification is done by cyclic sequence alignment and matching against the nodes of the weapons ontology. From the identified parts, the most-likely weapon will be determined by using the weapon ontology.

A method for exploring implicit concept relatedness in biomedical knowledge network.

PubMed

Bai, Tian; Gong, Leiguang; Wang, Ye; Wang, Yan; Kulikowski, Casimir A; Huang, Lan

2016-07-19

Biomedical information and knowledge, structural and non-structural, stored in different repositories can be semantically connected to form a hybrid knowledge network. How to compute relatedness between concepts and discover valuable but implicit information or knowledge from it effectively and efficiently is of paramount importance for precision medicine, and a major challenge facing the biomedical research community. In this study, a hybrid biomedical knowledge network is constructed by linking concepts across multiple biomedical ontologies as well as non-structural biomedical knowledge sources. To discover implicit relatedness between concepts in ontologies for which potentially valuable relationships (implicit knowledge) may exist, we developed a Multi-Ontology Relatedness Model (MORM) within the knowledge network, for which a relatedness network (RN) is defined and computed across multiple ontologies using a formal inference mechanism of set-theoretic operations. Semantic constraints are designed and implemented to prune the search space of the relatedness network. Experiments to test examples of several biomedical applications have been carried out, and the evaluation of the results showed an encouraging potential of the proposed approach to biomedical knowledge discovery.

Using analytic hierarchy process approach in ontological multicriterial decision making - Preliminary considerations

NASA Astrophysics Data System (ADS)

Wasielewska, K.; Ganzha, M.

2012-10-01

In this paper we consider combining ontologically demarcated information with Saaty's Analytic Hierarchy Process (AHP) [1] for the multicriterial assessment of offers during contract negotiations. The context for the proposal is provided by the Agents in Grid project (AiG; [2]), which aims at development of an agent-based infrastructure for efficient resource management in the Grid. In the AiG project, software agents representing users can either (1) join a team and earn money, or (2) find a team to execute a job. Moreover, agents form teams, managers of which negotiate with clients and workers terms of potential collaboration. Here, ontologically described contracts (Service Level Agreements) are the results of autonomous multiround negotiations. Therefore, taking into account relatively complex nature of the negotiated contracts, multicriterial assessment of proposals plays a crucial role. The AHP method is based on pairwise comparisons of criteria and relies on the judgement of a panel of experts. It measures how well does an offer serve the objective of a decision maker. In this paper, we propose how the AHP method can be used to assess ontologically described contract proposals.

Ontology-based data integration between clinical and research systems.

PubMed

Mate, Sebastian; Köpcke, Felix; Toddenroth, Dennis; Martin, Marcus; Prokosch, Hans-Ulrich; Bürkle, Thomas; Ganslandt, Thomas

2015-01-01

Data from the electronic medical record comprise numerous structured but uncoded elements, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of relevant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it.

Study on the E-commerce platform based on the agent

NASA Astrophysics Data System (ADS)

Fu, Ruixue; Qin, Lishuan; Gao, Yinmin

2011-10-01

To solve problem of dynamic integration in e-commerce, the Multi-Agent architecture of electronic commerce platform system based on Agent and Ontology has been introduced, which includes three major types of agent, Ontology and rule collection. In this architecture, service agent and rule are used to realize the business process reengineering, the reuse of software component, and agility of the electronic commerce platform. To illustrate the architecture, a simulation work has been done and the results imply that the architecture provides a very efficient method to design and implement the flexible, distributed, open and intelligent electronic commerce platform system to solve problem of dynamic integration in ecommerce. The objective of this paper is to illustrate the architecture of electronic commerce platform system, and the approach how Agent and Ontology support the electronic commerce platform system.

Knowledge engineering in volcanology: Practical claims and general approach

NASA Astrophysics Data System (ADS)

Pshenichny, Cyril A.

2014-10-01

Knowledge engineering, being a branch of artificial intelligence, offers a variety of methods for elicitation and structuring of knowledge in a given domain. Only a few of them (ontologies and semantic nets, event/probability trees, Bayesian belief networks and event bushes) are known to volcanologists. Meanwhile, the tasks faced by volcanology and the solutions found so far favor a much wider application of knowledge engineering, especially tools for handling dynamic knowledge. This raises some fundamental logical and mathematical problems and requires an organizational effort, but may strongly improve panel discussions, enhance decision support, optimize physical modeling and support scientific collaboration.

Methodology for the inference of gene function from phenotype data.

PubMed

Ascensao, Joao A; Dolan, Mary E; Hill, David P; Blake, Judith A

2014-12-12

Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data.

The ROOT and STEM of a Fruitful Business Education

ERIC Educational Resources Information Center

Badua, Frank

2015-01-01

The author discusses the role of the liberal arts in a business curriculum for an increasingly science, technology, engineering, and mathematics (STEM)-centered world. The author introduces the rhetoric, orthography, ontology, and teleology (ROOT) disciplines, and links them to the traditional liberal arts foundation of higher education. The…

A web-based rapid assessment tool for production publishing solutions

NASA Astrophysics Data System (ADS)

Sun, Tong

2010-02-01

Solution assessment is a critical first-step in understanding and measuring the business process efficiency enabled by an integrated solution package. However, assessing the effectiveness of any solution is usually a very expensive and timeconsuming task which involves lots of domain knowledge, collecting and understanding the specific customer operational context, defining validation scenarios and estimating the expected performance and operational cost. This paper presents an intelligent web-based tool that can rapidly assess any given solution package for production publishing workflows via a simulation engine and create a report for various estimated performance metrics (e.g. throughput, turnaround time, resource utilization) and operational cost. By integrating the digital publishing workflow ontology and an activity based costing model with a Petri-net based workflow simulation engine, this web-based tool allows users to quickly evaluate any potential digital publishing solutions side-by-side within their desired operational contexts, and provides a low-cost and rapid assessment for organizations before committing any purchase. This tool also benefits the solution providers to shorten the sales cycles, establishing a trustworthy customer relationship and supplement the professional assessment services with a proven quantitative simulation and estimation technology.

Knowledge-Based Environmental Context Modeling

NASA Astrophysics Data System (ADS)

Pukite, P. R.; Challou, D. J.

2017-12-01

As we move from the oil-age to an energy infrastructure based on renewables, the need arises for new educational tools to support the analysis of geophysical phenomena and their behavior and properties. Our objective is to present models of these phenomena to make them amenable for incorporation into more comprehensive analysis contexts. Starting at the level of a college-level computer science course, the intent is to keep the models tractable and therefore practical for student use. Based on research performed via an open-source investigation managed by DARPA and funded by the Department of Interior [1], we have adapted a variety of physics-based environmental models for a computer-science curriculum. The original research described a semantic web architecture based on patterns and logical archetypal building-blocks (see figure) well suited for a comprehensive environmental modeling framework. The patterns span a range of features that cover specific land, atmospheric and aquatic domains intended for engineering modeling within a virtual environment. The modeling engine contained within the server relied on knowledge-based inferencing capable of supporting formal terminology (through NASA JPL's Semantic Web for Earth and Environmental Technology (SWEET) ontology and a domain-specific language) and levels of abstraction via integrated reasoning modules. One of the key goals of the research was to simplify models that were ordinarily computationally intensive to keep them lightweight enough for interactive or virtual environment contexts. The breadth of the elements incorporated is well-suited for learning as the trend toward ontologies and applying semantic information is vital for advancing an open knowledge infrastructure. As examples of modeling, we have covered such geophysics topics as fossil-fuel depletion, wind statistics, tidal analysis, and terrain modeling, among others. Techniques from the world of computer science will be necessary to promote efficient use of our renewable natural resources. [1] C2M2L (Component, Context, and Manufacturing Model Library) Final Report, https://doi.org/10.13140/RG.2.1.4956.3604

IntegromeDB: an integrated system and biological search engine

PubMed Central

2012-01-01

Background With the growth of biological data in volume and heterogeneity, web search engines become key tools for researchers. However, general-purpose search engines are not specialized for the search of biological data. Description Here, we present an approach at developing a biological web search engine based on the Semantic Web technologies and demonstrate its implementation for retrieving gene- and protein-centered knowledge. The engine is available at http://www.integromedb.org. Conclusions The IntegromeDB search engine allows scanning data on gene regulation, gene expression, protein-protein interactions, pathways, metagenomics, mutations, diseases, and other gene- and protein-related data that are automatically retrieved from publicly available databases and web pages using biological ontologies. To perfect the resource design and usability, we welcome and encourage community feedback. PMID:22260095

An ontology-driven, case-based clinical decision support model for removable partial denture design

NASA Astrophysics Data System (ADS)

Chen, Qingxiao; Wu, Ji; Li, Shusen; Lyu, Peijun; Wang, Yong; Li, Miao

2016-06-01

We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient’s oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.

An ontology-driven, case-based clinical decision support model for removable partial denture design.

PubMed

Chen, Qingxiao; Wu, Ji; Li, Shusen; Lyu, Peijun; Wang, Yong; Li, Miao

2016-06-14

We present the initial work toward developing a clinical decision support model for specific design of removable partial dentures (RPDs) in dentistry. We developed an ontological paradigm to represent knowledge of a patient's oral conditions and denture component parts. During the case-based reasoning process, a cosine similarity algorithm was applied to calculate similarity values between input patients and standard ontology cases. A group of designs from the most similar cases were output as the final results. To evaluate this model, the output designs of RPDs for 104 randomly selected patients were compared with those selected by professionals. An area under the curve of the receiver operating characteristic (AUC-ROC) was created by plotting true-positive rates against the false-positive rate at various threshold settings. The precision at position 5 of the retrieved cases was 0.67 and at the top of the curve it was 0.96, both of which are very high. The mean average of precision (MAP) was 0.61 and the normalized discounted cumulative gain (NDCG) was 0.74 both of which confirmed the efficient performance of our model. All the metrics demonstrated the efficiency of our model. This methodology merits further research development to match clinical applications for designing RPDs. This paper is organized as follows. After the introduction and description of the basis for the paper, the evaluation and results are presented in Section 2. Section 3 provides a discussion of the methodology and results. Section 4 describes the details of the ontology, similarity algorithm, and application.

A concept ideation framework for medical device design.

PubMed

Hagedorn, Thomas J; Grosse, Ian R; Krishnamurty, Sundar

2015-06-01

Medical device design is a challenging process, often requiring collaboration between medical and engineering domain experts. This collaboration can be best institutionalized through systematic knowledge transfer between the two domains coupled with effective knowledge management throughout the design innovation process. Toward this goal, we present the development of a semantic framework for medical device design that unifies a large medical ontology with detailed engineering functional models along with the repository of design innovation information contained in the US Patent Database. As part of our development, existing medical, engineering, and patent document ontologies were modified and interlinked to create a comprehensive medical device innovation and design tool with appropriate properties and semantic relations to facilitate knowledge capture, enrich existing knowledge, and enable effective knowledge reuse for different scenarios. The result is a Concept Ideation Framework for Medical Device Design (CIFMeDD). Key features of the resulting framework include function-based searching and automated inter-domain reasoning to uniquely enable identification of functionally similar procedures, tools, and inventions from multiple domains based on simple semantic searches. The significance and usefulness of the resulting framework for aiding in conceptual design and innovation in the medical realm are explored via two case studies examining medical device design problems. Copyright © 2015 Elsevier Inc. All rights reserved.

Semantic Web Data Discovery of Earth Science Data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC)

NASA Technical Reports Server (NTRS)

Hegde, Mahabaleshwara; Strub, Richard F.; Lynnes, Christopher S.; Fang, Hongliang; Teng, William

2008-01-01

Mirador is a web interface for searching Earth Science data archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Mirador provides keyword-based search and guided navigation for providing efficient search and access to Earth Science data. Mirador employs the power of Google's universal search technology for fast metadata keyword searches, augmented by additional capabilities such as event searches (e.g., hurricanes), searches based on location gazetteer, and data services like format converters and data sub-setters. The objective of guided data navigation is to present users with multiple guided navigation in Mirador is an ontology based on the Global Change Master directory (GCMD) Directory Interchange Format (DIF). Current implementation includes the project ontology covering various instruments and model data. Additional capabilities in the pipeline include Earth Science parameter and applications ontologies.

An Ontology-based Architecture for Integration of Clinical Trials Management Applications

PubMed Central

Shankar, Ravi D.; Martins, Susana B.; O’Connor, Martin; Parrish, David B.; Das, Amar K.

2007-01-01

Management of complex clinical trials involves coordinated-use of a myriad of software applications by trial personnel. The applications typically use distinct knowledge representations and generate enormous amount of information during the course of a trial. It becomes vital that the applications exchange trial semantics in order for efficient management of the trials and subsequent analysis of clinical trial data. Existing model-based frameworks do not address the requirements of semantic integration of heterogeneous applications. We have built an ontology-based architecture to support interoperation of clinical trial software applications. Central to our approach is a suite of clinical trial ontologies, which we call Epoch, that define the vocabulary and semantics necessary to represent information on clinical trials. We are continuing to demonstrate and validate our approach with different clinical trials management applications and with growing number of clinical trials. PMID:18693919

Describing and recognizing patterns of events in smart environments with description logic.

PubMed

Scalmato, Antonello; Sgorbissa, Antonio; Zaccaria, Renato

2013-12-01

This paper describes a system for context awareness in smart environments, which is based on an ontology expressed in description logic and implemented in OWL 2 EL, which is a subset of the Web Ontology Language that allows for reasoning in polynomial time. The approach is different from all other works in the literature since the proposed system requires only the basic reasoning mechanisms of description logic, i.e., subsumption and instance checking, without any additional external reasoning engine. Experiments performed with data collected in three different scenarios are described, i.e., the CASAS Project at Washington State University, the assisted living facility Villa Basilea in Genoa, and the Merry Porter mobile robot at the Polyclinic of Modena.

Adding question answering to an e-tutor for programming languages

NASA Astrophysics Data System (ADS)

Taylor, Kate; Moore, Simon

Control over a closed domain of textual material removes many question answering issues, as does an ontology that is closely intertwined with its sources. This pragmatic, shallow approach to many challenging areas of research in adaptive hypermedia, question answering, intelligent tutoring and humancomputer interaction has been put into practice at Cambridge in the Computer Science undergraduate course to teach the hardware description language Veri/og. This language itself poses many challenges as it crosses the interdisciplinary boundary between hardware and software engineers, giving rise to severalhuman ontologies as well as theprogramming language itself We present further results from ourformal and informal surveys. We look at further work to increase the dialogue between studentand tutor and export our knowledge to the Semantic Web.

An Ontology and a Software Framework for Competency Modeling and Management

ERIC Educational Resources Information Center

Paquette, Gilbert

2007-01-01

The importance given to competency management is well justified. Acquiring new competencies is the central goal of any education or knowledge management process. Thus, it must be embedded in any software framework as an instructional engineering tool, to inform the runtime environment of the knowledge that is processed by actors, and their…

An Ontology Engineering Approach to the Realization of Theory-Driven Group Formation

ERIC Educational Resources Information Center

Isotani, Seiji; Inaba, Akiko; Ikeda, Mitsuru; Mizoguchi, Riichiro

2009-01-01

One of the main difficulties during the design of collaborative learning activities is adequate group formation. In any type of collaboration, group formation plays a critical role in the learners' acceptance of group activities, as well as the success of the collaborative learning process. Nevertheless, to propose both an effective and…

A semantic medical multimedia retrieval approach using ontology information hiding.

PubMed

Guo, Kehua; Zhang, Shigeng

2013-01-01

Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users' query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches.

From Lexical Regularities to Axiomatic Patterns for the Quality Assurance of Biomedical Terminologies and Ontologies.

PubMed

van Damme, Philip; Quesada-Martínez, Manuel; Cornet, Ronald; Fernández-Breis, Jesualdo Tomás

2018-06-13

Ontologies and terminologies have been identified as key resources for the achievement of semantic interoperability in biomedical domains. The development of ontologies is performed as a joint work by domain experts and knowledge engineers. The maintenance and auditing of these resources is also the responsibility of such experts, and this is usually a time-consuming, mostly manual task. Manual auditing is impractical and ineffective for most biomedical ontologies, especially for larger ones. An example is SNOMED CT, a key resource in many countries for codifying medical information. SNOMED CT contains more than 300000 concepts. Consequently its auditing requires the support of automatic methods. Many biomedical ontologies contain natural language content for humans and logical axioms for machines. The 'lexically suggest, logically define' principle means that there should be a relation between what is expressed in natural language and as logical axioms, and that such a relation should be useful for auditing and quality assurance. Besides, the meaning of this principle is that the natural language content for humans could be used to generate the logical axioms for the machines. In this work, we propose a method that combines lexical analysis and clustering techniques to (1) identify regularities in the natural language content of ontologies; (2) cluster, by similarity, labels exhibiting a regularity; (3) extract relevant information from those clusters; and (4) propose logical axioms for each cluster with the support of axiom templates. These logical axioms can then be evaluated with the existing axioms in the ontology to check their correctness and completeness, which are two fundamental objectives in auditing and quality assurance. In this paper, we describe the application of the method to two SNOMED CT modules, a 'congenital' module, obtained using concepts exhibiting the attribute Occurrence - Congenital, and a 'chronic' module, using concepts exhibiting the attribute Clinical course - Chronic. We obtained a precision and a recall of respectively 75% and 28% for the 'congenital' module, and 64% and 40% for the 'chronic' one. We consider these results to be promising, so our method can contribute to the support of content editors by using automatic methods for assuring the quality of biomedical ontologies and terminologies. Copyright © 2018. Published by Elsevier Inc.

Muscle Logic: New Knowledge Resource for Anatomy Enables Comprehensive Searches of the Literature on the Feeding Muscles of Mammals

PubMed Central

Druzinsky, Robert E.; Balhoff, James P.; Crompton, Alfred W.; Done, James; German, Rebecca Z.; Haendel, Melissa A.; Herrel, Anthony; Herring, Susan W.; Lapp, Hilmar; Mabee, Paula M.; Muller, Hans-Michael; Mungall, Christopher J.; Sternberg, Paul W.; Van Auken, Kimberly; Vinyard, Christopher J.; Williams, Susan H.; Wall, Christine E.

2016-01-01

Background In recent years large bibliographic databases have made much of the published literature of biology available for searches. However, the capabilities of the search engines integrated into these databases for text-based bibliographic searches are limited. To enable searches that deliver the results expected by comparative anatomists, an underlying logical structure known as an ontology is required. Development and Testing of the Ontology Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repository for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). We compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. Results and Significance Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities. PMID:26870952

Automated compound classification using a chemical ontology.

PubMed

Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

2012-12-29

Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated.

Automated compound classification using a chemical ontology

PubMed Central

2012-01-01

Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated. PMID:23273256

Community Based Informatics: Geographical Information Systems, Remote Sensing and Ontology collaboration - A technical hands-on approach

NASA Astrophysics Data System (ADS)

Branch, B. D.; Raskin, R. G.; Rock, B.; Gagnon, M.; Lecompte, M. A.; Hayden, L. B.

2009-12-01

With the nation challenged to comply with Executive Order 12906 and its needs to augment the Science, Technology, Engineering and Mathematics (STEM) pipeline, applied focus on geosciences pipelines issue may be at risk. The Geosciences pipeline may require intentional K-12 standard course of study consideration in the form of project based, science based and evidenced based learning. Thus, the K-12 to geosciences to informatics pipeline may benefit from an earth science experience that utilizes a community based “learning by doing” approach. Terms such as Community GIS, Community Remotes Sensing, and Community Based Ontology development are termed Community Informatics. Here, approaches of interdisciplinary work to promote and earth science literacy are affordable, consisting of low cost equipment that renders GIS/remote sensing data processing skills necessary in the workforce. Hence, informal community ontology development may evolve or mature from a local community towards formal scientific community collaboration. Such consideration may become a means to engage educational policy towards earth science paradigms and needs, specifically linking synergy among Math, Computer Science, and Earth Science disciplines.

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

Understanding and using the data and knowledge encoded in semantic web documents requires an inference engine. F-OWL is an inference engine for the semantic web language OWL language based on F-logic, an approach to defining frame-based systems in logic. F-OWL is implemented using XSB and Flora-2 and takes full advantage of their features. We describe how F-OWL computes ontology entailment and compare it with other description logic based approaches. We also describe TAGA, a trading agent environment that we have used as a test bed for F-OWL and to explore how multiagent systems can use semantic web concepts and technology.

Muscle Logic: New Knowledge Resource for Anatomy Enables Comprehensive Searches of the Literature on the Feeding Muscles of Mammals.

PubMed

Druzinsky, Robert E; Balhoff, James P; Crompton, Alfred W; Done, James; German, Rebecca Z; Haendel, Melissa A; Herrel, Anthony; Herring, Susan W; Lapp, Hilmar; Mabee, Paula M; Muller, Hans-Michael; Mungall, Christopher J; Sternberg, Paul W; Van Auken, Kimberly; Vinyard, Christopher J; Williams, Susan H; Wall, Christine E

2016-01-01

In recent years large bibliographic databases have made much of the published literature of biology available for searches. However, the capabilities of the search engines integrated into these databases for text-based bibliographic searches are limited. To enable searches that deliver the results expected by comparative anatomists, an underlying logical structure known as an ontology is required. Here we present the Mammalian Feeding Muscle Ontology (MFMO), a multi-species ontology focused on anatomical structures that participate in feeding and other oral/pharyngeal behaviors. A unique feature of the MFMO is that a simple, computable, definition of each muscle, which includes its attachments and innervation, is true across mammals. This construction mirrors the logical foundation of comparative anatomy and permits searches using language familiar to biologists. Further, it provides a template for muscles that will be useful in extending any anatomy ontology. The MFMO is developed to support the Feeding Experiments End-User Database Project (FEED, https://feedexp.org/), a publicly-available, online repository for physiological data collected from in vivo studies of feeding (e.g., mastication, biting, swallowing) in mammals. Currently the MFMO is integrated into FEED and also into two literature-specific implementations of Textpresso, a text-mining system that facilitates powerful searches of a corpus of scientific publications. We evaluate the MFMO by asking questions that test the ability of the ontology to return appropriate answers (competency questions). We compare the results of queries of the MFMO to results from similar searches in PubMed and Google Scholar. Our tests demonstrate that the MFMO is competent to answer queries formed in the common language of comparative anatomy, but PubMed and Google Scholar are not. Overall, our results show that by incorporating anatomical ontologies into searches, an expanded and anatomically comprehensive set of results can be obtained. The broader scientific and publishing communities should consider taking up the challenge of semantically enabled search capabilities.

Ontology-based approach for in vivo human connectomics: the medial Brodmann area 6 case study

PubMed Central

Moreau, Tristan; Gibaud, Bernard

2015-01-01

Different non-invasive neuroimaging modalities and multi-level analysis of human connectomics datasets yield a great amount of heterogeneous data which are hard to integrate into an unified representation. Biomedical ontologies can provide a suitable integrative framework for domain knowledge as well as a tool to facilitate information retrieval, data sharing and data comparisons across scales, modalities and species. Especially, it is urgently needed to fill the gap between neurobiology and in vivo human connectomics in order to better take into account the reality highlighted in Magnetic Resonance Imaging (MRI) and relate it to existing brain knowledge. The aim of this study was to create a neuroanatomical ontology, called “Human Connectomics Ontology” (HCO), in order to represent macroscopic gray matter regions connected with fiber bundles assessed by diffusion tractography and to annotate MRI connectomics datasets acquired in the living human brain. First a neuroanatomical “view” called NEURO-DL-FMA was extracted from the reference ontology Foundational Model of Anatomy (FMA) in order to construct a gross anatomy ontology of the brain. HCO extends NEURO-DL-FMA by introducing entities (such as “MR_Node” and “MR_Route”) and object properties (such as “tracto_connects”) pertaining to MR connectivity. The Web Ontology Language Description Logics (OWL DL) formalism was used in order to enable reasoning with common reasoning engines. Moreover, an experimental work was achieved in order to demonstrate how the HCO could be effectively used to address complex queries concerning in vivo MRI connectomics datasets. Indeed, neuroimaging datasets of five healthy subjects were annotated with terms of the HCO and a multi-level analysis of the connectivity patterns assessed by diffusion tractography of the right medial Brodmann Area 6 was achieved using a set of queries. This approach can facilitate comparison of data across scales, modalities and species. PMID:25914640

Design of a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oriented clustering case-based reasoning mechanism.

PubMed

Ku, Hao-Hsiang

2015-01-01

Nowadays, people can easily use a smartphone to get wanted information and requested services. Hence, this study designs and proposes a Golf Swing Injury Detection and Evaluation open service platform with Ontology-oritened clustering case-based reasoning mechanism, which is called GoSIDE, based on Arduino and Open Service Gateway initative (OSGi). GoSIDE is a three-tier architecture, which is composed of Mobile Users, Application Servers and a Cloud-based Digital Convergence Server. A mobile user is with a smartphone and Kinect sensors to detect the user's Golf swing actions and to interact with iDTV. An application server is with Intelligent Golf Swing Posture Analysis Model (iGoSPAM) to check a user's Golf swing actions and to alter this user when he is with error actions. Cloud-based Digital Convergence Server is with Ontology-oriented Clustering Case-based Reasoning (CBR) for Quality of Experiences (OCC4QoE), which is designed to provide QoE services by QoE-based Ontology strategies, rules and events for this user. Furthermore, GoSIDE will automatically trigger OCC4QoE and deliver popular rules for a new user. Experiment results illustrate that GoSIDE can provide appropriate detections for Golfers. Finally, GoSIDE can be a reference model for researchers and engineers.

Ontology-Based Data Integration between Clinical and Research Systems

PubMed Central

Mate, Sebastian; Köpcke, Felix; Toddenroth, Dennis; Martin, Marcus; Prokosch, Hans-Ulrich

2015-01-01

Data from the electronic medical record comprise numerous structured but uncoded ele-ments, which are not linked to standard terminologies. Reuse of such data for secondary research purposes has gained in importance recently. However, the identification of rele-vant data elements and the creation of database jobs for extraction, transformation and loading (ETL) are challenging: With current methods such as data warehousing, it is not feasible to efficiently maintain and reuse semantically complex data extraction and trans-formation routines. We present an ontology-supported approach to overcome this challenge by making use of abstraction: Instead of defining ETL procedures at the database level, we use ontologies to organize and describe the medical concepts of both the source system and the target system. Instead of using unique, specifically developed SQL statements or ETL jobs, we define declarative transformation rules within ontologies and illustrate how these constructs can then be used to automatically generate SQL code to perform the desired ETL procedures. This demonstrates how a suitable level of abstraction may not only aid the interpretation of clinical data, but can also foster the reutilization of methods for un-locking it. PMID:25588043

mz5: space- and time-efficient storage of mass spectrometry data sets.

PubMed

Wilhelm, Mathias; Kirchner, Marc; Steen, Judith A J; Steen, Hanno

2012-01-01

Across a host of MS-driven-omics fields, researchers witness the acquisition of ever increasing amounts of high throughput MS data and face the need for their compact yet efficiently accessible storage. Addressing the need for an open data exchange format, the Proteomics Standards Initiative and the Seattle Proteome Center at the Institute for Systems Biology independently developed the mzData and mzXML formats, respectively. In a subsequent joint effort, they defined an ontology and associated controlled vocabulary that specifies the contents of MS data files, implemented as the newer mzML format. All three formats are based on XML and are thus not particularly efficient in either storage space requirements or read/write speed. This contribution introduces mz5, a complete reimplementation of the mzML ontology that is based on the efficient, industrial strength storage backend HDF5. Compared with the current mzML standard, this strategy yields an average file size reduction to ∼54% and increases linear read and write speeds ∼3-4-fold. The format is implemented as part of the ProteoWizard project and is available under a permissive Apache license. Additional information and download links are available from http://software.steenlab.org/mz5.

Insight: An ontology-based integrated database and analysis platform for epilepsy self-management research.

PubMed

Sahoo, Satya S; Ramesh, Priya; Welter, Elisabeth; Bukach, Ashley; Valdez, Joshua; Tatsuoka, Curtis; Bamps, Yvan; Stoll, Shelley; Jobst, Barbara C; Sajatovic, Martha

2016-10-01

We present Insight as an integrated database and analysis platform for epilepsy self-management research as part of the national Managing Epilepsy Well Network. Insight is the only available informatics platform for accessing and analyzing integrated data from multiple epilepsy self-management research studies with several new data management features and user-friendly functionalities. The features of Insight include, (1) use of Common Data Elements defined by members of the research community and an epilepsy domain ontology for data integration and querying, (2) visualization tools to support real time exploration of data distribution across research studies, and (3) an interactive visual query interface for provenance-enabled research cohort identification. The Insight platform contains data from five completed epilepsy self-management research studies covering various categories of data, including depression, quality of life, seizure frequency, and socioeconomic information. The data represents over 400 participants with 7552 data points. The Insight data exploration and cohort identification query interface has been developed using Ruby on Rails Web technology and open source Web Ontology Language Application Programming Interface to support ontology-based reasoning. We have developed an efficient ontology management module that automatically updates the ontology mappings each time a new version of the Epilepsy and Seizure Ontology is released. The Insight platform features a Role-based Access Control module to authenticate and effectively manage user access to different research studies. User access to Insight is managed by the Managing Epilepsy Well Network database steering committee consisting of representatives of all current collaborating centers of the Managing Epilepsy Well Network. New research studies are being continuously added to the Insight database and the size as well as the unique coverage of the dataset allows investigators to conduct aggregate data analysis that will inform the next generation of epilepsy self-management studies. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Ontology-based literature mining and class effect analysis of adverse drug reactions associated with neuropathy-inducing drugs.

PubMed

Hur, Junguk; Özgür, Arzucan; He, Yongqun

2018-06-07

Adverse drug reactions (ADRs), also called as drug adverse events (AEs), are reported in the FDA drug labels; however, it is a big challenge to properly retrieve and analyze the ADRs and their potential relationships from textual data. Previously, we identified and ontologically modeled over 240 drugs that can induce peripheral neuropathy through mining public drug-related databases and drug labels. However, the ADR mechanisms of these drugs are still unclear. In this study, we aimed to develop an ontology-based literature mining system to identify ADRs from drug labels and to elucidate potential mechanisms of the neuropathy-inducing drugs (NIDs). We developed and applied an ontology-based SciMiner literature mining strategy to mine ADRs from the drug labels provided in the Text Analysis Conference (TAC) 2017, which included drug labels for 53 neuropathy-inducing drugs (NIDs). We identified an average of 243 ADRs per NID and constructed an ADR-ADR network, which consists of 29 ADR nodes and 149 edges, including only those ADR-ADR pairs found in at least 50% of NIDs. Comparison to the ADR-ADR network of non-NIDs revealed that the ADRs such as pruritus, pyrexia, thrombocytopenia, nervousness, asthenia, acute lymphocytic leukaemia were highly enriched in the NID network. Our ChEBI-based ontology analysis identified three benzimidazole NIDs (i.e., lansoprazole, omeprazole, and pantoprazole), which were associated with 43 ADRs. Based on ontology-based drug class effect definition, the benzimidazole drug group has a drug class effect on all of these 43 ADRs. Many of these 43 ADRs also exist in the enriched NID ADR network. Our Ontology of Adverse Events (OAE) classification further found that these 43 benzimidazole-related ADRs were distributed in many systems, primarily in behavioral and neurological, digestive, skin, and immune systems. Our study demonstrates that ontology-based literature mining and network analysis can efficiently identify and study specific group of drugs and their associated ADRs. Furthermore, our analysis of drug class effects identified 3 benzimidazole drugs sharing 43 ADRs, leading to new hypothesis generation and possible mechanism understanding of drug-induced peripheral neuropathy.

Data mart construction based on semantic annotation of scientific articles: A case study for the prioritization of drug targets.

PubMed

Teixeira, Marlon Amaro Coelho; Belloze, Kele Teixeira; Cavalcanti, Maria Cláudia; Silva-Junior, Floriano P

2018-04-01

Semantic text annotation enables the association of semantic information (ontology concepts) to text expressions (terms), which are readable by software agents. In the scientific scenario, this is particularly useful because it reveals a lot of scientific discoveries that are hidden within academic articles. The Biomedical area has more than 300 ontologies, most of them composed of over 500 concepts. These ontologies can be used to annotate scientific papers and thus, facilitate data extraction. However, in the context of a scientific research, a simple keyword-based query using the interface of a digital scientific texts library can return more than a thousand hits. The analysis of such a large set of texts, annotated with such numerous and large ontologies, is not an easy task. Therefore, the main objective of this work is to provide a method that could facilitate this task. This work describes a method called Text and Ontology ETL (TOETL), to build an analytical view over such texts. First, a corpus of selected papers is semantically annotated using distinct ontologies. Then, the annotation data is extracted, organized and aggregated into the dimensional schema of a data mart. Besides the TOETL method, this work illustrates its application through the development of the TaP DM (Target Prioritization data mart). This data mart has focus on the research of gene essentiality, a key concept to be considered when searching for genes showing potential as anti-infective drug targets. This work reveals that the proposed approach is a relevant tool to support decision making in the prioritization of new drug targets, being more efficient than the keyword-based traditional tools. Copyright © 2018 Elsevier B.V. All rights reserved.

Development and application of an interaction network ontology for literature mining of vaccine-associated gene-gene interactions.

PubMed

Hur, Junguk; Özgür, Arzucan; Xiang, Zuoshuang; He, Yongqun

2015-01-01

Literature mining of gene-gene interactions has been enhanced by ontology-based name classifications. However, in biomedical literature mining, interaction keywords have not been carefully studied and used beyond a collection of keywords. In this study, we report the development of a new Interaction Network Ontology (INO) that classifies >800 interaction keywords and incorporates interaction terms from the PSI Molecular Interactions (PSI-MI) and Gene Ontology (GO). Using INO-based literature mining results, a modified Fisher's exact test was established to analyze significantly over- and under-represented enriched gene-gene interaction types within a specific area. Such a strategy was applied to study the vaccine-mediated gene-gene interactions using all PubMed abstracts. The Vaccine Ontology (VO) and INO were used to support the retrieval of vaccine terms and interaction keywords from the literature. INO is aligned with the Basic Formal Ontology (BFO) and imports terms from 10 other existing ontologies. Current INO includes 540 terms. In terms of interaction-related terms, INO imports and aligns PSI-MI and GO interaction terms and includes over 100 newly generated ontology terms with 'INO_' prefix. A new annotation property, 'has literature mining keywords', was generated to allow the listing of different keywords mapping to the interaction types in INO. Using all PubMed documents published as of 12/31/2013, approximately 266,000 vaccine-associated documents were identified, and a total of 6,116 gene-pairs were associated with at least one INO term. Out of 78 INO interaction terms associated with at least five gene-pairs of the vaccine-associated sub-network, 14 terms were significantly over-represented (i.e., more frequently used) and 17 under-represented based on our modified Fisher's exact test. These over-represented and under-represented terms share some common top-level terms but are distinct at the bottom levels of the INO hierarchy. The analysis of these interaction types and their associated gene-gene pairs uncovered many scientific insights. INO provides a novel approach for defining hierarchical interaction types and related keywords for literature mining. The ontology-based literature mining, in combination with an INO-based statistical interaction enrichment test, provides a new platform for efficient mining and analysis of topic-specific gene interaction networks.

The Orbital Space Environment and Space Situational Awareness Domain Ontology - Toward an International Information System for Space Data

NASA Astrophysics Data System (ADS)

Rovetto, R.

2016-09-01

The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding our knowledge of near-Earth space. To facilitate data-sharing interoperability among distinct orbital debris and space object catalogs, and SSA information systems, I proposed ontology in (Rovetto, 2015) and (Rovetto and Kelso, 2016). I continue this effort toward formal representations and models of the overall domain that may serve to improve peaceful SSA and increase our scientific knowledge. This paper explains the project concept introduced in those publications, summarizing efforts to date as well as the research field of ontology development and engineering. I describe concepts for an ontological framework for the orbital space environment, near-Earth space environment and SSA domain. An ontological framework is conceived as a part of a potential international information system. The purpose of such a system is to consolidate, analyze and reason over various sources and types of orbital and SSA data toward the mutually beneficial goals of safer space navigation and scientific research. Recent internationals findings on the limitations of orbital data, in addition to existing publications on collaborative SSA, demonstrate both the overlap with this project and the need for datasharing and integration.

On2broker: Semantic-Based Access to Information Sources at the WWW.

ERIC Educational Resources Information Center

Fensel, Dieter; Angele, Jurgen; Decker, Stefan; Erdmann, Michael; Schnurr, Hans-Peter; Staab, Steffen; Studer, Rudi; Witt, Andreas

On2broker provides brokering services to improve access to heterogeneous, distributed, and semistructured information sources as they are presented in the World Wide Web. It relies on the use of ontologies to make explicit the semantics of Web pages. This paper discusses the general architecture and main components (i.e., query engine, information…

Research on geo-ontology construction based on spatial affairs

NASA Astrophysics Data System (ADS)

Li, Bin; Liu, Jiping; Shi, Lihong

2008-12-01

Geo-ontology, a kind of domain ontology, is used to make the knowledge, information and data of concerned geographical science in the abstract to form a series of single object or entity with common cognition. These single object or entity can compose a specific system in some certain way and can be disposed on conception and given specific definition at the same time. Ultimately, these above-mentioned worked results can be expressed in some manners of formalization. The main aim of constructing geo-ontology is to get the knowledge of the domain of geography, and provide the commonly approbatory vocabularies in the domain, as well as give the definite definition about these geographical vocabularies and mutual relations between them in the mode of formalization at different hiberarchy. Consequently, the modeling tool of conception model of describing geographic Information System at the hiberarchy of semantic meaning and knowledge can be provided to solve the semantic conception of information exchange in geographical space and make them possess the comparatively possible characters of accuracy, maturity and universality, etc. In fact, some experiments have been made to validate geo-ontology. During the course of studying, Geo-ontology oriented to flood can be described and constructed by making the method based on geo-spatial affairs to serve the governmental departments at all levels to deal with flood. Thereinto, intelligent retrieve and service based on geoontology of disaster are main functions known from the traditional manner by using keywords. For instance, the function of dealing with disaster information based on geo-ontology can be provided when a supposed flood happened in a certain city. The correlative officers can input some words, such as "city name, flood", which have been realized semantic label, to get the information they needed when they browse different websites. The information, including basic geographical information and flood distributing and change about flood with different scales and ranges in the city, can be distilled intellectively and on its own initiative from the geo-ontology database. Besides, correlative statistical information can also be provided to the governmental departments at all levels to help them to carry out timely measures of fighting back disaster and rescue. Compared with the past manners, the efficiency of dealing with flood information has been improved to some extent than ever because plenty of information irrespective and interferential to flood in different websites can be sieved in advance based on the retrieve method oriented to Geo-ontology. In a word, it will take the pursuers long time to study geo-ontology due to actual limited resource. But then, geo-ontology will be sure to further perfect correspondingly especially in the field of Geographic Information System owing to its more and more factual applications.

Unhappy with internal corporate search? : learn tips and tricks for building a controlled vocabulary ontology.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Arpin, Bettina Karin Schimanski; Jones, Brian S.; Bemesderfer, Joy

2010-06-01

Are your employees unhappy with internal corporate search? Frequent complaints include: too many results to sift through; results are unrelated/outdated; employees aren't sure which terms to search for. One way to improve intranet search is to implement a controlled vocabulary ontology. Employing this takes the guess work out of searching, makes search efficient and precise, educates employees about the lingo used within the corporation, and allows employees to contribute to the corpus of terms. It promotes internal corporate search to rival its superior sibling, internet search. We will cover our experiences, lessons learned, and conclusions from implementing a controlled vocabularymore » ontology at Sandia National Laboratories. The work focuses on construction of this ontology from the content perspective and the technical perspective. We'll discuss the following: (1) The tool we used to build a polyhierarchical taxonomy; (2) Examples of two methods of indexing the content: traditional 'back of the book' and folksonomy word-mapping; (3) Tips on how to build future search capabilities while building the basic controlled vocabulary; (4) How to implement the controlled vocabulary as an ontology that mimics Google's search suggestions; (5) Making the user experience more interactive and intuitive; and (6) Sorting suggestions based on preferred, alternate and related terms using SPARQL queries. In summary, future improvements will be presented, including permitting end-users to add, edit and remove terms, and filtering on different subject domains.« less

Context-Based Tourism Information Filtering with a Semantic Rule Engine

PubMed Central

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction. PMID:22778584

Context-based tourism information filtering with a semantic rule engine.

PubMed

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction.

A Semantic Medical Multimedia Retrieval Approach Using Ontology Information Hiding

PubMed Central

Guo, Kehua; Zhang, Shigeng

2013-01-01

Searching useful information from unstructured medical multimedia data has been a difficult problem in information retrieval. This paper reports an effective semantic medical multimedia retrieval approach which can reflect the users' query intent. Firstly, semantic annotations will be given to the multimedia documents in the medical multimedia database. Secondly, the ontology that represented semantic information will be hidden in the head of the multimedia documents. The main innovations of this approach are cross-type retrieval support and semantic information preservation. Experimental results indicate a good precision and efficiency of our approach for medical multimedia retrieval in comparison with some traditional approaches. PMID:24082915

Waste Management Using Request-Based Virtual Organizations

NASA Astrophysics Data System (ADS)

Katriou, Stamatia Ann; Fragidis, Garyfallos; Ignatiadis, Ioannis; Tolias, Evangelos; Koumpis, Adamantios

Waste management is on top of the political agenda globally as a high priority environmental issue, with billions spent on it each year. This paper proposes an approach for the disposal, transportation, recycling and reuse of waste. This approach incorporates the notion of Request Based Virtual Organizations (RBVOs) using a Service Oriented Architecture (SOA) and an ontology that serves the definition of waste management requirements. The populated ontology is utilized by a Multi-Agent System which performs negotiations and forms RBVOs. The proposed approach could be used by governments and companies searching for a means to perform such activities in an effective and efficient manner.

I.M. Sechenov (1829 - 1905) and the scientific self-understanding for medical sciences.

PubMed

Kofler, Walter

2007-01-01

There is no discussion about the historic relevance of I. Sechenov for physiology and neurosciences as the "father of Russian modern physiology". But he is relevant for modern natural science too because of his basic epistemological and ontological work. He did not accept the up to now basic paradigm of "Ignorabimus" which can be seen as the reason to exclude even the generalizable aspects of individuality, creativity and spontaneity from natural science. He developed techniques for empirical based science to deal with materialistic and idealistic aspects of the comprehensive person the "ignoramus" according to the actual stay of knowledge and the acceptable ontologies. He demonstrated that ontologies ("paradigms") can be used as tools according to the given problem which should be solved. So Sechenov can be seen as a precursor of the so efficient philosophical positions of Einstein and Th. Kuhn. The stay of the art in physiology and neurosciences changed since the time of Sechenov dramatically. Therefore the philosophical positions of the 19th century should be discussed. Maybe this is indispensable for the needed linkage between materialistic and idealistic aspects of a person. For this the proposals of Sechenov are helpful up to now but nearly unknown. There is no discussion about the historic relevance of I. Sechenov as the "father of Russian physiology." But he is relevant for modern natural science too because of his epistemological and ontological work. He did not accept the up to now basic paradigm of "Ignorabimus" that can be seen as the reason to exclude even the generalizable aspects of individuality, creativity, and spontaneity from natural science. He demonstrated that ontologies ("paradigms") and epistemology can be used as tools according to the given problem. So Sechenov can be seen as a precursor of the so efficient philosophical positions of Einstein and Th. Kuhn. The state of the art changed dramatically. Therefore, the philosophical positions of the nineteenth century should be questioned. Maybe this is indispensable for the needed link between materialistic and idealistic aspects of a person as a whole. In this respect the proposals of Sechenov are helpful for medical science in the twenty-first century too but nearly unknown.

An MDA Based Ontology Platform: AIR

NASA Astrophysics Data System (ADS)

Gaševic, Dragan; Djuric, Dragan; Devedžic, Vladan

In the past few years, software engineering has witnessed two major shifts: model-driven engineering has entered the mainstream, and some leading development tools have become open and extensible.1 AI has always been a spring of new ideas that have been adopted in software engineering, but most of its gems have stayed buried in laboratories, available only to a limited number of AI practitioners. Should AI tools be integrated into mainstream tools and could it be done? We think that it is feasible, and that both communities can benefit from this integration. In fact, some efforts in this direction have already been made, both by major industrial standardization bodies such as the OMG, and by academic laboratories.

Designing and visualizing the water-energy-food nexus system

NASA Astrophysics Data System (ADS)

Endo, A.; Kumazawa, T.; Yamada, M.; Kato, T.

2017-12-01

The objective of this study is to design and visualize a water-energy-food nexus system to identify the interrelationships between water-energy-food (WEF) resources and to understand the subsequent complexity of WEF nexus systems holistically, taking an interdisciplinary approach. Object-oriented concepts and ontology engineering methods were applied according to the hypothesis that the chains of changes in linkages between water, energy, and food resources holistically affect the water-energy-food nexus system, including natural and social systems, both temporally and spatially. The water-energy-food nexus system that is developed is significant because it allows us to: 1) visualize linkages between water, energy, and food resources in social and natural systems; 2) identify tradeoffs between these resources; 3) find a way of using resources efficiently or enhancing the synergy between the utilization of different resources; and 4) aid scenario planning using economic tools. The paper also discusses future challenges for applying the developed water-energy-food nexus system in other areas.

An ontology-driven clinical decision support system (IDDAP) for infectious disease diagnosis and antibiotic prescription.

PubMed

Shen, Ying; Yuan, Kaiqi; Chen, Daoyuan; Colloc, Joël; Yang, Min; Li, Yaliang; Lei, Kai

2018-03-01

The available antibiotic decision-making systems were developed from a physician's perspective. However, because infectious diseases are common, many patients desire access to knowledge via a search engine. Although the use of antibiotics should, in principle, be subject to a doctor's advice, many patients take them without authorization, and some people cannot easily or rapidly consult a doctor. In such cases, a reliable antibiotic prescription support system is needed. This study describes the construction and optimization of the sensitivity and specificity of a decision support system named IDDAP, which is based on ontologies for infectious disease diagnosis and antibiotic therapy. The ontology for this system was constructed by collecting existing ontologies associated with infectious diseases, syndromes, bacteria and drugs into the ontology's hierarchical conceptual schema. First, IDDAP identifies a potential infectious disease based on a patient's self-described disease state. Then, the system searches for and proposes an appropriate antibiotic therapy specifically adapted to the patient based on factors such as the patient's body temperature, infection sites, symptoms/signs, complications, antibacterial spectrum, contraindications, drug-drug interactions between the proposed therapy and previously prescribed medication, and the route of therapy administration. The constructed domain ontology contains 1,267,004 classes, 7,608,725 axioms, and 1,266,993 members of "SubClassOf" that pertain to infectious diseases, bacteria, syndromes, anti-bacterial drugs and other relevant components. The system includes 507 infectious diseases and their therapy methods in combination with 332 different infection sites, 936 relevant symptoms of the digestive, reproductive, neurological and other systems, 371 types of complications, 838,407 types of bacteria, 341 types of antibiotics, 1504 pairs of reaction rates (antibacterial spectrum) between antibiotics and bacteria, 431 pairs of drug interaction relationships and 86 pairs of antibiotic-specific population contraindicated relationships. Compared with the existing infectious disease-relevant ontologies in the field of knowledge comprehension, this ontology is more complete. Analysis of IDDAP's performance in terms of classifiers based on receiver operating characteristic (ROC) curve results (89.91%) revealed IDDAP's advantages when combined with our ontology. This study attempted to bridge the patient/caregiver gap by building a sophisticated application that uses artificial intelligence and machine learning computational techniques to perform data-driven decision-making at the point of primary care. The first level of decision-making is conducted by the IDDAP and provides the patient with a first-line therapy. Patients can then make a subjective judgment, and if any questions arise, should consult a physician for subsequent decisions, particularly in complicated cases or in cases in which the necessary information is not yet available in the knowledge base. Copyright © 2018 Elsevier B.V. All rights reserved.

ODISEES: A New Paradigm in Data Access

NASA Astrophysics Data System (ADS)

Huffer, E.; Little, M. M.; Kusterer, J.

2013-12-01

As part of its ongoing efforts to improve access to data, the Atmospheric Science Data Center has developed a high-precision Earth Science domain ontology (the 'ES Ontology') implemented in a graph database ('the Semantic Metadata Repository') that is used to store detailed, semantically-enhanced, parameter-level metadata for ASDC data products. The ES Ontology provides the semantic infrastructure needed to drive the ASDC's Ontology-Driven Interactive Search Environment for Earth Science ('ODISEES'), a data discovery and access tool, and will support additional data services such as analytics and visualization. The ES ontology is designed on the premise that naming conventions alone are not adequate to provide the information needed by prospective data consumers to assess the suitability of a given dataset for their research requirements; nor are current metadata conventions adequate to support seamless machine-to-machine interactions between file servers and end-user applications. Data consumers need information not only about what two data elements have in common, but also about how they are different. End-user applications need consistent, detailed metadata to support real-time data interoperability. The ES ontology is a highly precise, bottom-up, queriable model of the Earth Science domain that focuses on critical details about the measurable phenomena, instrument techniques, data processing methods, and data file structures. Earth Science parameters are described in detail in the ES Ontology and mapped to the corresponding variables that occur in ASDC datasets. Variables are in turn mapped to well-annotated representations of the datasets that they occur in, the instrument(s) used to create them, the instrument platforms, the processing methods, etc., creating a linked-data structure that allows both human and machine users to access a wealth of information critical to understanding and manipulating the data. The mappings are recorded in the Semantic Metadata Repository as RDF-triples. An off-the-shelf Ontology Development Environment and a custom Metadata Conversion Tool comprise a human-machine/machine-machine hybrid tool that partially automates the creation of metadata as RDF-triples by interfacing with existing metadata repositories and providing a user interface that solicits input from a human user, when needed. RDF-triples are pushed to the Ontology Development Environment, where a reasoning engine executes a series of inference rules whose antecedent conditions can be satisfied by the initial set of RDF-triples, thereby generating the additional detailed metadata that is missing in existing repositories. A SPARQL Endpoint, a web-based query service and a Graphical User Interface allow prospective data consumers - even those with no familiarity with NASA data products - to search the metadata repository to find and order data products that meet their exact specifications. A web-based API will provide an interface for machine-to-machine transactions.

FROG - Fingerprinting Genomic Variation Ontology

PubMed Central

Bhardwaj, Anshu

2015-01-01

Genetic variations play a crucial role in differential phenotypic outcomes. Given the complexity in establishing this correlation and the enormous data available today, it is imperative to design machine-readable, efficient methods to store, label, search and analyze this data. A semantic approach, FROG: “FingeRprinting Ontology of Genomic variations” is implemented to label variation data, based on its location, function and interactions. FROG has six levels to describe the variation annotation, namely, chromosome, DNA, RNA, protein, variations and interactions. Each level is a conceptual aggregation of logically connected attributes each of which comprises of various properties for the variant. For example, in chromosome level, one of the attributes is location of variation and which has two properties, allosomes or autosomes. Another attribute is variation kind which has four properties, namely, indel, deletion, insertion, substitution. Likewise, there are 48 attributes and 278 properties to capture the variation annotation across six levels. Each property is then assigned a bit score which in turn leads to generation of a binary fingerprint based on the combination of these properties (mostly taken from existing variation ontologies). FROG is a novel and unique method designed for the purpose of labeling the entire variation data generated till date for efficient storage, search and analysis. A web-based platform is designed as a test case for users to navigate sample datasets and generate fingerprints. The platform is available at http://ab-openlab.csir.res.in/frog. PMID:26244889

Finding My Needle in the Haystack: Effective Personalized Re-ranking of Search Results in Prospector

NASA Astrophysics Data System (ADS)

König, Florian; van Velsen, Lex; Paramythis, Alexandros

This paper provides an overview of Prospector, a personalized Internet meta-search engine, which utilizes a combination of ontological information, ratings-based models of user interests, and complementary theme-oriented group models to recommend (through re-ranking) search results obtained from an underlying search engine. Re-ranking brings “closer to the top” those items that are of particular interest to a user or have high relevance to a given theme. A user-based, real-world evaluation has shown that the system is effective in promoting results of interest, but lags behind Google in user acceptance, possibly due to the absence of features popularized by said search engine. Overall, users would consider employing a personalized search engine to perform searches with terms that require disambiguation and / or contextualization.

SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services

PubMed Central

Gessler, Damian DG; Schiltz, Gary S; May, Greg D; Avraham, Shulamit; Town, Christopher D; Grant, David; Nelson, Rex T

2009-01-01

Background SSWAP (Simple Semantic Web Architecture and Protocol; pronounced "swap") is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies. Results There are currently over 2400 resources published in SSWAP. Approximately two dozen are custom-written services for QTL (Quantitative Trait Loci) and mapping data for legumes and grasses (grains). The remaining are wrappers to Nucleic Acids Research Database and Web Server entries. As an architecture, SSWAP establishes how clients (users of data, services, and ontologies), providers (suppliers of data, services, and ontologies), and discovery servers (semantic search engines) interact to allow for the description, querying, discovery, invocation, and response of semantic web services. As a protocol, SSWAP provides the vocabulary and semantics to allow clients, providers, and discovery servers to engage in semantic web services. The protocol is based on the W3C-sanctioned first-order description logic language OWL DL. As an open source platform, a discovery server running at (as in to "swap info") uses the description logic reasoner Pellet to integrate semantic resources. The platform hosts an interactive guide to the protocol at , developer tools at , and a portal to third-party ontologies at (a "swap meet"). Conclusion SSWAP addresses the three basic requirements of a semantic web services architecture (i.e., a common syntax, shared semantic, and semantic discovery) while addressing three technology limitations common in distributed service systems: i.e., i) the fatal mutability of traditional interfaces, ii) the rigidity and fragility of static subsumption hierarchies, and iii) the confounding of content, structure, and presentation. SSWAP is novel by establishing the concept of a canonical yet mutable OWL DL graph that allows data and service providers to describe their resources, to allow discovery servers to offer semantically rich search engines, to allow clients to discover and invoke those resources, and to allow providers to respond with semantically tagged data. SSWAP allows for a mix-and-match of terms from both new and legacy third-party ontologies in these graphs. PMID:19775460

A future Outlook: Web based Simulation of Hydrodynamic models

NASA Astrophysics Data System (ADS)

Islam, A. S.; Piasecki, M.

2003-12-01

Despite recent advances to present simulation results as 3D graphs or animation contours, the modeling user community still faces some shortcomings when trying to move around and analyze data. Typical problems include the lack of common platforms with standard vocabulary to exchange simulation results from different numerical models, insufficient descriptions about data (metadata), lack of robust search and retrieval tools for data, and difficulties to reuse simulation domain knowledge. This research demonstrates how to create a shared simulation domain in the WWW and run a number of models through multi-user interfaces. Firstly, meta-datasets have been developed to describe hydrodynamic model data based on geographic metadata standard (ISO 19115) that has been extended to satisfy the need of the hydrodynamic modeling community. The Extended Markup Language (XML) is used to publish this metadata by the Resource Description Framework (RDF). Specific domain ontology for Web Based Simulation (WBS) has been developed to explicitly define vocabulary for the knowledge based simulation system. Subsequently, this knowledge based system is converted into an object model using Meta Object Family (MOF). The knowledge based system acts as a Meta model for the object oriented system, which aids in reusing the domain knowledge. Specific simulation software has been developed based on the object oriented model. Finally, all model data is stored in an object relational database. Database back-ends help store, retrieve and query information efficiently. This research uses open source software and technology such as Java Servlet and JSP, Apache web server, Tomcat Servlet Engine, PostgresSQL databases, Protégé ontology editor, RDQL and RQL for querying RDF in semantic level, Jena Java API for RDF. Also, we use international standards such as the ISO 19115 metadata standard, and specifications such as XML, RDF, OWL, XMI, and UML. The final web based simulation product is deployed as Web Archive (WAR) files which is platform and OS independent and can be used by Windows, UNIX, or Linux. Keywords: Apache, ISO 19115, Java Servlet, Jena, JSP, Metadata, MOF, Linux, Ontology, OWL, PostgresSQL, Protégé, RDF, RDQL, RQL, Tomcat, UML, UNIX, Windows, WAR, XML

Efficient Results in Semantic Interoperability for Health Care. Findings from the Section on Knowledge Representation and Management.

PubMed

Soualmia, L F; Charlet, J

2016-11-10

To summarize excellent current research in the field of Knowledge Representation and Management (KRM) within the health and medical care domain. We provide a synopsis of the 2016 IMIA selected articles as well as a related synthetic overview of the current and future field activities. A first step of the selection was performed through MEDLINE querying with a list of MeSH descriptors completed by a list of terms adapted to the KRM section. The second step of the selection was completed by the two section editors who separately evaluated the set of 1,432 articles. The third step of the selection consisted of a collective work that merged the evaluation results to retain 15 articles for peer-review. The selection and evaluation process of this Yearbook's section on Knowledge Representation and Management has yielded four excellent and interesting articles regarding semantic interoperability for health care by gathering heterogeneous sources (knowledge and data) and auditing ontologies. In the first article, the authors present a solution based on standards and Semantic Web technologies to access distributed and heterogeneous datasets in the domain of breast cancer clinical trials. The second article describes a knowledge-based recommendation system that relies on ontologies and Semantic Web rules in the context of chronic diseases dietary. The third article is related to concept-recognition and text-mining to derive common human diseases model and a phenotypic network of common diseases. In the fourth article, the authors highlight the need for auditing the SNOMED CT. They propose to use a crowdbased method for ontology engineering. The current research activities further illustrate the continuous convergence of Knowledge Representation and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care by proposing solutions to cope with the problem of semantic interoperability. Indeed, there is a need for powerful tools able to manage and interpret complex, large-scale and distributed datasets and knowledge bases, but also a need for user-friendly tools developed for the clinicians in their daily practice.

Text-Content-Analysis based on the Syntactic Correlations between Ontologies

NASA Astrophysics Data System (ADS)

Tenschert, Axel; Kotsiopoulos, Ioannis; Koller, Bastian

The work presented in this chapter is concerned with the analysis of semantic knowledge structures, represented in the form of Ontologies, through which Service Level Agreements (SLAs) are enriched with new semantic data. The objective of the enrichment process is to enable SLA negotiation in a way that is much more convenient for a Service Users. For this purpose the deployment of an SLA-Management-System as well as the development of an analyzing procedure for Ontologies is required. This chapter will refer to the BREIN, the FinGrid and the LarKC projects. The analyzing procedure examines the syntactic correlations of several Ontologies whose focus lies in the field of mechanical engineering. A method of analyzing text and content is developed as part of this procedure. In order to so, we introduce a formalism as well as a method for understanding content. The analysis and methods are integrated to an SLA Management System which enables a Service User to interact with the system as a service by negotiating the user requests and including the semantic knowledge. Through negotiation between Service User and Service Provider the analysis procedure considers the user requests by extending the SLAs with semantic knowledge. Through this the economic use of an SLA-Management-System is increased by the enhancement of SLAs with semantic knowledge structures. The main focus of this chapter is the analyzing procedure, respectively the Text-Content-Analysis, which provides the mentioned semantic knowledge structures.

The Biomedical Ethics Ontology Proposal: Excellent Aims, Questionable Methods

PubMed Central

DuBois, James M.

2010-01-01

Koepsell et al. (2009) Describe an ideal biomedical ethics committee environment with efficiencies such as electronic and universal application forms and consent templates, automated decision-trees, and broad sharing of data. However, it is unclear that a biomedical ethics ontology (BMEO) is necessary or even helpful in establishing such environment. Two features of any applied ontology are particularly problematic in establishing a useful BMEO: (1) an ontology is a description of a domain of reality; and (2) the description is subject to ongoing revision as it is developed through open processes, e.g., the use of a wiki. A BMEO would need to address two main kinds of entities, regulatory definitions and ethical concepts, and is ill-suited to both. Regulatory definitions are fiats and ought to be adopted verbatim to ensure compliance, but in such cases we do not need the assistance of ontologists, and their modes of working (constant revision within open wiki-based communities) might even be counterproductive. Ethical concepts within pluralistic societies are social constructs, not a priori concepts or biological natural kinds, and the prospects of generating intuitive definitions that enjoy broad acceptance across cultures and institutional settings are slim. In making these arguments, I draw from the writings of leading applied ontologists and Koepsell et al.’s own proof of concept. PMID:19382878

An Ontology-Based Archive Information Model for the Planetary Science Community

NASA Technical Reports Server (NTRS)

Hughes, J. Steven; Crichton, Daniel J.; Mattmann, Chris

2008-01-01

The Planetary Data System (PDS) information model is a mature but complex model that has been used to capture over 30 years of planetary science data for the PDS archive. As the de-facto information model for the planetary science data archive, it is being adopted by the International Planetary Data Alliance (IPDA) as their archive data standard. However, after seventeen years of evolutionary change the model needs refinement. First a formal specification is needed to explicitly capture the model in a commonly accepted data engineering notation. Second, the core and essential elements of the model need to be identified to help simplify the overall archive process. A team of PDS technical staff members have captured the PDS information model in an ontology modeling tool. Using the resulting knowledge-base, work continues to identify the core elements, identify problems and issues, and then test proposed modifications to the model. The final deliverables of this work will include specifications for the next generation PDS information model and the initial set of IPDA archive data standards. Having the information model captured in an ontology modeling tool also makes the model suitable for use by Semantic Web applications.

Contingency and the order of nature.

PubMed

Cartwright, Nancy

2016-08-01

Many profess faith in the universal rule of deterministic law. I urge remaining agnostic, putting into nature only what we need to account for what we know to be the case: order where, and to the extent that, we see it. Powers and mechanisms can do that job. Embracing contingency and deriving order from powers and mechanisms reduces three kinds of problems: ontological, theological, and epistemological. Ontologically, there is no puzzle about why models from various branches of natural and social science, daily life, and engineering serve us in good stead if all that's happening is physics laws playing themselves out. Also, when universal laws are replaced with a power/mechanism ontology, nothing is set irredeemably by the Big Bang or at some hyper-surface in space-time. What happens can depend on how we arrange things to exploit the powers of their parts. That may be put to significant theological advantage. The epistemological problem comes from philosopher of physics, Erhard Scheibe. Given what we take physics to teach about the universality of interaction, there is just one very large object - the entire universe - to be governed by laws of nature. How then do we ever learn those laws? Copyright © 2015 Elsevier Ltd. All rights reserved.

Improvements to the Ontology-based Metadata Portal for Unified Semantics (OlyMPUS)

NASA Astrophysics Data System (ADS)

Linsinbigler, M. A.; Gleason, J. L.; Huffer, E.

2016-12-01

The Ontology-based Metadata Portal for Unified Semantics (OlyMPUS), funded by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is an end-to-end system designed to support Earth Science data consumers and data providers, enabling the latter to register data sets and provision them with the semantically rich metadata that drives the Ontology-Driven Interactive Search Environment for Earth Sciences (ODISEES). OlyMPUS complements the ODISEES' data discovery system with an intelligent tool to enable data producers to auto-generate semantically enhanced metadata and upload it to the metadata repository that drives ODISEES. Like ODISEES, the OlyMPUS metadata provisioning tool leverages robust semantics, a NoSQL database and query engine, an automated reasoning engine that performs first- and second-order deductive inferencing, and uses a controlled vocabulary to support data interoperability and automated analytics. The ODISEES data discovery portal leverages this metadata to provide a seamless data discovery and access experience for data consumers who are interested in comparing and contrasting the multiple Earth science data products available across NASA data centers. Olympus will support scientists' services and tools for performing complex analyses and identifying correlations and non-obvious relationships across all types of Earth System phenomena using the full spectrum of NASA Earth Science data available. By providing an intelligent discovery portal that supplies users - both human users and machines - with detailed information about data products, their contents and their structure, ODISEES will reduce the level of effort required to identify and prepare large volumes of data for analysis. This poster will explain how OlyMPUS leverages deductive reasoning and other technologies to create an integrated environment for generating and exploiting semantically rich metadata.

mz5: Space- and Time-efficient Storage of Mass Spectrometry Data Sets*

PubMed Central

Wilhelm, Mathias; Kirchner, Marc; Steen, Judith A. J.; Steen, Hanno

2012-01-01

Across a host of MS-driven-omics fields, researchers witness the acquisition of ever increasing amounts of high throughput MS data and face the need for their compact yet efficiently accessible storage. Addressing the need for an open data exchange format, the Proteomics Standards Initiative and the Seattle Proteome Center at the Institute for Systems Biology independently developed the mzData and mzXML formats, respectively. In a subsequent joint effort, they defined an ontology and associated controlled vocabulary that specifies the contents of MS data files, implemented as the newer mzML format. All three formats are based on XML and are thus not particularly efficient in either storage space requirements or read/write speed. This contribution introduces mz5, a complete reimplementation of the mzML ontology that is based on the efficient, industrial strength storage backend HDF5. Compared with the current mzML standard, this strategy yields an average file size reduction to ∼54% and increases linear read and write speeds ∼3–4-fold. The format is implemented as part of the ProteoWizard project and is available under a permissive Apache license. Additional information and download links are available from http://software.steenlab.org/mz5. PMID:21960719

Method of transition from 3D model to its ontological representation in aircraft design process

NASA Astrophysics Data System (ADS)

Govorkov, A. S.; Zhilyaev, A. S.; Fokin, I. V.

2018-05-01

This paper proposes the method of transition from a 3D model to its ontological representation and describes its usage in the aircraft design process. The problems of design for manufacturability and design automation are also discussed. The introduced method is to aim to ease the process of data exchange between important aircraft design phases, namely engineering and design control. The method is also intended to increase design speed and 3D model customizability. This requires careful selection of the complex systems (CAD / CAM / CAE / PDM), providing the basis for the integration of design and technological preparation of production and more fully take into account the characteristics of products and processes for their manufacture. It is important to solve this problem, as investment in the automation define the company's competitiveness in the years ahead.

Constructing Adverse Outcome Pathways: a Demonstration of an Ontology-based Semantics Mapping Approach

EPA Science Inventory

Adverse outcome pathway (AOP) provides a conceptual framework to evaluate and integrate chemical toxicity and its effects across the levels of biological organization. As such, it is essential to develop a resource-efficient and effective approach to extend molecular initiating ...

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains

PubMed Central

2014-01-01

The application of semantic technologies to the integration of biological data and the interoperability of bioinformatics analysis and visualization tools has been the common theme of a series of annual BioHackathons hosted in Japan for the past five years. Here we provide a review of the activities and outcomes from the BioHackathons held in 2011 in Kyoto and 2012 in Toyama. In order to efficiently implement semantic technologies in the life sciences, participants formed various sub-groups and worked on the following topics: Resource Description Framework (RDF) models for specific domains, text mining of the literature, ontology development, essential metadata for biological databases, platforms to enable efficient Semantic Web technology development and interoperability, and the development of applications for Semantic Web data. In this review, we briefly introduce the themes covered by these sub-groups. The observations made, conclusions drawn, and software development projects that emerged from these activities are discussed. PMID:24495517

Ontology-Based Gap Analysis for Technology Selection: A Knowledge Management Framework for the Support of Equipment Purchasing Processes

NASA Astrophysics Data System (ADS)

Macris, Aristomenis M.; Georgakellos, Dimitrios A.

Technology selection decisions such as equipment purchasing and supplier selection are decisions of strategic importance to companies. The nature of these decisions usually is complex, unstructured and thus, difficult to be captured in a way that will be efficiently reusable. Knowledge reusability is of paramount importance since it enables users participate actively in process design/redesign activities stimulated by the changing technology selection environment. This paper addresses the technology selection problem through an ontology-based approach that captures and makes reusable the equipment purchasing process and assists in identifying (a) the specifications requested by the users' organization, (b) those offered by various candidate vendors' organizations and (c) in performing specifications gap analysis as a prerequisite for effective and efficient technology selection. This approach has practical appeal, operational simplicity, and the potential for both immediate and long-term strategic impact. An example from the iron and steel industry is also presented to illustrate the approach.

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

NASA Astrophysics Data System (ADS)

Gao, Wei; Zhu, Linli; Wang, Kaiyun

2015-12-01

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

Deep Question Answering for protein annotation

PubMed Central

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/ PMID:26384372

Deep Question Answering for protein annotation.

PubMed

Gobeill, Julien; Gaudinat, Arnaud; Pasche, Emilie; Vishnyakova, Dina; Gaudet, Pascale; Bairoch, Amos; Ruch, Patrick

2015-01-01

Biomedical professionals have access to a huge amount of literature, but when they use a search engine, they often have to deal with too many documents to efficiently find the appropriate information in a reasonable time. In this perspective, question-answering (QA) engines are designed to display answers, which were automatically extracted from the retrieved documents. Standard QA engines in literature process a user question, then retrieve relevant documents and finally extract some possible answers out of these documents using various named-entity recognition processes. In our study, we try to answer complex genomics questions, which can be adequately answered only using Gene Ontology (GO) concepts. Such complex answers cannot be found using state-of-the-art dictionary- and redundancy-based QA engines. We compare the effectiveness of two dictionary-based classifiers for extracting correct GO answers from a large set of 100 retrieved abstracts per question. In the same way, we also investigate the power of GOCat, a GO supervised classifier. GOCat exploits the GOA database to propose GO concepts that were annotated by curators for similar abstracts. This approach is called deep QA, as it adds an original classification step, and exploits curated biological data to infer answers, which are not explicitly mentioned in the retrieved documents. We show that for complex answers such as protein functional descriptions, the redundancy phenomenon has a limited effect. Similarly usual dictionary-based approaches are relatively ineffective. In contrast, we demonstrate how existing curated data, beyond information extraction, can be exploited by a supervised classifier, such as GOCat, to massively improve both the quantity and the quality of the answers with a +100% improvement for both recall and precision. Database URL: http://eagl.unige.ch/DeepQA4PA/. © The Author(s) 2015. Published by Oxford University Press.

Toward an Ontological Approach in Goal-Oriented Language Courseware Design and Its Implications for Technology-Independent Content Structuring

ERIC Educational Resources Information Center

Colpaert, Jozef

2006-01-01

The term "design" is being understood more and more as a methodological process, together with its acceptance as the result of such a process. As a process, it is a stage in the courseware engineering life cycle which primarily focuses on rendering the development process more effective and on enhancing the qualities of the finished system,…

Building a Community Memory in Communities of Practice of E-Learning: A Knowledge Engineering Approach

ERIC Educational Resources Information Center

Sarirete, Akila; Chikh, Azeddine; Noble, Elizabeth

2011-01-01

Purpose: The purpose of this paper is to define a community memory for a virtual communities of practice (CoP) based on organizational learning (OL) concept and ontologies. Design/methodology/approach: The paper focuses on applying the OL concept to virtual CoP and proposes a framework for building the CoP memory by identifying several layers of…

Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews.

PubMed

Ji, Xiaonan; Ritter, Alan; Yen, Po-Yin

2017-05-01

Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner. Here we propose to enhance article relationships with background semantic knowledge derived from Unified Medical Language System (UMLS) concepts and ontologies. We developed a pipelined semantic concepts representation process to represent articles from an SR into an optimized and enriched semantic space of UMLS concepts. Throughout the process, we leveraged concepts and concept relations encoded in biomedical ontologies (SNOMED-CT and MeSH) within the UMLS framework to prompt concept features of each article. Article relationships (similarities) were established and represented as a semantic article network, which was readily applied to assist with the article screening process. We incorporated the concept of active learning to simulate an interactive article recommendation process, and evaluated the performance on 15 completed SRs. We used work saved over sampling at 95% recall (WSS95) as the performance measure. We compared the WSS95 performance of our ontology-based semantic approach to existing lexical feature approaches and corpus-based semantic approaches, and found that we had better WSS95 in most SRs. We also had the highest average WSS95 of 43.81% and the highest total WSS95 of 657.18%. We demonstrated using ontology-based semantics to facilitate the identification of relevant articles for SRs. Effective concepts and concept relations derived from UMLS ontologies can be utilized to establish article semantic relationships. Our approach provided a promising performance and can easily apply to any SR topics in the biomedical domain with generalizability. Copyright © 2017 Elsevier Inc. All rights reserved.

Computable visually observed phenotype ontological framework for plants

PubMed Central

2011-01-01

Background The ability to search for and precisely compare similar phenotypic appearances within and across species has vast potential in plant science and genetic research. The difficulty in doing so lies in the fact that many visual phenotypic data, especially visually observed phenotypes that often times cannot be directly measured quantitatively, are in the form of text annotations, and these descriptions are plagued by semantic ambiguity, heterogeneity, and low granularity. Though several bio-ontologies have been developed to standardize phenotypic (and genotypic) information and permit comparisons across species, these semantic issues persist and prevent precise analysis and retrieval of information. A framework suitable for the modeling and analysis of precise computable representations of such phenotypic appearances is needed. Results We have developed a new framework called the Computable Visually Observed Phenotype Ontological Framework for plants. This work provides a novel quantitative view of descriptions of plant phenotypes that leverages existing bio-ontologies and utilizes a computational approach to capture and represent domain knowledge in a machine-interpretable form. This is accomplished by means of a robust and accurate semantic mapping module that automatically maps high-level semantics to low-level measurements computed from phenotype imagery. The framework was applied to two different plant species with semantic rules mined and an ontology constructed. Rule quality was evaluated and showed high quality rules for most semantics. This framework also facilitates automatic annotation of phenotype images and can be adopted by different plant communities to aid in their research. Conclusions The Computable Visually Observed Phenotype Ontological Framework for plants has been developed for more efficient and accurate management of visually observed phenotypes, which play a significant role in plant genomics research. The uniqueness of this framework is its ability to bridge the knowledge of informaticians and plant science researchers by translating descriptions of visually observed phenotypes into standardized, machine-understandable representations, thus enabling the development of advanced information retrieval and phenotype annotation analysis tools for the plant science community. PMID:21702966

Data Integration and Mining for Synthetic Biology Design.

PubMed

Mısırlı, Göksel; Hallinan, Jennifer; Pocock, Matthew; Lord, Phillip; McLaughlin, James Alastair; Sauro, Herbert; Wipat, Anil

2016-10-21

One aim of synthetic biologists is to create novel and predictable biological systems from simpler modular parts. This approach is currently hampered by a lack of well-defined and characterized parts and devices. However, there is a wealth of existing biological information, which can be used to identify and characterize biological parts, and their design constraints in the literature and numerous biological databases. However, this information is spread among these databases in many different formats. New computational approaches are required to make this information available in an integrated format that is more amenable to data mining. A tried and tested approach to this problem is to map disparate data sources into a single data set, with common syntax and semantics, to produce a data warehouse or knowledge base. Ontologies have been used extensively in the life sciences, providing this common syntax and semantics as a model for a given biological domain, in a fashion that is amenable to computational analysis and reasoning. Here, we present an ontology for applications in synthetic biology design, SyBiOnt, which facilitates the modeling of information about biological parts and their relationships. SyBiOnt was used to create the SyBiOntKB knowledge base, incorporating and building upon existing life sciences ontologies and standards. The reasoning capabilities of ontologies were then applied to automate the mining of biological parts from this knowledge base. We propose that this approach will be useful to speed up synthetic biology design and ultimately help facilitate the automation of the biological engineering life cycle.

From Classification to Epilepsy Ontology and Informatics

PubMed Central

Zhang, Guo-Qiang; Sahoo, Satya S; Lhatoo, Samden D

2012-01-01

Summary The 2010 International League Against Epilepsy (ILAE) classification and terminology commission report proposed a much needed departure from previous classifications to incorporate advances in molecular biology, neuroimaging, and genetics. It proposed an interim classification and defined two key requirements that need to be satisfied. The first is the ability to classify epilepsy in dimensions according to a variety of purposes including clinical research, patient care, and drug discovery. The second is the ability of the classification system to evolve with new discoveries. Multi-dimensionality and flexibility are crucial to the success of any future classification. In addition, a successful classification system must play a central role in the rapidly growing field of epilepsy informatics. An epilepsy ontology, based on classification, will allow information systems to facilitate data-intensive studies and provide a proven route to meeting the two foregoing key requirements. Epilepsy ontology will be a structured terminology system that accommodates proposed and evolving ILAE classifications, the NIH/NINDS Common Data Elements, the ICD systems and explicitly specifies all known relationships between epilepsy concepts in a proper framework. This will aid evidence based epilepsy diagnosis, investigation, treatment and research for a diverse community of clinicians and researchers. Benefits range from systematization of electronic patient records to multi-modal data repositories for research and training manuals for those involved in epilepsy care. Given the complexity, heterogeneity and pace of research advances in the epilepsy domain, such an ontology must be collaboratively developed by key stakeholders in the epilepsy community and experts in knowledge engineering and computer science. PMID:22765502

Information Models, Data Requirements, and Agile Data Curation

NASA Astrophysics Data System (ADS)

Hughes, John S.; Crichton, Dan; Ritschel, Bernd; Hardman, Sean; Joyner, Ron

2015-04-01

The Planetary Data System's next generation system, PDS4, is an example of the successful use of an ontology-based Information Model (IM) to drive the development and operations of a data system. In traditional systems engineering, requirements or statements about what is necessary for the system are collected and analyzed for input into the design stage of systems development. With the advent of big data the requirements associated with data have begun to dominate and an ontology-based information model can be used to provide a formalized and rigorous set of data requirements. These requirements address not only the usual issues of data quantity, quality, and disposition but also data representation, integrity, provenance, context, and semantics. In addition the use of these data requirements during system's development has many characteristics of Agile Curation as proposed by Young et al. [Taking Another Look at the Data Management Life Cycle: Deconstruction, Agile, and Community, AGU 2014], namely adaptive planning, evolutionary development, early delivery, continuous improvement, and rapid and flexible response to change. For example customers can be satisfied through early and continuous delivery of system software and services that are configured directly from the information model. This presentation will describe the PDS4 architecture and its three principle parts: the ontology-based Information Model (IM), the federated registries and repositories, and the REST-based service layer for search, retrieval, and distribution. The development of the IM will be highlighted with special emphasis on knowledge acquisition, the impact of the IM on development and operations, and the use of shared ontologies at multiple governance levels to promote system interoperability and data correlation.

ATOS-1: Designing the infrastructure for an advanced spacecraft operations system

NASA Technical Reports Server (NTRS)

Poulter, K. J.; Smith, H. N.

1993-01-01

The space industry has identified the need to use artificial intelligence and knowledge based system techniques as integrated, central, symbolic processing components of future mission design, support and operations systems. Various practical and commercial constraints require that off-the-shelf applications, and their knowledge bases, are reused where appropriate and that different mission contractors, potentially using different KBS technologies, can provide application and knowledge sub-modules of an overall integrated system. In order to achieve this integration, which we call knowledge sharing and distributed reasoning, there needs to be agreement on knowledge representations, knowledge interchange-formats, knowledge level communications protocols, and ontology. Research indicates that the latter is most important, providing the applications with a common conceptualization of the domain, in our case spacecraft operations, mission design, and planning. Agreement on ontology permits applications that employ different knowledge representations to interwork through mediators which we refer to as knowledge agents. This creates the illusion of a shared model without the constraints, both technical and commercial, that occur in centralized or uniform architectures. This paper explains how these matters are being addressed within the ATOS program at ESOC, using techniques which draw upon ideas and standards emerging from the DARPA Knowledge Sharing Effort. In particular, we explain how the project is developing an electronic Ontology of Spacecraft Operations and how this can be used as an enabling component within space support systems that employ advanced software engineering. We indicate our hope and expectation that the core ontology developed in ATOS, will permit the full development of standards for such systems throughout the space industry.

LapOntoSPM: an ontology for laparoscopic surgeries and its application to surgical phase recognition.

PubMed

Katić, Darko; Julliard, Chantal; Wekerle, Anna-Laura; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie; Jannin, Pierre; Gibaud, Bernard

2015-09-01

The rise of intraoperative information threatens to outpace our abilities to process it. Context-aware systems, filtering information to automatically adapt to the current needs of the surgeon, are necessary to fully profit from computerized surgery. To attain context awareness, representation of medical knowledge is crucial. However, most existing systems do not represent knowledge in a reusable way, hindering also reuse of data. Our purpose is therefore to make our computational models of medical knowledge sharable, extensible and interoperational with established knowledge representations in the form of the LapOntoSPM ontology. To show its usefulness, we apply it to situation interpretation, i.e., the recognition of surgical phases based on surgical activities. Considering best practices in ontology engineering and building on our ontology for laparoscopy, we formalized the workflow of laparoscopic adrenalectomies, cholecystectomies and pancreatic resections in the framework of OntoSPM, a new standard for surgical process models. Furthermore, we provide a rule-based situation interpretation algorithm based on SQWRL to recognize surgical phases using the ontology. The system was evaluated on ground-truth data from 19 manually annotated surgeries. The aim was to show that the phase recognition capabilities are equal to a specialized solution. The recognition rates of the new system were equal to the specialized one. However, the time needed to interpret a situation rose from 0.5 to 1.8 s on average which is still viable for practical application. We successfully integrated medical knowledge for laparoscopic surgeries into OntoSPM, facilitating knowledge and data sharing. This is especially important for reproducibility of results and unbiased comparison of recognition algorithms. The associated recognition algorithm was adapted to the new representation without any loss of classification power. The work is an important step to standardized knowledge and data representation in the field on context awareness and thus toward unified benchmark data sets.

An engineering paradigm in the biomedical sciences: Knowledge as epistemic tool.

PubMed

Boon, Mieke

2017-10-01

In order to deal with the complexity of biological systems and attempts to generate applicable results, current biomedical sciences are adopting concepts and methods from the engineering sciences. Philosophers of science have interpreted this as the emergence of an engineering paradigm, in particular in systems biology and synthetic biology. This article aims at the articulation of the supposed engineering paradigm by contrast with the physics paradigm that supported the rise of biochemistry and molecular biology. This articulation starts from Kuhn's notion of a disciplinary matrix, which indicates what constitutes a paradigm. It is argued that the core of the physics paradigm is its metaphysical and ontological presuppositions, whereas the core of the engineering paradigm is the epistemic aim of producing useful knowledge for solving problems external to the scientific practice. Therefore, the two paradigms involve distinct notions of knowledge. Whereas the physics paradigm entails a representational notion of knowledge, the engineering paradigm involves the notion of 'knowledge as epistemic tool'. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development and Evaluation of an Adolescents' Depression Ontology for Analyzing Social Data.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae; Song, Tae-Min

2016-01-01

This study aims to develop and evaluate an ontology for adolescents' depression to be used for collecting and analyzing social data. The ontology was developed according to the 'ontology development 101' methodology. Concepts were extracted from clinical practice guidelines and related literatures. The ontology is composed of five sub-ontologies which represent risk factors, sign and symptoms, measurement, diagnostic result and management care. The ontology was evaluated in four different ways: First, we examined the frequency of ontology concept appeared in social data; Second, the content coverage of ontology was evaluated by comparing ontology concepts with concepts extracted from the youth depression counseling records; Third, the structural and representational layer of the ontology were evaluated by 5 ontology and psychiatric nursing experts; Fourth, the scope of the ontology was examined by answering 59 competency questions. The ontology was improved by adding new concepts and synonyms and revising the level of structure.

A Method for Evaluating and Standardizing Ontologies

ERIC Educational Resources Information Center

Seyed, Ali Patrice

2012-01-01

The Open Biomedical Ontology (OBO) Foundry initiative is a collaborative effort for developing interoperable, science-based ontologies. The Basic Formal Ontology (BFO) serves as the upper ontology for the domain-level ontologies of OBO. BFO is an upper ontology of types as conceived by defenders of realism. Among the ontologies developed for OBO…

A Tailored Ontology Supporting Sensor Implementation for the Maintenance of Industrial Machines.

PubMed

Maleki, Elaheh; Belkadi, Farouk; Ritou, Mathieu; Bernard, Alain

2017-09-08

The longtime productivity of an industrial machine is improved by condition-based maintenance strategies. To do this, the integration of sensors and other cyber-physical devices is necessary in order to capture and analyze a machine's condition through its lifespan. Thus, choosing the best sensor is a critical step to ensure the efficiency of the maintenance process. Indeed, considering the variety of sensors, and their features and performance, a formal classification of a sensor's domain knowledge is crucial. This classification facilitates the search for and reuse of solutions during the design of a new maintenance service. Following a Knowledge Management methodology, the paper proposes and develops a new sensor ontology that structures the domain knowledge, covering both theoretical and experimental sensor attributes. An industrial case study is conducted to validate the proposed ontology and to demonstrate its utility as a guideline to ease the search of suitable sensors. Based on the ontology, the final solution will be implemented in a shared repository connected to legacy CAD (computer-aided design) systems. The selection of the best sensor is, firstly, obtained by the matching of application requirements and sensor specifications (that are proposed by this sensor repository). Then, it is refined from the experimentation results. The achieved solution is recorded in the sensor repository for future reuse. As a result, the time and cost of the design process of new condition-based maintenance services is reduced.

Representing Ontogeny Through Ontology: A Developmental Biologist’s Guide to The Gene Ontology

PubMed Central

Hill, David P.; Berardini, Tanya Z.; Howe, Douglas G.; Van Auken, Kimberly M.

2010-01-01

Developmental biology, like many other areas of biology, has undergone a dramatic shift in the perspective from which developmental processes are viewed. Instead of focusing on the actions of a handful of genes or functional RNAs, we now consider the interactions of large functional gene networks and study how these complex systems orchestrate the unfolding of an organism, from gametes to adult. Developmental biologists are beginning to realize that understanding ontogeny on this scale requires the utilization of computational methods to capture, store and represent the knowledge we have about the underlying processes. Here we review the use of the Gene Ontology (GO) to study developmental biology. We describe the organization and structure of the GO and illustrate some of the ways we use it to capture the current understanding of many common developmental processes. We also discuss ways in which gene product annotations using the GO have been used to ask and answer developmental questions in a variety of model developmental systems. We provide suggestions as to how the GO might be used in more powerful ways to address questions about development. Our goal is to provide developmental biologists with enough background about the GO that they can begin to think about how they might use the ontology efficiently and in the most powerful ways possible. PMID:19921742

Cross-species multiple environmental stress responses: An integrated approach to identify candidate genes for multiple stress tolerance in sorghum (Sorghum bicolor (L.) Moench) and related model species

PubMed Central

Modise, David M.; Gemeildien, Junaid; Ndimba, Bongani K.; Christoffels, Alan

2018-01-01

Background Crop response to the changing climate and unpredictable effects of global warming with adverse conditions such as drought stress has brought concerns about food security to the fore; crop yield loss is a major cause of concern in this regard. Identification of genes with multiple responses across environmental stresses is the genetic foundation that leads to crop adaptation to environmental perturbations. Methods In this paper, we introduce an integrated approach to assess candidate genes for multiple stress responses across-species. The approach combines ontology based semantic data integration with expression profiling, comparative genomics, phylogenomics, functional gene enrichment and gene enrichment network analysis to identify genes associated with plant stress phenotypes. Five different ontologies, viz., Gene Ontology (GO), Trait Ontology (TO), Plant Ontology (PO), Growth Ontology (GRO) and Environment Ontology (EO) were used to semantically integrate drought related information. Results Target genes linked to Quantitative Trait Loci (QTLs) controlling yield and stress tolerance in sorghum (Sorghum bicolor (L.) Moench) and closely related species were identified. Based on the enriched GO terms of the biological processes, 1116 sorghum genes with potential responses to 5 different stresses, such as drought (18%), salt (32%), cold (20%), heat (8%) and oxidative stress (25%) were identified to be over-expressed. Out of 169 sorghum drought responsive QTLs associated genes that were identified based on expression datasets, 56% were shown to have multiple stress responses. On the other hand, out of 168 additional genes that have been evaluated for orthologous pairs, 90% were conserved across species for drought tolerance. Over 50% of identified maize and rice genes were responsive to drought and salt stresses and were co-located within multifunctional QTLs. Among the total identified multi-stress responsive genes, 272 targets were shown to be co-localized within QTLs associated with different traits that are responsive to multiple stresses. Ontology mapping was used to validate the identified genes, while reconstruction of the phylogenetic tree was instrumental to infer the evolutionary relationship of the sorghum orthologs. The results also show specific genes responsible for various interrelated components of drought response mechanism such as drought tolerance, drought avoidance and drought escape. Conclusions We submit that this approach is novel and to our knowledge, has not been used previously in any other research; it enables us to perform cross-species queries for genes that are likely to be associated with multiple stress tolerance, as a means to identify novel targets for engineering stress resistance in sorghum and possibly, in other crop species. PMID:29590108

Towards ontology personalization to enrich social conversations on AAC systems

NASA Astrophysics Data System (ADS)

Mancilla V., Daniela; Sastoque H., Sebastian; Iregui G., Marcela

2015-01-01

Communication is one of the essential needs of human beings. Augmentative and Alternative Communication Systems (AAC) seek to help in the generation of oral and written language to people with physical disorders that limit their natural communication. These systems present significant challenges such as: the composition of consistent messages according to syntactic and semantic rules, the improvement of message production times, the application to social contexts and, consequently, the incorporation of user-specific information. This work presents an original ontology personalization approach for an AAC instant messaging system incorporating personalized information to improve the efficacy and efficiency of the message production. This proposal is based on a projection of a general ontology into a more specific one, avoiding storage redundancy and data coupling, representing a big opportunity to enrich communication capabilities of current AAC systems. The evaluation was performed for a study case based on an AAC system for assistance in composing messages. The results show that adding user-specific information allows generation of enriched phrases, so improving the accuracy of the message, facilitating the communication process.

SemanticOrganizer: A Customizable Semantic Repository for Distributed NASA Project Teams

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Berrios, Daniel C.; Carvalho, Robert E.; Hall, David R.; Rich, Stephen J.; Sturken, Ian B.; Swanson, Keith J.; Wolfe, Shawn R.

2004-01-01

SemanticOrganizer is a collaborative knowledge management system designed to support distributed NASA projects, including diverse teams of scientists, engineers, and accident investigators. The system provides a customizable, semantically structured information repository that stores work products relevant to multiple projects of differing types. SemanticOrganizer is one of the earliest and largest semantic web applications deployed at NASA to date, and has been used in diverse contexts ranging from the investigation of Space Shuttle Columbia's accident to the search for life on other planets. Although the underlying repository employs a single unified ontology, access control and ontology customization mechanisms make the repository contents appear different for each project team. This paper describes SemanticOrganizer, its customization facilities, and a sampling of its applications. The paper also summarizes some key lessons learned from building and fielding a successful semantic web application across a wide-ranging set of domains with diverse users.

Linking the Long Tail of Data: A Bottoms-up Approach to Connecting Scientific Research

NASA Astrophysics Data System (ADS)

Jacob, B.; Arctur, D. K.

2016-12-01

Highly curated ontologies are often developed for big scientific data, but the long tail of research data rarely receives the same treatment. The learning curve for Semantic Web technology is steep, and the value of linking each long-tail data set to known taxonomies and ontologies in isolation rarely justifies the level of effort required to bring a Knowledge Engineer into the project. We present an approach that takes a bottoms-up approach of producing a Linked Data model of datasets mechanically, inferring the shape and structure of the data from the original format, and adding derived variables and semantic linkages via iterative, interactive refinements of that model. In this way, the vast corpus of small but rich scientific data becomes part of the greater linked web of knowledge, and the connectivity of that data can be iteratively improved over time.

What next after determinism in the ontology of technology? Distributing responsibility in the biofuel debate.

PubMed

Boucher, Philip

2011-09-01

This article builds upon previous discussion of social and technical determinisms as implicit positions in the biofuel debate. To ensure these debates are balanced, it has been suggested that they should be designed to contain a variety of deterministic positions. Whilst it is agreed that determinism does not feature strongly in contemporary academic literatures, it is found that they have generally been superseded by an absence of any substantive conceptualisation of how the social shaping of technology may be related to, or occur alongside, an objective or autonomous reality. The problem of determinism emerges at an ontological level and must be resolved in situ. A critical realist approach to technology is presented which may provide a more appropriate framework for debate. In dialogue with previous discussion, the distribution of responsibility is revisited with reference to the role of scientists and engineers.

Knowledge Representation and Management, It's Time to Integrate!

PubMed

Dhombres, F; Charlet, J

2017-08-01

Objectives: To select, present, and summarize the best papers published in 2016 in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive and standardized review of the medical informatics literature was performed based on a PubMed query. Results: Among the 1,421 retrieved papers, the review process resulted in the selection of four best papers focused on the integration of heterogeneous data via the development and the alignment of terminological resources. In the first article, the authors provide a curated and standardized version of the publicly available US FDA Adverse Event Reporting System. Such a resource will improve the quality of the underlying data, and enable standardized analyses using common vocabularies. The second article describes a project developed in order to facilitate heterogeneous data integration in the i2b2 framework. The originality is to allow users integrate the data described in different terminologies and to build a new repository, with a unique model able to support the representation of the various data. The third paper is dedicated to model the association between multiple phenotypic traits described within the Human Phenotype Ontology (HPO) and the corresponding genotype in the specific context of rare diseases (rare variants). Finally, the fourth paper presents solutions to annotation-ontology mapping in genome-scale data. Of particular interest in this work is the Experimental Factor Ontology (EFO) and its generic association model, the Ontology of Biomedical AssociatioN (OBAN). Conclusion: Ontologies have started to show their efficiency to integrate medical data for various tasks in medical informatics: electronic health records data management, clinical research, and knowledge-based systems development. Georg Thieme Verlag KG Stuttgart.

Closed-Loop Lifecycle Management of Service and Product in the Internet of Things: Semantic Framework for Knowledge Integration.

PubMed

Yoo, Min-Jung; Grozel, Clément; Kiritsis, Dimitris

2016-07-08

This paper describes our conceptual framework of closed-loop lifecycle information sharing for product-service in the Internet of Things (IoT). The framework is based on the ontology model of product-service and a type of IoT message standard, Open Messaging Interface (O-MI) and Open Data Format (O-DF), which ensures data communication. (1) BACKGROUND: Based on an existing product lifecycle management (PLM) methodology, we enhanced the ontology model for the purpose of integrating efficiently the product-service ontology model that was newly developed; (2) METHODS: The IoT message transfer layer is vertically integrated into a semantic knowledge framework inside which a Semantic Info-Node Agent (SINA) uses the message format as a common protocol of product-service lifecycle data transfer; (3) RESULTS: The product-service ontology model facilitates information retrieval and knowledge extraction during the product lifecycle, while making more information available for the sake of service business creation. The vertical integration of IoT message transfer, encompassing all semantic layers, helps achieve a more flexible and modular approach to knowledge sharing in an IoT environment; (4) Contribution: A semantic data annotation applied to IoT can contribute to enhancing collected data types, which entails a richer knowledge extraction. The ontology-based PLM model enables as well the horizontal integration of heterogeneous PLM data while breaking traditional vertical information silos; (5) CONCLUSION: The framework was applied to a fictive case study with an electric car service for the purpose of demonstration. For the purpose of demonstrating the feasibility of the approach, the semantic model is implemented in Sesame APIs, which play the role of an Internet-connected Resource Description Framework (RDF) database.

Closed-Loop Lifecycle Management of Service and Product in the Internet of Things: Semantic Framework for Knowledge Integration

PubMed Central

Yoo, Min-Jung; Grozel, Clément; Kiritsis, Dimitris

2016-01-01

This paper describes our conceptual framework of closed-loop lifecycle information sharing for product-service in the Internet of Things (IoT). The framework is based on the ontology model of product-service and a type of IoT message standard, Open Messaging Interface (O-MI) and Open Data Format (O-DF), which ensures data communication. (1) Background: Based on an existing product lifecycle management (PLM) methodology, we enhanced the ontology model for the purpose of integrating efficiently the product-service ontology model that was newly developed; (2) Methods: The IoT message transfer layer is vertically integrated into a semantic knowledge framework inside which a Semantic Info-Node Agent (SINA) uses the message format as a common protocol of product-service lifecycle data transfer; (3) Results: The product-service ontology model facilitates information retrieval and knowledge extraction during the product lifecycle, while making more information available for the sake of service business creation. The vertical integration of IoT message transfer, encompassing all semantic layers, helps achieve a more flexible and modular approach to knowledge sharing in an IoT environment; (4) Contribution: A semantic data annotation applied to IoT can contribute to enhancing collected data types, which entails a richer knowledge extraction. The ontology-based PLM model enables as well the horizontal integration of heterogeneous PLM data while breaking traditional vertical information silos; (5) Conclusion: The framework was applied to a fictive case study with an electric car service for the purpose of demonstration. For the purpose of demonstrating the feasibility of the approach, the semantic model is implemented in Sesame APIs, which play the role of an Internet-connected Resource Description Framework (RDF) database. PMID:27399717

OntologyWidget - a reusable, embeddable widget for easily locating ontology terms.

PubMed

Beauheim, Catherine C; Wymore, Farrell; Nitzberg, Michael; Zachariah, Zachariah K; Jin, Heng; Skene, J H Pate; Ball, Catherine A; Sherlock, Gavin

2007-09-13

Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form. We have produced a tool, OntologyWidget, which allows users to rapidly search for and browse ontology terms. OntologyWidget can easily be embedded in other web-based applications. OntologyWidget is written using AJAX (Asynchronous JavaScript and XML) and has two related elements. The first is a dynamic auto-complete ontology search feature. As a user enters characters into the search box, the appropriate ontology is queried remotely for terms that match the typed-in text, and the query results populate a drop-down list with all potential matches. Upon selection of a term from the list, the user can locate this term within a generic and dynamic ontology browser, which comprises the second element of the tool. The ontology browser shows the paths from a selected term to the root as well as parent/child tree hierarchies. We have implemented web services at the Stanford Microarray Database (SMD), which provide the OntologyWidget with access to over 40 ontologies from the Open Biological Ontology (OBO) website 1. Each ontology is updated weekly. Adopters of the OntologyWidget can either use SMD's web services, or elect to rely on their own. Deploying the OntologyWidget can be accomplished in three simple steps: (1) install Apache Tomcat 2 on one's web server, (2) download and install the OntologyWidget servlet stub that provides access to the SMD ontology web services, and (3) create an html (HyperText Markup Language) file that refers to the OntologyWidget using a simple, well-defined format. We have developed OntologyWidget, an easy-to-use ontology search and display tool that can be used on any web page by creating a simple html description. OntologyWidget provides a rapid auto-complete search function paired with an interactive tree display. We have developed a web service layer that communicates between the web page interface and a database of ontology terms. We currently store 40 of the ontologies from the OBO website 1, as well as a several others. These ontologies are automatically updated on a weekly basis. OntologyWidget can be used in any web-based application to take advantage of the ontologies we provide via web services or any other ontology that is provided elsewhere in the correct format. The full source code for the JavaScript and description of the OntologyWidget is available from http://smd.stanford.edu/ontologyWidget/.

ontologyX: a suite of R packages for working with ontological data.

PubMed

Greene, Daniel; Richardson, Sylvia; Turro, Ernest

2017-04-01

Ontologies are widely used constructs for encoding and analyzing biomedical data, but the absence of simple and consistent tools has made exploratory and systematic analysis of such data unnecessarily difficult. Here we present three packages which aim to simplify such procedures. The ontologyIndex package enables arbitrary ontologies to be read into R, supports representation of ontological objects by native R types, and provides a parsimonius set of performant functions for querying ontologies. ontologySimilarity and ontologyPlot extend ontologyIndex with functionality for straightforward visualization and semantic similarity calculations, including statistical routines. ontologyIndex , ontologyPlot and ontologySimilarity are all available on the Comprehensive R Archive Network website under https://cran.r-project.org/web/packages/ . Daniel Greene dg333@cam.ac.uk. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Ontology for Life-Cycle Modeling of Water Distribution Systems: Model View Definition

DTIC Science & Technology

2013-06-01

Research and Development Center, Construction Engineering Research Laboratory (ERDC-CERL) to develop a life-cycle building model have resulted in the...Laboratory (ERDC-CERL) to develop a life-cycle building model have resulted in the definition of a “core” building information model that contains...developed experimental BIM models us- ing commercial off-the-shelf (COTS) software. Those models represent three types of typical low-rise Army

OntologyWidget – a reusable, embeddable widget for easily locating ontology terms

PubMed Central

Beauheim, Catherine C; Wymore, Farrell; Nitzberg, Michael; Zachariah, Zachariah K; Jin, Heng; Skene, JH Pate; Ball, Catherine A; Sherlock, Gavin

2007-01-01

Background Biomedical ontologies are being widely used to annotate biological data in a computer-accessible, consistent and well-defined manner. However, due to their size and complexity, annotating data with appropriate terms from an ontology is often challenging for experts and non-experts alike, because there exist few tools that allow one to quickly find relevant ontology terms to easily populate a web form. Results We have produced a tool, OntologyWidget, which allows users to rapidly search for and browse ontology terms. OntologyWidget can easily be embedded in other web-based applications. OntologyWidget is written using AJAX (Asynchronous JavaScript and XML) and has two related elements. The first is a dynamic auto-complete ontology search feature. As a user enters characters into the search box, the appropriate ontology is queried remotely for terms that match the typed-in text, and the query results populate a drop-down list with all potential matches. Upon selection of a term from the list, the user can locate this term within a generic and dynamic ontology browser, which comprises the second element of the tool. The ontology browser shows the paths from a selected term to the root as well as parent/child tree hierarchies. We have implemented web services at the Stanford Microarray Database (SMD), which provide the OntologyWidget with access to over 40 ontologies from the Open Biological Ontology (OBO) website [1]. Each ontology is updated weekly. Adopters of the OntologyWidget can either use SMD's web services, or elect to rely on their own. Deploying the OntologyWidget can be accomplished in three simple steps: (1) install Apache Tomcat [2] on one's web server, (2) download and install the OntologyWidget servlet stub that provides access to the SMD ontology web services, and (3) create an html (HyperText Markup Language) file that refers to the OntologyWidget using a simple, well-defined format. Conclusion We have developed OntologyWidget, an easy-to-use ontology search and display tool that can be used on any web page by creating a simple html description. OntologyWidget provides a rapid auto-complete search function paired with an interactive tree display. We have developed a web service layer that communicates between the web page interface and a database of ontology terms. We currently store 40 of the ontologies from the OBO website [1], as well as a several others. These ontologies are automatically updated on a weekly basis. OntologyWidget can be used in any web-based application to take advantage of the ontologies we provide via web services or any other ontology that is provided elsewhere in the correct format. The full source code for the JavaScript and description of the OntologyWidget is available from . PMID:17854506

Using Linked Open Data and Semantic Integration to Search Across Geoscience Repositories

NASA Astrophysics Data System (ADS)

Mickle, A.; Raymond, L. M.; Shepherd, A.; Arko, R. A.; Carbotte, S. M.; Chandler, C. L.; Cheatham, M.; Fils, D.; Hitzler, P.; Janowicz, K.; Jones, M.; Krisnadhi, A.; Lehnert, K. A.; Narock, T.; Schildhauer, M.; Wiebe, P. H.

2014-12-01

The MBLWHOI Library is a partner in the OceanLink project, an NSF EarthCube Building Block, applying semantic technologies to enable knowledge discovery, sharing and integration. OceanLink is testing ontology design patterns that link together: two data repositories, Rolling Deck to Repository (R2R), Biological and Chemical Oceanography Data Management Office (BCO-DMO); the MBLWHOI Library Institutional Repository (IR) Woods Hole Open Access Server (WHOAS); National Science Foundation (NSF) funded awards; and American Geophysical Union (AGU) conference presentations. The Library is collaborating with scientific users, data managers, DSpace engineers, experts in ontology design patterns, and user interface developers to make WHOAS, a DSpace repository, linked open data enabled. The goal is to allow searching across repositories without any of the information providers having to change how they manage their collections. The tools developed for DSpace will be made available to the community of users. There are 257 registered DSpace repositories in the United Stated and over 1700 worldwide. Outcomes include: Integration of DSpace with OpenRDF Sesame triple store to provide SPARQL endpoint for the storage and query of RDF representation of DSpace resources, Mapping of DSpace resources to OceanLink ontology, and DSpace "data" add on to provide resolvable linked open data representation of DSpace resources.

Toward a Blended Ontology: Applying Knowledge Systems to ...

EPA Pesticide Factsheets

Bionanomedicine and environmental research share need common terms and ontologies. This study applied knowledge systems, data mining, and bibliometrics used in nano-scale ADME research from 1991 to 2011. The prominence of nano-ADME in environmental research began to exceed the publication rate in medical research in 2006. That trend appears to continue as a result of the growing products in commerce using nanotechnology, that is, 5-fold growth in number of countries with nanomaterials research centers. Funding for this research virtually did not exist prior to 2002, whereas today both medical and environmental research is funded globally. Key nanoparticle research began with pharmacology and therapeutic drug-delivery and contrasting agents, but the advances have found utility in the environmental research community. As evidence ultrafine aerosols and aquatic colloids research increased 6-fold, indicating a new emphasis on environmental nanotoxicology. User-directed expert elicitation from the engineering and chemical/ADME domains can be combined with appropriate Boolean logic and queries to define the corpus of nanoparticle interest. The study combined pharmacological expertise and informatics to identify the corpus by building logical conclusions and observations. Publication records informatics can lead to an enhanced understanding the connectivity between fields, as well as overcoming the differences in ontology between the fields. The National Exposure Resea

Flood AI: An Intelligent Systems for Discovery and Communication of Disaster Knowledge

NASA Astrophysics Data System (ADS)

Demir, I.; Sermet, M. Y.

2017-12-01

Communities are not immune from extreme events or natural disasters that can lead to large-scale consequences for the nation and public. Improving resilience to better prepare, plan, recover, and adapt to disasters is critical to reduce the impacts of extreme events. The National Research Council (NRC) report discusses the topic of how to increase resilience to extreme events through a vision of resilient nation in the year 2030. The report highlights the importance of data, information, gaps and knowledge challenges that needs to be addressed, and suggests every individual to access the risk and vulnerability information to make their communities more resilient. This project presents an intelligent system, Flood AI, for flooding to improve societal preparedness by providing a knowledge engine using voice recognition, artificial intelligence, and natural language processing based on a generalized ontology for disasters with a primary focus on flooding. The knowledge engine utilizes the flood ontology and concepts to connect user input to relevant knowledge discovery channels on flooding by developing a data acquisition and processing framework utilizing environmental observations, forecast models, and knowledge bases. Communication channels of the framework includes web-based systems, agent-based chat bots, smartphone applications, automated web workflows, and smart home devices, opening the knowledge discovery for flooding to many unique use cases.

KneeTex: an ontology-driven system for information extraction from MRI reports.

PubMed

Spasić, Irena; Zhao, Bo; Jones, Christopher B; Button, Kate

2015-01-01

In the realm of knee pathology, magnetic resonance imaging (MRI) has the advantage of visualising all structures within the knee joint, which makes it a valuable tool for increasing diagnostic accuracy and planning surgical treatments. Therefore, clinical narratives found in MRI reports convey valuable diagnostic information. A range of studies have proven the feasibility of natural language processing for information extraction from clinical narratives. However, no study focused specifically on MRI reports in relation to knee pathology, possibly due to the complexity of knee anatomy and a wide range of conditions that may be associated with different anatomical entities. In this paper we describe KneeTex, an information extraction system that operates in this domain. As an ontology-driven information extraction system, KneeTex makes active use of an ontology to strongly guide and constrain text analysis. We used automatic term recognition to facilitate the development of a domain-specific ontology with sufficient detail and coverage for text mining applications. In combination with the ontology, high regularity of the sublanguage used in knee MRI reports allowed us to model its processing by a set of sophisticated lexico-semantic rules with minimal syntactic analysis. The main processing steps involve named entity recognition combined with coordination, enumeration, ambiguity and co-reference resolution, followed by text segmentation. Ontology-based semantic typing is then used to drive the template filling process. We adopted an existing ontology, TRAK (Taxonomy for RehAbilitation of Knee conditions), for use within KneeTex. The original TRAK ontology expanded from 1,292 concepts, 1,720 synonyms and 518 relationship instances to 1,621 concepts, 2,550 synonyms and 560 relationship instances. This provided KneeTex with a very fine-grained lexico-semantic knowledge base, which is highly attuned to the given sublanguage. Information extraction results were evaluated on a test set of 100 MRI reports. A gold standard consisted of 1,259 filled template records with the following slots: finding, finding qualifier, negation, certainty, anatomy and anatomy qualifier. KneeTex extracted information with precision of 98.00 %, recall of 97.63 % and F-measure of 97.81 %, the values of which are in line with human-like performance. KneeTex is an open-source, stand-alone application for information extraction from narrative reports that describe an MRI scan of the knee. Given an MRI report as input, the system outputs the corresponding clinical findings in the form of JavaScript Object Notation objects. The extracted information is mapped onto TRAK, an ontology that formally models knowledge relevant for the rehabilitation of knee conditions. As a result, formally structured and coded information allows for complex searches to be conducted efficiently over the original MRI reports, thereby effectively supporting epidemiologic studies of knee conditions.

Formalizing the Austrian Procedure Catalogue: A 4-step methodological analysis approach.

PubMed

Neururer, Sabrina Barbara; Lasierra, Nelia; Peiffer, Karl Peter; Fensel, Dieter

2016-04-01

Due to the lack of an internationally accepted and adopted standard for coding health interventions, Austria has established its own country-specific procedure classification system - the Austrian Procedure Catalogue (APC). Even though the APC is an elaborate coding standard for medical procedures, it has shortcomings that limit its usability. In order to enhance usability and usefulness, especially for research purposes and e-health applications, we developed an ontologized version of the APC. In this paper we present a novel four-step approach for the ontology engineering process, which enables accurate extraction of relevant concepts for medical ontologies from written text. The proposed approach for formalizing the APC consists of the following four steps: (1) comparative pre-analysis, (2) definition analysis, (3) typological analysis, and (4) ontology implementation. The first step contained a comparison of the APC to other well-established or elaborate health intervention coding systems in order to identify strengths and weaknesses of the APC. In the second step, a list of definitions of medical terminology used in the APC was obtained. This list of definitions was used as input for Step 3, in which we identified the most important concepts to describe medical procedures using the qualitative typological analysis approach. The definition analysis as well as the typological analysis are well-known and effective methods used in social sciences, but not commonly employed in the computer science or ontology engineering domain. Finally, this list of concepts was used in Step 4 to formalize the APC. The pre-analysis highlighted the major shortcomings of the APC, such as the lack of formal definition, leading to implicitly available, but not directly accessible information (hidden data), or the poor procedural type classification. After performing the definition and subsequent typological analyses, we were able to identify the following main characteristics of health interventions: (1) Procedural type, (2) Anatomical site, (3) Medical device, (4) Pathology, (5) Access, (6) Body system, (7) Population, (8) Aim, (9) Discipline, (10) Technique, and (11) Body Function. These main characteristics were taken as input of classes for the formalization of the APC. We were also able to identify relevant relations between classes. The proposed four-step approach for formalizing the APC provides a novel, systematically developed, strong framework to semantically enrich procedure classifications. Although this methodology was designed to address the particularities of the APC, the included methods are based on generic analysis tasks, and therefore can be re-used to provide a systematic representation of other procedure catalogs or classification systems and hence contribute towards a universal alignment of such representations, if desired. Copyright © 2015 Elsevier Inc. All rights reserved.

Ontology Research and Development. Part 2 - A Review of Ontology Mapping and Evolving.

ERIC Educational Resources Information Center

Ding, Ying; Foo, Schubert

2002-01-01

Reviews ontology research and development, specifically ontology mapping and evolving. Highlights include an overview of ontology mapping projects; maintaining existing ontologies and extending them as appropriate when new information or knowledge is acquired; and ontology's role and the future of the World Wide Web, or Semantic Web. (Contains 55…

SSWAP: A Simple Semantic Web Architecture and Protocol for semantic web services.

PubMed

Gessler, Damian D G; Schiltz, Gary S; May, Greg D; Avraham, Shulamit; Town, Christopher D; Grant, David; Nelson, Rex T

2009-09-23

SSWAP (Simple Semantic Web Architecture and Protocol; pronounced "swap") is an architecture, protocol, and platform for using reasoning to semantically integrate heterogeneous disparate data and services on the web. SSWAP was developed as a hybrid semantic web services technology to overcome limitations found in both pure web service technologies and pure semantic web technologies. There are currently over 2400 resources published in SSWAP. Approximately two dozen are custom-written services for QTL (Quantitative Trait Loci) and mapping data for legumes and grasses (grains). The remaining are wrappers to Nucleic Acids Research Database and Web Server entries. As an architecture, SSWAP establishes how clients (users of data, services, and ontologies), providers (suppliers of data, services, and ontologies), and discovery servers (semantic search engines) interact to allow for the description, querying, discovery, invocation, and response of semantic web services. As a protocol, SSWAP provides the vocabulary and semantics to allow clients, providers, and discovery servers to engage in semantic web services. The protocol is based on the W3C-sanctioned first-order description logic language OWL DL. As an open source platform, a discovery server running at http://sswap.info (as in to "swap info") uses the description logic reasoner Pellet to integrate semantic resources. The platform hosts an interactive guide to the protocol at http://sswap.info/protocol.jsp, developer tools at http://sswap.info/developer.jsp, and a portal to third-party ontologies at http://sswapmeet.sswap.info (a "swap meet"). SSWAP addresses the three basic requirements of a semantic web services architecture (i.e., a common syntax, shared semantic, and semantic discovery) while addressing three technology limitations common in distributed service systems: i.e., i) the fatal mutability of traditional interfaces, ii) the rigidity and fragility of static subsumption hierarchies, and iii) the confounding of content, structure, and presentation. SSWAP is novel by establishing the concept of a canonical yet mutable OWL DL graph that allows data and service providers to describe their resources, to allow discovery servers to offer semantically rich search engines, to allow clients to discover and invoke those resources, and to allow providers to respond with semantically tagged data. SSWAP allows for a mix-and-match of terms from both new and legacy third-party ontologies in these graphs.

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

PubMed

Martínez-Romero, Marcos; Jonquet, Clement; O'Connor, Martin J; Graybeal, John; Pazos, Alejandro; Musen, Mark A

2017-06-07

Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability across disparate datasets. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a novel recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four different criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies to use together. It also can be customized to fit the needs of different ontology recommendation scenarios. Ontology Recommender 2.0 suggests relevant ontologies for annotating biomedical text data. It combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available (both via the user interface at http://bioportal.bioontology.org/recommender , and via a Web service API).

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability.

PubMed

He, Yongqun; Xiang, Zuoshuang; Zheng, Jie; Lin, Yu; Overton, James A; Ong, Edison

2018-01-12

Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies developed, it has become critical to ensure ontology interoperability and the usage of interoperable ontologies for standardized data representation and integration. The suite of web-based Ontoanimal tools (e.g., Ontofox, Ontorat, and Ontobee) support different aspects of extensible ontology development. By summarizing the common features of Ontoanimal and other similar tools, we identified and proposed an "eXtensible Ontology Development" (XOD) strategy and its associated four principles. These XOD principles reuse existing terms and semantic relations from reliable ontologies, develop and apply well-established ontology design patterns (ODPs), and involve community efforts to support new ontology development, promoting standardized and interoperable data and knowledge representation and integration. The adoption of the XOD strategy, together with robust XOD tool development, will greatly support ontology interoperability and robust ontology applications to support data to be Findable, Accessible, Interoperable and Reusable (i.e., FAIR).

Inferring ontology graph structures using OWL reasoning.

PubMed

Rodríguez-García, Miguel Ángel; Hoehndorf, Robert

2018-01-05

Ontologies are representations of a conceptualization of a domain. Traditionally, ontologies in biology were represented as directed acyclic graphs (DAG) which represent the backbone taxonomy and additional relations between classes. These graphs are widely exploited for data analysis in the form of ontology enrichment or computation of semantic similarity. More recently, ontologies are developed in a formal language such as the Web Ontology Language (OWL) and consist of a set of axioms through which classes are defined or constrained. While the taxonomy of an ontology can be inferred directly from the axioms of an ontology as one of the standard OWL reasoning tasks, creating general graph structures from OWL ontologies that exploit the ontologies' semantic content remains a challenge. We developed a method to transform ontologies into graphs using an automated reasoner while taking into account all relations between classes. Searching for (existential) patterns in the deductive closure of ontologies, we can identify relations between classes that are implied but not asserted and generate graph structures that encode for a large part of the ontologies' semantic content. We demonstrate the advantages of our method by applying it to inference of protein-protein interactions through semantic similarity over the Gene Ontology and demonstrate that performance is increased when graph structures are inferred using deductive inference according to our method. Our software and experiment results are available at http://github.com/bio-ontology-research-group/Onto2Graph . Onto2Graph is a method to generate graph structures from OWL ontologies using automated reasoning. The resulting graphs can be used for improved ontology visualization and ontology-based data analysis.

Development and Evaluation of an Ontology for Guiding Appropriate Antibiotic Prescribing

PubMed Central

Furuya, E. Yoko; Kuperman, Gilad J.; Cimino, James J.; Bakken, Suzanne

2011-01-01

Objectives To develop and apply formal ontology creation methods to the domain of antimicrobial prescribing and to formally evaluate the resulting ontology through intrinsic and extrinsic evaluation studies. Methods We extended existing ontology development methods to create the ontology and implemented the ontology using Protégé-OWL. Correctness of the ontology was assessed using a set of ontology design principles and domain expert review via the laddering technique. We created three artifacts to support the extrinsic evaluation (set of prescribing rules, alerts and an ontology-driven alert module, and a patient database) and evaluated the usefulness of the ontology for performing knowledge management tasks to maintain the ontology and for generating alerts to guide antibiotic prescribing. Results The ontology includes 199 classes, 10 properties, and 1,636 description logic restrictions. Twenty-three Semantic Web Rule Language rules were written to generate three prescribing alerts: 1) antibiotic-microorganism mismatch alert; 2) medication-allergy alert; and 3) non-recommended empiric antibiotic therapy alert. The evaluation studies confirmed the correctness of the ontology, usefulness of the ontology for representing and maintaining antimicrobial treatment knowledge rules, and usefulness of the ontology for generating alerts to provide feedback to clinicians during antibiotic prescribing. Conclusions This study contributes to the understanding of ontology development and evaluation methods and addresses one knowledge gap related to using ontologies as a clinical decision support system component—a need for formal ontology evaluation methods to measure their quality from the perspective of their intrinsic characteristics and their usefulness for specific tasks. PMID:22019377

Development and evaluation of an ontology for guiding appropriate antibiotic prescribing.

PubMed

Bright, Tiffani J; Yoko Furuya, E; Kuperman, Gilad J; Cimino, James J; Bakken, Suzanne

2012-02-01

To develop and apply formal ontology creation methods to the domain of antimicrobial prescribing and to formally evaluate the resulting ontology through intrinsic and extrinsic evaluation studies. We extended existing ontology development methods to create the ontology and implemented the ontology using Protégé-OWL. Correctness of the ontology was assessed using a set of ontology design principles and domain expert review via the laddering technique. We created three artifacts to support the extrinsic evaluation (set of prescribing rules, alerts and an ontology-driven alert module, and a patient database) and evaluated the usefulness of the ontology for performing knowledge management tasks to maintain the ontology and for generating alerts to guide antibiotic prescribing. The ontology includes 199 classes, 10 properties, and 1636 description logic restrictions. Twenty-three Semantic Web Rule Language rules were written to generate three prescribing alerts: (1) antibiotic-microorganism mismatch alert; (2) medication-allergy alert; and (3) non-recommended empiric antibiotic therapy alert. The evaluation studies confirmed the correctness of the ontology, usefulness of the ontology for representing and maintaining antimicrobial treatment knowledge rules, and usefulness of the ontology for generating alerts to provide feedback to clinicians during antibiotic prescribing. This study contributes to the understanding of ontology development and evaluation methods and addresses one knowledge gap related to using ontologies as a clinical decision support system component-a need for formal ontology evaluation methods to measure their quality from the perspective of their intrinsic characteristics and their usefulness for specific tasks. Copyright © 2011 Elsevier Inc. All rights reserved.

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.

PubMed

Amith, Muhammad; He, Zhe; Bian, Jiang; Lossio-Ventura, Juan Antonio; Tao, Cui

2018-04-01

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled terminologies are intended to assure interoperability. Nevertheless, the quality of biomedical ontologies has hindered their applicability and subsequent adoption in real-world applications. Ontology evaluation is an integral part of ontology development and maintenance. In the biomedicine domain, ontology evaluation is often conducted by third parties as a quality assurance (or auditing) effort that focuses on identifying modeling errors and inconsistencies. In this work, we first organized four categorical schemes of ontology evaluation methods in the existing literature to create an integrated taxonomy. Further, to understand the ontology evaluation practice in the biomedicine domain, we reviewed a sample of 200 ontologies from the National Center for Biomedical Ontology (NCBO) BioPortal-the largest repository for biomedical ontologies-and observed that only 15 of these ontologies have documented evaluation in their corresponding inception papers. We then surveyed the recent quality assurance approaches for biomedical ontologies and their use. We also mapped these quality assurance approaches to the ontology evaluation criteria. It is our anticipation that ontology evaluation and quality assurance approaches will be more widely adopted in the development life cycle of biomedical ontologies. Copyright © 2018 Elsevier Inc. All rights reserved.

A novel algorithm for fully automated mapping of geospatial ontologies

NASA Astrophysics Data System (ADS)

Chaabane, Sana; Jaziri, Wassim

2018-01-01

Geospatial information is collected from different sources thus making spatial ontologies, built for the same geographic domain, heterogeneous; therefore, different and heterogeneous conceptualizations may coexist. Ontology integrating helps creating a common repository of the geospatial ontology and allows removing the heterogeneities between the existing ontologies. Ontology mapping is a process used in ontologies integrating and consists in finding correspondences between the source ontologies. This paper deals with the "mapping" process of geospatial ontologies which consist in applying an automated algorithm in finding the correspondences between concepts referring to the definitions of matching relationships. The proposed algorithm called "geographic ontologies mapping algorithm" defines three types of mapping: semantic, topological and spatial.

Interestingness measures and strategies for mining multi-ontology multi-level association rules from gene ontology annotations for the discovery of new GO relationships.

PubMed

Manda, Prashanti; McCarthy, Fiona; Bridges, Susan M

2013-10-01

The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.

Reusing Design Knowledge Based on Design Cases and Knowledge Map

ERIC Educational Resources Information Center

Yang, Cheng; Liu, Zheng; Wang, Haobai; Shen, Jiaoqi

2013-01-01

Design knowledge was reused for innovative design work to support designers with product design knowledge and help designers who lack rich experiences to improve their design capacity and efficiency. First, based on the ontological model of product design knowledge constructed by taxonomy, implicit and explicit knowledge was extracted from some…

Situation exploration in a persistent surveillance system with multidimensional data

NASA Astrophysics Data System (ADS)

Habibi, Mohammad S.

2013-03-01

There is an emerging need for fusing hard and soft sensor data in an efficient surveillance system to provide accurate estimation of situation awareness. These mostly abstract, multi-dimensional and multi-sensor data pose a great challenge to the user in performing analysis of multi-threaded events efficiently and cohesively. To address this concern an interactive Visual Analytics (VA) application is developed for rapid assessment and evaluation of different hypotheses based on context-sensitive ontology spawn from taxonomies describing human/human and human/vehicle/object interactions. A methodology is described here for generating relevant ontology in a Persistent Surveillance System (PSS) and demonstrates how they can be utilized in the context of PSS to track and identify group activities pertaining to potential threats. The proposed VA system allows for visual analysis of raw data as well as metadata that have spatiotemporal representation and content-based implications. Additionally in this paper, a technique for rapid search of tagged information contingent to ranking and confidence is explained for analysis of multi-dimensional data. Lastly the issue of uncertainty associated with processing and interpretation of heterogeneous data is also addressed.

Modulated evaluation metrics for drug-based ontologies.

PubMed

Amith, Muhammad; Tao, Cui

2017-04-24

Research for ontology evaluation is scarce. If biomedical ontological datasets and knowledgebases are to be widely used, there needs to be quality control and evaluation for the content and structure of the ontology. This paper introduces how to effectively utilize a semiotic-inspired approach to ontology evaluation, specifically towards drug-related ontologies hosted on the National Center for Biomedical Ontology BioPortal. Using the semiotic-based evaluation framework for drug-based ontologies, we adjusted the quality metrics based on the semiotic features of drug ontologies. Then, we compared the quality scores before and after tailoring. The scores revealed a more precise measurement and a closer distribution compared to the before-tailoring. The results of this study reveal that a tailored semiotic evaluation produced a more meaningful and accurate assessment of drug-based ontologies, lending to the possible usefulness of semiotics in ontology evaluation.

The Semantic eScience Framework

NASA Astrophysics Data System (ADS)

McGuinness, Deborah; Fox, Peter; Hendler, James

2010-05-01

The goal of this effort is to design and implement a configurable and extensible semantic eScience framework (SESF). Configuration requires research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility is being achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in community settings, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.We report on how we are accommodating the rapid advances in semantic technologies and tools and the sustainable software path for the future (certain) technical advances. In addition to a generalization of the current data science interface, we will present plans for an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.SESF builds upon previous work in the Virtual Solar-Terrestrial Observatory. The VSTO utilizes leading edge knowledge representation, query and reasoning techniques to support knowledge-enhanced search, data access, integration, and manipulation. It encodes term meanings and their inter-relationships in ontologies anduses these ontologies and associated inference engines to semantically enable the data services. The Semantically-Enabled Science Data Integration (SESDI) project implemented data integration capabilities among three sub-disciplines; solar radiation, volcanic outgassing and atmospheric structure using extensions to existingmodular ontolgies and used the VSTO data framework, while adding smart faceted search and semantic data registrationtools. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) has added explanation provenance capabilities to an observational data ingest pipeline for images of the Sun providing a set of tools to answer diverseend user questions such as ``Why does this image look bad?. http://tw.rpi.edu/portal/SESF

The Semantic eScience Framework

NASA Astrophysics Data System (ADS)

Fox, P. A.; McGuinness, D. L.

2009-12-01

The goal of this effort is to design and implement a configurable and extensible semantic eScience framework (SESF). Configuration requires research into accommodating different levels of semantic expressivity and user requirements from use cases. Extensibility is being achieved in a modular approach to the semantic encodings (i.e. ontologies) performed in community settings, i.e. an ontology framework into which specific applications all the way up to communities can extend the semantics for their needs.We report on how we are accommodating the rapid advances in semantic technologies and tools and the sustainable software path for the future (certain) technical advances. In addition to a generalization of the current data science interface, we will present plans for an upper-level interface suitable for use by clearinghouses, and/or educational portals, digital libraries, and other disciplines.SESF builds upon previous work in the Virtual Solar-Terrestrial Observatory. The VSTO utilizes leading edge knowledge representation, query and reasoning techniques to support knowledge-enhanced search, data access, integration, and manipulation. It encodes term meanings and their inter-relationships in ontologies anduses these ontologies and associated inference engines to semantically enable the data services. The Semantically-Enabled Science Data Integration (SESDI) project implemented data integration capabilities among three sub-disciplines; solar radiation, volcanic outgassing and atmospheric structure using extensions to existingmodular ontolgies and used the VSTO data framework, while adding smart faceted search and semantic data registrationtools. The Semantic Provenance Capture in Data Ingest Systems (SPCDIS) has added explanation provenance capabilities to an observational data ingest pipeline for images of the Sun providing a set of tools to answer diverseend user questions such as ``Why does this image look bad?.

An Information Infrastructure for Coastal Models and Data

NASA Astrophysics Data System (ADS)

Hardin, D.; Keiser, K.; Conover, H.; Graves, S.

2007-12-01

Advances in semantics and visualization have given rise to new capabilities for the location, manipulation, integration, management and display of data and information in and across domains. An example of these capabilities is illustrated by a coastal restoration project that utilizes satellite, in-situ data and hydrodynamic model output to address seagrass habitat restoration in the Northern Gulf of Mexico. In this project a standard stressor conceptual model was implemented as an ontology in addition to the typical CMAP diagram. The ontology captures the elements of the seagrass conceptual model as well as the relationships between them. Noesis, developed by the University of Alabama in Huntsville, is an application that provides a simple but powerful way to search and organize data and information represented by ontologies. Noesis uses domain ontologies to help scope search queries to ensure that search results are both accurate and complete. Semantics are captured by refining the query terms to cover synonyms, specializations, generalizations and related concepts. As a resource aggregator Noesis categorizes search results returned from multiple, concurrent search engines such as Google, Yahoo, and Ask.com. Search results are further directed by accessing domain specific catalogs that include outputs from hydrodynamic and other models. Embedded within the search results are links that invoke applications such as web map displays, animation tools and virtual globe applications such as Google Earth. In the seagrass prioritization project Noesis is used to locate information that is vital to understanding the impact of stressors on the habitat. This presentation will show how the intelligent search capabilities of Noesis are coupled with visualization tools and model output to investigate the restoration of seagrass habitat.

A Tailored Ontology Supporting Sensor Implementation for the Maintenance of Industrial Machines

PubMed Central

Belkadi, Farouk; Bernard, Alain

2017-01-01

The longtime productivity of an industrial machine is improved by condition-based maintenance strategies. To do this, the integration of sensors and other cyber-physical devices is necessary in order to capture and analyze a machine’s condition through its lifespan. Thus, choosing the best sensor is a critical step to ensure the efficiency of the maintenance process. Indeed, considering the variety of sensors, and their features and performance, a formal classification of a sensor’s domain knowledge is crucial. This classification facilitates the search for and reuse of solutions during the design of a new maintenance service. Following a Knowledge Management methodology, the paper proposes and develops a new sensor ontology that structures the domain knowledge, covering both theoretical and experimental sensor attributes. An industrial case study is conducted to validate the proposed ontology and to demonstrate its utility as a guideline to ease the search of suitable sensors. Based on the ontology, the final solution will be implemented in a shared repository connected to legacy CAD (computer-aided design) systems. The selection of the best sensor is, firstly, obtained by the matching of application requirements and sensor specifications (that are proposed by this sensor repository). Then, it is refined from the experimentation results. The achieved solution is recorded in the sensor repository for future reuse. As a result, the time and cost of the design process of new condition-based maintenance services is reduced. PMID:28885592

Community-based Ontology Development, Annotation and Discussion with MediaWiki extension Ontokiwi and Ontokiwi-based Ontobedia

PubMed Central

Ong, Edison; He, Yongqun

2016-01-01

Hundreds of biological and biomedical ontologies have been developed to support data standardization, integration and analysis. Although ontologies are typically developed for community usage, community efforts in ontology development are limited. To support ontology visualization, distribution, and community-based annotation and development, we have developed Ontokiwi, an ontology extension to the MediaWiki software. Ontokiwi displays hierarchical classes and ontological axioms. Ontology classes and axioms can be edited and added using Ontokiwi form or MediaWiki source editor. Ontokiwi also inherits MediaWiki features such as Wikitext editing and version control. Based on the Ontokiwi/MediaWiki software package, we have developed Ontobedia, which targets to support community-based development and annotations of biological and biomedical ontologies. As demonstrations, we have loaded the Ontology of Adverse Events (OAE) and the Cell Line Ontology (CLO) into Ontobedia. Our studies showed that Ontobedia was able to achieve expected Ontokiwi features. PMID:27570653

An empirical analysis of ontology reuse in BioPortal.

PubMed

Ochs, Christopher; Perl, Yehoshua; Geller, James; Arabandi, Sivaram; Tudorache, Tania; Musen, Mark A

2017-07-01

Biomedical ontologies often reuse content (i.e., classes and properties) from other ontologies. Content reuse enables a consistent representation of a domain and reusing content can save an ontology author significant time and effort. Prior studies have investigated the existence of reused terms among the ontologies in the NCBO BioPortal, but as of yet there has not been a study investigating how the ontologies in BioPortal utilize reused content in the modeling of their own content. In this study we investigate how 355 ontologies hosted in the NCBO BioPortal reuse content from other ontologies for the purposes of creating new ontology content. We identified 197 ontologies that reuse content. Among these ontologies, 108 utilize reused classes in the modeling of their own classes and 116 utilize reused properties in class restrictions. Current utilization of reuse and quality issues related to reuse are discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

The ontology life cycle: Integrated tools for editing, publishing, peer review, and evolution of ontologies

PubMed Central

Noy, Natalya; Tudorache, Tania; Nyulas, Csongor; Musen, Mark

2010-01-01

Ontologies have become a critical component of many applications in biomedical informatics. However, the landscape of the ontology tools today is largely fragmented, with independent tools for ontology editing, publishing, and peer review: users develop an ontology in an ontology editor, such as Protégé; and publish it on a Web server or in an ontology library, such as BioPortal, in order to share it with the community; they use the tools provided by the library or mailing lists and bug trackers to collect feedback from users. In this paper, we present a set of tools that bring the ontology editing and publishing closer together, in an integrated platform for the entire ontology lifecycle. This integration streamlines the workflow for collaborative development and increases integration between the ontologies themselves through the reuse of terms. PMID:21347039

Effects of an ontology display with history representation on organizational memory information systems.

PubMed

Hwang, Wonil; Salvendy, Gavriel

2005-06-10

Ontologies, as a possible element of organizational memory information systems, appear to support organizational learning. Ontology tools can be used to share knowledge among the members of an organization. However, current ontology-viewing user interfaces of ontology tools do not fully support organizational learning, because most of them lack proper history representation in their display. In this study, a conceptual model was developed that emphasized the role of ontology in the organizational learning cycle and explored the integration of history representation in the ontology display. Based on the experimental results from a split-plot design with 30 participants, two conclusions were derived: first, appropriately selected history representations in the ontology display help users to identify changes in the ontologies; and second, compatibility between types of ontology display and history representation is more important than ontology display and history representation in themselves.

The Intertwining of Enterprise Strategy and Requirements

NASA Astrophysics Data System (ADS)

Loucopoulos, Pericles; Garfield, Joy

Requirements Engineering techniques need to focus not only on the target technical system, as has traditionally been the case, but also on the interplay between business and system functionality. Whether a business wishes to exploit advances in technology to achieve new strategic objectives or to organise work in innovative ways, the process of Requirements Engineering could and should present opportunities for modelling and evaluating the potential impact that technology can bring about to the enterprise.This chapter discusses a co-designing process that offers opportunities of change to both the business and its underlying technical systems, in a synergistic manner. In these design situations some of the most challenging projects involve multiple stakeholders from different participating organisations, subcontractors, divisions etc who may have a diversity of expertise, come from different organisational cultures and often have competing goals. Stakeholders are faced with many different alternative future ‘worlds’ each one demanding a possibly different development strategy.There are acute questions about the potential structure of the new business system and how key variables in this structure could impact on the dynamics of the system. This chapter presents a framework which enables the evaluation of requirements through (a) system dynamics modelling, (b) ontology modelling, (c) scenario modelling and (d) rationale modelling. System dynamics modelling is used to define the behaviour of an enterprise system in terms of four perspectives. Ontology modelling is used to formally define invariant components of the physical and social world within the enterprise domain. Scenario modelling is used to identify critical variables and by quantitatively analyzing the effects of these variables through simulation to better understand the dynamic behaviour of the possible future structures. Rationale modelling is used to assist collaborative discussions when considering either ontology models or scenarios for change, developing maps, which chart the assumptions and reasoning behind key decisions during the requirements process.

Ontology Mappings to Improve Learning Resource Search

ERIC Educational Resources Information Center

Gasevic, Dragan; Hatala, Marek

2006-01-01

This paper proposes an ontology mapping-based framework that allows searching for learning resources using multiple ontologies. The present applications of ontologies in e-learning use various ontologies (eg, domain, curriculum, context), but they do not give a solution on how to interoperate e-learning systems based on different ontologies. The…

Webulous and the Webulous Google Add-On--a web service and application for ontology building from templates.

PubMed

Jupp, Simon; Burdett, Tony; Welter, Danielle; Sarntivijai, Sirarat; Parkinson, Helen; Malone, James

2016-01-01

Authoring bio-ontologies is a task that has traditionally been undertaken by skilled experts trained in understanding complex languages such as the Web Ontology Language (OWL), in tools designed for such experts. As requests for new terms are made, the need for expert ontologists represents a bottleneck in the development process. Furthermore, the ability to rigorously enforce ontology design patterns in large, collaboratively developed ontologies is difficult with existing ontology authoring software. We present Webulous, an application suite for supporting ontology creation by design patterns. Webulous provides infrastructure to specify templates for populating ontology design patterns that get transformed into OWL assertions in a target ontology. Webulous provides programmatic access to the template server and a client application has been developed for Google Sheets that allows templates to be loaded, populated and resubmitted to the Webulous server for processing. The development and delivery of ontologies to the community requires software support that goes beyond the ontology editor. Building ontologies by design patterns and providing simple mechanisms for the addition of new content helps reduce the overall cost and effort required to develop an ontology. The Webulous system provides support for this process and is used as part of the development of several ontologies at the European Bioinformatics Institute.

Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration

PubMed Central

Ong, Edison; Xiang, Zuoshuang; Zhao, Bin; Liu, Yue; Lin, Yu; Zheng, Jie; Mungall, Chris; Courtot, Mélanie; Ruttenberg, Alan; He, Yongqun

2017-01-01

Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies. PMID:27733503

Where to Publish and Find Ontologies? A Survey of Ontology Libraries

PubMed Central

d'Aquin, Mathieu; Noy, Natalya F.

2011-01-01

One of the key promises of the Semantic Web is its potential to enable and facilitate data interoperability. The ability of data providers and application developers to share and reuse ontologies is a critical component of this data interoperability: if different applications and data sources use the same set of well defined terms for describing their domain and data, it will be much easier for them to “talk” to one another. Ontology libraries are the systems that collect ontologies from different sources and facilitate the tasks of finding, exploring, and using these ontologies. Thus ontology libraries can serve as a link in enabling diverse users and applications to discover, evaluate, use, and publish ontologies. In this paper, we provide a survey of the growing—and surprisingly diverse—landscape of ontology libraries. We highlight how the varying scope and intended use of the libraries a ects their features, content, and potential exploitation in applications. From reviewing eleven ontology libraries, we identify a core set of questions that ontology practitioners and users should consider in choosing an ontology library for finding ontologies or publishing their own. We also discuss the research challenges that emerge from this survey, for the developers of ontology libraries to address. PMID:22408576

Ontology-based systematic representation and analysis of traditional Chinese drugs against rheumatism.

PubMed

Liu, Qingping; Wang, Jiahao; Zhu, Yan; He, Yongqun

2017-12-21

Rheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs. In this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e.g., eugenol), which may explain the formation of some TCD AEs. The OCMR could be efficiently queried for useful information using SPARQL scripts. The OCMR ontology was developed to systematically represent 26 traditional anti-rheumatism Chinese drugs and their related information. The OCMR analysis identified possible anti-rheumatism and AE mechanisms of these drugs. Our novel ontology-based approach can also be applied to systematic representation and analysis of other traditional Chinese drugs.

InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.

PubMed

Cheng, Liang; Jiang, Yue; Ju, Hong; Sun, Jie; Peng, Jiajie; Zhou, Meng; Hu, Yang

2018-01-19

Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown. We proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations. The experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.

The Planteome database: an integrated resource for reference ontologies, plant genomics and phenomics

PubMed Central

Cooper, Laurel; Meier, Austin; Laporte, Marie-Angélique; Elser, Justin L; Mungall, Chris; Sinn, Brandon T; Cavaliere, Dario; Carbon, Seth; Dunn, Nathan A; Smith, Barry; Qu, Botong; Preece, Justin; Zhang, Eugene; Todorovic, Sinisa; Gkoutos, Georgios; Doonan, John H; Stevenson, Dennis W; Arnaud, Elizabeth

2018-01-01

Abstract The Planteome project (http://www.planteome.org) provides a suite of reference and species-specific ontologies for plants and annotations to genes and phenotypes. Ontologies serve as common standards for semantic integration of a large and growing corpus of plant genomics, phenomics and genetics data. The reference ontologies include the Plant Ontology, Plant Trait Ontology and the Plant Experimental Conditions Ontology developed by the Planteome project, along with the Gene Ontology, Chemical Entities of Biological Interest, Phenotype and Attribute Ontology, and others. The project also provides access to species-specific Crop Ontologies developed by various plant breeding and research communities from around the world. We provide integrated data on plant traits, phenotypes, and gene function and expression from 95 plant taxa, annotated with reference ontology terms. The Planteome project is developing a plant gene annotation platform; Planteome Noctua, to facilitate community engagement. All the Planteome ontologies are publicly available and are maintained at the Planteome GitHub site (https://github.com/Planteome) for sharing, tracking revisions and new requests. The annotated data are freely accessible from the ontology browser (http://browser.planteome.org/amigo) and our data repository. PMID:29186578

A UML profile for the OBO relation ontology

PubMed Central

2012-01-01

Background Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain. Results We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO). Conclusions The use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology. PMID:23095840

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots.

PubMed

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-03-11

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning.

Enabling online studies of conceptual relationships between medical terms: developing an efficient web platform.

PubMed

Albin, Aaron; Ji, Xiaonan; Borlawsky, Tara B; Ye, Zhan; Lin, Simon; Payne, Philip Ro; Huang, Kun; Xiang, Yang

2014-10-07

The Unified Medical Language System (UMLS) contains many important ontologies in which terms are connected by semantic relations. For many studies on the relationships between biomedical concepts, the use of transitively associated information from ontologies and the UMLS has been shown to be effective. Although there are a few tools and methods available for extracting transitive relationships from the UMLS, they usually have major restrictions on the length of transitive relations or on the number of data sources. Our goal was to design an efficient online platform that enables efficient studies on the conceptual relationships between any medical terms. To overcome the restrictions of available methods and to facilitate studies on the conceptual relationships between medical terms, we developed a Web platform, onGrid, that supports efficient transitive queries and conceptual relationship studies using the UMLS. This framework uses the latest technique in converting natural language queries into UMLS concepts, performs efficient transitive queries, and visualizes the result paths. It also dynamically builds a relationship matrix for two sets of input biomedical terms. We are thus able to perform effective studies on conceptual relationships between medical terms based on their relationship matrix. The advantage of onGrid is that it can be applied to study any two sets of biomedical concept relations and the relations within one set of biomedical concepts. We use onGrid to study the disease-disease relationships in the Online Mendelian Inheritance in Man (OMIM). By crossvalidating our results with an external database, the Comparative Toxicogenomics Database (CTD), we demonstrated that onGrid is effective for the study of conceptual relationships between medical terms. onGrid is an efficient tool for querying the UMLS for transitive relations, studying the relationship between medical terms, and generating hypotheses.

LEGO-MM: LEarning structured model by probabilistic loGic Ontology tree for MultiMedia.

PubMed

Tang, Jinhui; Chang, Shiyu; Qi, Guo-Jun; Tian, Qi; Rui, Yong; Huang, Thomas S

2016-09-22

Recent advances in Multimedia ontology have resulted in a number of concept models, e.g., LSCOM and Mediamill 101, which are accessible and public to other researchers. However, most current research effort still focuses on building new concepts from scratch, very few work explores the appropriate method to construct new concepts upon the existing models already in the warehouse. To address this issue, we propose a new framework in this paper, termed LEGO1-MM, which can seamlessly integrate both the new target training examples and the existing primitive concept models to infer the more complex concept models. LEGOMM treats the primitive concept models as the lego toy to potentially construct an unlimited vocabulary of new concepts. Specifically, we first formulate the logic operations to be the lego connectors to combine existing concept models hierarchically in probabilistic logic ontology trees. Then, we incorporate new target training information simultaneously to efficiently disambiguate the underlying logic tree and correct the error propagation. Extensive experiments are conducted on a large vehicle domain data set from ImageNet. The results demonstrate that LEGO-MM has significantly superior performance over existing state-of-the-art methods, which build new concept models from scratch.

Interoperable cross-domain semantic and geospatial framework for automatic change detection

NASA Astrophysics Data System (ADS)

Kuo, Chiao-Ling; Hong, Jung-Hong

2016-01-01

With the increasingly diverse types of geospatial data established over the last few decades, semantic interoperability in integrated applications has attracted much interest in the field of Geographic Information System (GIS). This paper proposes a new strategy and framework to process cross-domain geodata at the semantic level. This framework leverages the semantic equivalence of concepts between domains through bridge ontology and facilitates the integrated use of different domain data, which has been long considered as an essential superiority of GIS, but is impeded by the lack of understanding about the semantics implicitly hidden in the data. We choose the task of change detection to demonstrate how the introduction of ontology concept can effectively make the integration possible. We analyze the common properties of geodata and change detection factors, then construct rules and summarize possible change scenario for making final decisions. The use of topographic map data to detect changes in land use shows promising success, as far as the improvement of efficiency and level of automation is concerned. We believe the ontology-oriented approach will enable a new way for data integration across different domains from the perspective of semantic interoperability, and even open a new dimensionality for the future GIS.

The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data

PubMed Central

Kang, Hyunseok P.; Borromeo, Charles D.; Berman, Jules J.; Becich, Michael J.

2010-01-01

Background: Tissue microarrays (TMAs) are enormously useful tools for translational research, but incompatibilities in database systems between various researchers and institutions prevent the efficient sharing of data that could help realize their full potential. Resource Description Framework (RDF) provides a flexible method to represent knowledge in triples, which take the form Subject-Predicate-Object. All data resources are described using Uniform Resource Identifiers (URIs), which are global in scope. We present an OWL (Web Ontology Language) schema that expands upon the TMA data exchange specification to address this issue and assist in data sharing and integration. Methods: A minimal OWL schema was designed containing only concepts specific to TMA experiments. More general data elements were incorporated from predefined ontologies such as the NCI thesaurus. URIs were assigned using the Linked Data format. Results: We present examples of files utilizing the schema and conversion of XML data (similar to the TMA DES) to OWL. Conclusion: By utilizing predefined ontologies and global unique identifiers, this OWL schema provides a solution to the limitations of XML, which represents concepts defined in a localized setting. This will help increase the utilization of tissue resources, facilitating collaborative translational research efforts. PMID:20805954

Alignment of ICNP® 2.0 ontology and a proposed INCP® Brazilian ontology.

PubMed

Carvalho, Carina Maris Gaspar; Cubas, Marcia Regina; Malucelli, Andreia; Nóbrega, Maria Miriam Lima da

2014-01-01

to align the International Classification for Nursing Practice (ICNP®) Version 2.0 ontology and a proposed INCP® Brazilian Ontology. document-based, exploratory and descriptive study, the empirical basis of which was provided by the ICNP® 2.0 Ontology and the INCP® Brazilian Ontology. The ontology alignment was performed using a computer tool with algorithms to identify correspondences between concepts, which were organized and analyzed according to their presence or absence, their names, and their sibling, parent, and child classes. there were 2,682 concepts present in the ICNP® 2.0 Ontology that were missing in the Brazilian Ontology; 717 concepts present in the Brazilian Ontology were missing in the ICNP® 2.0 Ontology; and there were 215 pairs of matching concepts. it is believed that the correspondences identified in this study might contribute to the interoperability between the representations of nursing practice elements in ICNP®, thus allowing the standardization of nursing records based on this classification system.

A web-based data-querying tool based on ontology-driven methodology and flowchart-based model.

PubMed

Ping, Xiao-Ou; Chung, Yufang; Tseng, Yi-Ju; Liang, Ja-Der; Yang, Pei-Ming; Huang, Guan-Tarn; Lai, Feipei

2013-10-08

Because of the increased adoption rate of electronic medical record (EMR) systems, more health care records have been increasingly accumulating in clinical data repositories. Therefore, querying the data stored in these repositories is crucial for retrieving the knowledge from such large volumes of clinical data. The aim of this study is to develop a Web-based approach for enriching the capabilities of the data-querying system along the three following considerations: (1) the interface design used for query formulation, (2) the representation of query results, and (3) the models used for formulating query criteria. The Guideline Interchange Format version 3.5 (GLIF3.5), an ontology-driven clinical guideline representation language, was used for formulating the query tasks based on the GLIF3.5 flowchart in the Protégé environment. The flowchart-based data-querying model (FBDQM) query execution engine was developed and implemented for executing queries and presenting the results through a visual and graphical interface. To examine a broad variety of patient data, the clinical data generator was implemented to automatically generate the clinical data in the repository, and the generated data, thereby, were employed to evaluate the system. The accuracy and time performance of the system for three medical query tasks relevant to liver cancer were evaluated based on the clinical data generator in the experiments with varying numbers of patients. In this study, a prototype system was developed to test the feasibility of applying a methodology for building a query execution engine using FBDQMs by formulating query tasks using the existing GLIF. The FBDQM-based query execution engine was used to successfully retrieve the clinical data based on the query tasks formatted using the GLIF3.5 in the experiments with varying numbers of patients. The accuracy of the three queries (ie, "degree of liver damage," "degree of liver damage when applying a mutually exclusive setting," and "treatments for liver cancer") was 100% for all four experiments (10 patients, 100 patients, 1000 patients, and 10,000 patients). Among the three measured query phases, (1) structured query language operations, (2) criteria verification, and (3) other, the first two had the longest execution time. The ontology-driven FBDQM-based approach enriched the capabilities of the data-querying system. The adoption of the GLIF3.5 increased the potential for interoperability, shareability, and reusability of the query tasks.

Semantics and metaphysics in informatics: toward an ontology of tasks.

PubMed

Figdor, Carrie

2011-04-01

This article clarifies three principles that should guide the development of any cognitive ontology. First, that an adequate cognitive ontology depends essentially on an adequate task ontology; second, that the goal of developing a cognitive ontology is independent of the goal of finding neural implementations of the processes referred to in the ontology; and third, that cognitive ontologies are neutral regarding the metaphysical relationship between cognitive and neural processes. Copyright © 2011 Cognitive Science Society, Inc.

Ontobee: A linked ontology data server to support ontology term dereferencing, linkage, query and integration.

PubMed

Ong, Edison; Xiang, Zuoshuang; Zhao, Bin; Liu, Yue; Lin, Yu; Zheng, Jie; Mungall, Chris; Courtot, Mélanie; Ruttenberg, Alan; He, Yongqun

2017-01-04

Linked Data (LD) aims to achieve interconnected data by representing entities using Unified Resource Identifiers (URIs), and sharing information using Resource Description Frameworks (RDFs) and HTTP. Ontologies, which logically represent entities and relations in specific domains, are the basis of LD. Ontobee (http://www.ontobee.org/) is a linked ontology data server that stores ontology information using RDF triple store technology and supports query, visualization and linkage of ontology terms. Ontobee is also the default linked data server for publishing and browsing biomedical ontologies in the Open Biological Ontology (OBO) Foundry (http://obofoundry.org) library. Ontobee currently hosts more than 180 ontologies (including 131 OBO Foundry Library ontologies) with over four million terms. Ontobee provides a user-friendly web interface for querying and visualizing the details and hierarchy of a specific ontology term. Using the eXtensible Stylesheet Language Transformation (XSLT) technology, Ontobee is able to dereference a single ontology term URI, and then output RDF/eXtensible Markup Language (XML) for computer processing or display the HTML information on a web browser for human users. Statistics and detailed information are generated and displayed for each ontology listed in Ontobee. In addition, a SPARQL web interface is provided for custom advanced SPARQL queries of one or multiple ontologies. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

DeMO: An Ontology for Discrete-event Modeling and Simulation.

PubMed

Silver, Gregory A; Miller, John A; Hybinette, Maria; Baramidze, Gregory; York, William S

2011-09-01

Several fields have created ontologies for their subdomains. For example, the biological sciences have developed extensive ontologies such as the Gene Ontology, which is considered a great success. Ontologies could provide similar advantages to the Modeling and Simulation community. They provide a way to establish common vocabularies and capture knowledge about a particular domain with community-wide agreement. Ontologies can support significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved knowledge discovery capabilities. This paper discusses the design and development of an ontology for Modeling and Simulation called the Discrete-event Modeling Ontology (DeMO), and it presents prototype applications that demonstrate various uses and benefits that such an ontology may provide to the Modeling and Simulation community.

DeMO: An Ontology for Discrete-event Modeling and Simulation

PubMed Central

Silver, Gregory A; Miller, John A; Hybinette, Maria; Baramidze, Gregory; York, William S

2011-01-01

Several fields have created ontologies for their subdomains. For example, the biological sciences have developed extensive ontologies such as the Gene Ontology, which is considered a great success. Ontologies could provide similar advantages to the Modeling and Simulation community. They provide a way to establish common vocabularies and capture knowledge about a particular domain with community-wide agreement. Ontologies can support significantly improved (semantic) search and browsing, integration of heterogeneous information sources, and improved knowledge discovery capabilities. This paper discusses the design and development of an ontology for Modeling and Simulation called the Discrete-event Modeling Ontology (DeMO), and it presents prototype applications that demonstrate various uses and benefits that such an ontology may provide to the Modeling and Simulation community. PMID:22919114

Biomedical ontologies: toward scientific debate.

PubMed

Maojo, V; Crespo, J; García-Remesal, M; de la Iglesia, D; Perez-Rey, D; Kulikowski, C

2011-01-01

Biomedical ontologies have been very successful in structuring knowledge for many different applications, receiving widespread praise for their utility and potential. Yet, the role of computational ontologies in scientific research, as opposed to knowledge management applications, has not been extensively discussed. We aim to stimulate further discussion on the advantages and challenges presented by biomedical ontologies from a scientific perspective. We review various aspects of biomedical ontologies going beyond their practical successes, and focus on some key scientific questions in two ways. First, we analyze and discuss current approaches to improve biomedical ontologies that are based largely on classical, Aristotelian ontological models of reality. Second, we raise various open questions about biomedical ontologies that require further research, analyzing in more detail those related to visual reasoning and spatial ontologies. We outline significant scientific issues that biomedical ontologies should consider, beyond current efforts of building practical consensus between them. For spatial ontologies, we suggest an approach for building "morphospatial" taxonomies, as an example that could stimulate research on fundamental open issues for biomedical ontologies. Analysis of a large number of problems with biomedical ontologies suggests that the field is very much open to alternative interpretations of current work, and in need of scientific debate and discussion that can lead to new ideas and research directions.

What Four Million Mappings Can Tell You about Two Hundred Ontologies

NASA Astrophysics Data System (ADS)

Ghazvinian, Amir; Noy, Natalya F.; Jonquet, Clement; Shah, Nigam; Musen, Mark A.

The field of biomedicine has embraced the Semantic Web probably more than any other field. As a result, there is a large number of biomedical ontologies covering overlapping areas of the field. We have developed BioPortal—an open community-based repository of biomedical ontologies. We analyzed ontologies and terminologies in BioPortal and the Unified Medical Language System (UMLS), creating more than 4 million mappings between concepts in these ontologies and terminologies based on the lexical similarity of concept names and synonyms. We then analyzed the mappings and what they tell us about the ontologies themselves, the structure of the ontology repository, and the ways in which the mappings can help in the process of ontology design and evaluation. For example, we can use the mappings to guide users who are new to a field to the most pertinent ontologies in that field, to identify areas of the domain that are not covered sufficiently by the ontologies in the repository, and to identify which ontologies will serve well as background knowledge in domain-specific tools. While we used a specific (but large) ontology repository for the study, we believe that the lessons we learned about the value of a large-scale set of mappings to ontology users and developers are general and apply in many other domains.

A Statistical Ontology-Based Approach to Ranking for Multiword Search

ERIC Educational Resources Information Center

Kim, Jinwoo

2013-01-01

Keyword search is a prominent data retrieval method for the Web, largely because the simple and efficient nature of keyword processing allows a large amount of information to be searched with fast response. However, keyword search approaches do not formally capture the clear meaning of a keyword query and fail to address the semantic relationships…

A Semantic-Oriented Approach for Organizing and Developing Annotation for E-Learning

ERIC Educational Resources Information Center

Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D.

2011-01-01

This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…

Using AberOWL for fast and scalable reasoning over BioPortal ontologies.

PubMed

Slater, Luke; Gkoutos, Georgios V; Schofield, Paul N; Hoehndorf, Robert

2016-08-08

Reasoning over biomedical ontologies using their OWL semantics has traditionally been a challenging task due to the high theoretical complexity of OWL-based automated reasoning. As a consequence, ontology repositories, as well as most other tools utilizing ontologies, either provide access to ontologies without use of automated reasoning, or limit the number of ontologies for which automated reasoning-based access is provided. We apply the AberOWL infrastructure to provide automated reasoning-based access to all accessible and consistent ontologies in BioPortal (368 ontologies). We perform an extensive performance evaluation to determine query times, both for queries of different complexity and for queries that are performed in parallel over the ontologies. We demonstrate that, with the exception of a few ontologies, even complex and parallel queries can now be answered in milliseconds, therefore allowing automated reasoning to be used on a large scale, to run in parallel, and with rapid response times.

Publishing and Editing of Semantically-Enabled Scientific Metadata Across Multiple Web Platforms: Challenges and Experiences

NASA Astrophysics Data System (ADS)

Patton, E. W.; West, P.; Greer, R.; Jin, B.

2011-12-01

Following on work presented at the 2010 AGU Fall Meeting, we present a number of real-world collections of semantically-enabled scientific metadata ingested into the Tetherless World RDF2HTML system as structured data and presented and edited using that system. Two separate datasets from two different domains (oceanography and solar sciences) are made available using existing web standards and services, e.g. encoded using ontologies represented with the Web Ontology Language (OWL) and stored in a SPARQL endpoint for querying. These datasets are deployed for use in three different web environments, i.e. Drupal, MediaWiki, and a custom web portal written in Java, to highlight the cross-platform nature of the data presentation. Stylesheets used to transform concepts in each domain as well as shared terms into HTML will be presented to show the power of using common ontologies to publish data and support reuse of existing terminologies. In addition, a single domain dataset is shared between two separate portal instances to demonstrate the ability for this system to offer distributed access and modification of content across the Internet. Lastly, we will highlight challenges that arose in the software engineering process, outline the design choices we made in solving those issues, and discuss how future improvements to this and other systems will enable the evolution of distributed, decentralized collaborations for scientific data sharing across multiple research groups.

Clinical modeling--a critical analysis.

PubMed

Blobel, Bernd; Goossen, William; Brochhausen, Mathias

2014-01-01

Modeling clinical processes (and their informational representation) is a prerequisite for optimally enabling and supporting high quality and safe care through information and communication technology and meaningful use of gathered information. The paper investigates existing approaches to clinical modeling, thereby systematically analyzing the underlying principles, the consistency with and the integration opportunity to other existing or emerging projects, as well as the correctness of representing the reality of health and health services. The analysis is performed using an architectural framework for modeling real-world systems. In addition, fundamental work on the representation of facts, relations, and processes in the clinical domain by ontologies is applied, thereby including the integration of advanced methodologies such as translational and system medicine. The paper demonstrates fundamental weaknesses and different maturity as well as evolutionary potential in the approaches considered. It offers a development process starting with the business domain and its ontologies, continuing with the Reference Model-Open Distributed Processing (RM-ODP) related conceptual models in the ICT ontology space, the information and the computational view, and concluding with the implementation details represented as engineering and technology view, respectively. The existing approaches reflect at different levels the clinical domain, put the main focus on different phases of the development process instead of first establishing the real business process representation and therefore enable quite differently and partially limitedly the domain experts' involvement. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Semi-automatic Data Integration using Karma

NASA Astrophysics Data System (ADS)

Garijo, D.; Kejriwal, M.; Pierce, S. A.; Houser, P. I. Q.; Peckham, S. D.; Stanko, Z.; Hardesty Lewis, D.; Gil, Y.; Pennington, D. D.; Knoblock, C.

2017-12-01

Data integration applications are ubiquitous in scientific disciplines. A state-of-the-art data integration system accepts both a set of data sources and a target ontology as input, and semi-automatically maps the data sources in terms of concepts and relationships in the target ontology. Mappings can be both complex and highly domain-specific. Once such a semantic model, expressing the mapping using community-wide standard, is acquired, the source data can be stored in a single repository or database using the semantics of the target ontology. However, acquiring the mapping is a labor-prone process, and state-of-the-art artificial intelligence systems are unable to fully automate the process using heuristics and algorithms alone. Instead, a more realistic goal is to develop adaptive tools that minimize user feedback (e.g., by offering good mapping recommendations), while at the same time making it intuitive and easy for the user to both correct errors and to define complex mappings. We present Karma, a data integration system that has been developed over multiple years in the information integration group at the Information Sciences Institute, a research institute at the University of Southern California's Viterbi School of Engineering. Karma is a state-of-the-art data integration tool that supports an interactive graphical user interface, and has been featured in multiple domains over the last five years, including geospatial, biological, humanities and bibliographic applications. Karma allows a user to import their own ontology and datasets using widely used formats such as RDF, XML, CSV and JSON, can be set up either locally or on a server, supports a native backend database for prototyping queries, and can even be seamlessly integrated into external computational pipelines, including those ingesting data via streaming data sources, Web APIs and SQL databases. We illustrate a Karma workflow at a conceptual level, along with a live demo, and show use cases of Karma specifically for the geosciences. In particular, we show how Karma can be used intuitively to obtain the mapping model between case study data sources and a publicly available and expressive target ontology that has been designed to capture a broad set of concepts in geoscience with standardized, easily searchable names.

Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of Cerebrotendinous xanthomatosis.

PubMed

Taboada, María; Martínez, Diego; Pilo, Belén; Jiménez-Escrig, Adriano; Robinson, Peter N; Sobrido, María J

2012-07-31

Semantic Web technology can considerably catalyze translational genetics and genomics research in medicine, where the interchange of information between basic research and clinical levels becomes crucial. This exchange involves mapping abstract phenotype descriptions from research resources, such as knowledge databases and catalogs, to unstructured datasets produced through experimental methods and clinical practice. This is especially true for the construction of mutation databases. This paper presents a way of harmonizing abstract phenotype descriptions with patient data from clinical practice, and querying this dataset about relationships between phenotypes and genetic variants, at different levels of abstraction. Due to the current availability of ontological and terminological resources that have already reached some consensus in biomedicine, a reuse-based ontology engineering approach was followed. The proposed approach uses the Ontology Web Language (OWL) to represent the phenotype ontology and the patient model, the Semantic Web Rule Language (SWRL) to bridge the gap between phenotype descriptions and clinical data, and the Semantic Query Web Rule Language (SQWRL) to query relevant phenotype-genotype bidirectional relationships. The work tests the use of semantic web technology in the biomedical research domain named cerebrotendinous xanthomatosis (CTX), using a real dataset and ontologies. A framework to query relevant phenotype-genotype bidirectional relationships is provided. Phenotype descriptions and patient data were harmonized by defining 28 Horn-like rules in terms of the OWL concepts. In total, 24 patterns of SWQRL queries were designed following the initial list of competency questions. As the approach is based on OWL, the semantic of the framework adapts the standard logical model of an open world assumption. This work demonstrates how semantic web technologies can be used to support flexible representation and computational inference mechanisms required to query patient datasets at different levels of abstraction. The open world assumption is especially good for describing only partially known phenotype-genotype relationships, in a way that is easily extensible. In future, this type of approach could offer researchers a valuable resource to infer new data from patient data for statistical analysis in translational research. In conclusion, phenotype description formalization and mapping to clinical data are two key elements for interchanging knowledge between basic and clinical research.

Semantic Data Access Services at NASA's Atmospheric Science Data Center

NASA Astrophysics Data System (ADS)

Huffer, E.; Hertz, J.; Kusterer, J.

2012-12-01

The corpus of Earth Science data products at the Atmospheric Science Data Center at NASA's Langley Research Center comprises a widely heterogeneous set of products, even among those whose subject matter is very similar. Two distinct data products may both contain data on the same parameter, for instance, solar irradiance; but the instruments used, and the circumstances under which the data were collected and processed, may differ significantly. Understanding the differences is critical to using the data effectively. Data distribution services must be able to provide prospective users with enough information to allow them to meaningfully compare and evaluate the data products offered. Semantic technologies - ontologies, triple stores, reasoners, linked data - offer functionality for addressing this issue. Ontologies can provide robust, high-fidelity domain models that serve as common schema for discovering, evaluating, comparing and integrating data from disparate products. Reasoning engines and triple stores can leverage ontologies to support intelligent search applications that allow users to discover, query, retrieve, and easily reformat data from a broad spectrum of sources. We argue that because of the extremely complex nature of scientific data, data distribution systems should wholeheartedly embrace semantic technologies in order to make their data accessible to a broad array of prospective end users, and to ensure that the data they provide will be clearly understood and used appropriately by consumers. Toward this end, we propose a distribution system in which formal ontological models that accurately and comprehensively represent the ASDC's data domain, and fully leverage the expressivity and inferential capabilities of first order logic, are used to generate graph-based representations of the relevant relationships among data sets, observational systems, metadata files, and geospatial, temporal and scientific parameters to help prospective data consumers navigate directly to relevant data sets and query, subset, retrieve and compare the measurement and calculation data they contain. A critical part of developing semantically-enabled data distribution capabilities is developing an ontology that adequately describes 1) the data products - their structure, their content, and any supporting documentation; 2) the data domain - the objects and processes that the products denote; and 3) the relationship between the data and the domain. The ontology, in addition, should be machine readable and capable of integrating with the larger data distribution system to provide an interactive user experience. We will demonstrate how a formal, high-fidelity, queriable ontology representing the atmospheric science domain objects and data products, together with a robust set of inference rules for generating interactive graphs, allows researchers to navigate quickly and painlessly through the large volume of data at the ASDC. Scientists will be able to discover data products that exactly meet their particular criteria, link to information about the instruments and processing methods that generated the data; and compare and contrast related products.

Combined use of semantics and metadata to manage Research Data Life Cycle in Environmental Sciences

NASA Astrophysics Data System (ADS)

Aguilar Gómez, Fernando; de Lucas, Jesús Marco; Pertinez, Esther; Palacio, Aida

2017-04-01

The use of metadata to contextualize datasets is quite extended in Earth System Sciences. There are some initiatives and available tools to help data managers to choose the best metadata standard that fit their use cases, like the DCC Metadata Directory (http://www.dcc.ac.uk/resources/metadata-standards). In our use case, we have been gathering physical, chemical and biological data from a water reservoir since 2010. A well metadata definition is crucial not only to contextualize our own data but also to integrate datasets from other sources like satellites or meteorological agencies. That is why we have chosen EML (Ecological Metadata Language), which integrates many different elements to define a dataset, including the project context, instrumentation and parameters definition, and the software used to process, provide quality controls and include the publication details. Those metadata elements can contribute to help both human and machines to understand and process the dataset. However, the use of metadata is not enough to fully support the data life cycle, from the Data Management Plan definition to the Publication and Re-use. To do so, we need to define not only metadata and attributes but also the relationships between them, so semantics are needed. Ontologies, being a knowledge representation, can contribute to define the elements of a research data life cycle, including DMP, datasets, software, etc. They also can define how the different elements are related between them and how they interact. The first advantage of developing an ontology of a knowledge domain is that they provide a common vocabulary hierarchy (i.e. a conceptual schema) that can be used and standardized by all the agents interested in the domain (either humans or machines). This way of using ontologies is one of the basis of the Semantic Web, where ontologies are set to play a key role in establishing a common terminology between agents. To develop an ontology we are using a graphical tool Protégé, which is a graphical ontology-development tool that supports a rich knowledge model and it is open-source and freely available. To process and manage the ontology, we are using Semantic MediaWiki, which is able to process queries. Semantic MediaWiki is an extension of MediaWiki where we can do semantic search and export data in RDF. Our final goal is integrating our data repository portal and semantic processing engine in order to have a complete system to manage the data life cycle stages and their relationships, including machine-actionable DMP solution, datasets and software management, computing resources for processing and analysis and publication features (DOI mint). This way we will be able to reproduce the full data life cycle chain warranting the FAIR+R principles.

MBSE-Driven Visualization of Requirements Allocation and Traceability

NASA Technical Reports Server (NTRS)

Jackson, Maddalena; Wilkerson, Marcus

2016-01-01

In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well-formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA's proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.

COEUS: “semantic web in a box” for biomedical applications

PubMed Central

2012-01-01

Background As the “omics” revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter’s complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. Results COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a “semantic web in a box” approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. Conclusions The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/. PMID:23244467

COEUS: "semantic web in a box" for biomedical applications.

PubMed

Lopes, Pedro; Oliveira, José Luís

2012-12-17

As the "omics" revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter's complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a "semantic web in a box" approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/.

Ontology-Oriented Programming for Biomedical Informatics.

PubMed

Lamy, Jean-Baptiste

2016-01-01

Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites. Two main approaches have been proposed for accessing ontologies in a computer program: traditional API (Application Programming Interface) and ontology-oriented programming, either static or dynamic. In this paper, we will review these approaches and discuss their appropriateness for biomedical ontologies. We will also present an experience feedback about the integration of an ontology in a computer software during the VIIIP research project. Finally, we will present OwlReady, the solution we developed.

SWARMs Ontology: A Common Information Model for the Cooperation of Underwater Robots

PubMed Central

Li, Xin; Bilbao, Sonia; Martín-Wanton, Tamara; Bastos, Joaquim; Rodriguez, Jonathan

2017-01-01

In order to facilitate cooperation between underwater robots, it is a must for robots to exchange information with unambiguous meaning. However, heterogeneity, existing in information pertaining to different robots, is a major obstruction. Therefore, this paper presents a networked ontology, named the Smart and Networking Underwater Robots in Cooperation Meshes (SWARMs) ontology, to address information heterogeneity and enable robots to have the same understanding of exchanged information. The SWARMs ontology uses a core ontology to interrelate a set of domain-specific ontologies, including the mission and planning, the robotic vehicle, the communication and networking, and the environment recognition and sensing ontology. In addition, the SWARMs ontology utilizes ontology constructs defined in the PR-OWL ontology to annotate context uncertainty based on the Multi-Entity Bayesian Network (MEBN) theory. Thus, the SWARMs ontology can provide both a formal specification for information that is necessarily exchanged between robots and a command and control entity, and also support for uncertainty reasoning. A scenario on chemical pollution monitoring is described and used to showcase how the SWARMs ontology can be instantiated, be extended, represent context uncertainty, and support uncertainty reasoning. PMID:28287468

BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications.

PubMed

Whetzel, Patricia L; Noy, Natalya F; Shah, Nigam H; Alexander, Paul R; Nyulas, Csongor; Tudorache, Tania; Musen, Mark A

2011-07-01

The National Center for Biomedical Ontology (NCBO) is one of the National Centers for Biomedical Computing funded under the NIH Roadmap Initiative. Contributing to the national computing infrastructure, NCBO has developed BioPortal, a web portal that provides access to a library of biomedical ontologies and terminologies (http://bioportal.bioontology.org) via the NCBO Web services. BioPortal enables community participation in the evaluation and evolution of ontology content by providing features to add mappings between terms, to add comments linked to specific ontology terms and to provide ontology reviews. The NCBO Web services (http://www.bioontology.org/wiki/index.php/NCBO_REST_services) enable this functionality and provide a uniform mechanism to access ontologies from a variety of knowledge representation formats, such as Web Ontology Language (OWL) and Open Biological and Biomedical Ontologies (OBO) format. The Web services provide multi-layered access to the ontology content, from getting all terms in an ontology to retrieving metadata about a term. Users can easily incorporate the NCBO Web services into software applications to generate semantically aware applications and to facilitate structured data collection.

A unified approach for debugging is-a structure and mappings in networked taxonomies

PubMed Central

2013-01-01

Background With the increased use of ontologies and ontology mappings in semantically-enabled applications such as ontology-based search and data integration, the issue of detecting and repairing defects in ontologies and ontology mappings has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Results We propose a unified framework for debugging the is-a structure of and mappings between taxonomies, the most used kind of ontologies. We present theory and algorithms as well as an implemented system RepOSE, that supports a domain expert in detecting and repairing missing and wrong is-a relations and mappings. We also discuss two experiments performed by domain experts: an experiment on the Anatomy ontologies from the Ontology Alignment Evaluation Initiative, and a debugging session for the Swedish National Food Agency. Conclusions Semantically-enabled applications need high quality ontologies and ontology mappings. One key aspect is the detection and removal of defects in the ontologies and ontology mappings. Our system RepOSE provides an environment that supports domain experts to deal with this issue. We have shown the usefulness of the approach in two experiments by detecting and repairing circa 200 and 30 defects, respectively. PMID:23548155

SPONGY (SPam ONtoloGY): Email Classification Using Two-Level Dynamic Ontology

PubMed Central

2014-01-01

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user's background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance. PMID:25254240

Ontologies as integrative tools for plant science

PubMed Central

Walls, Ramona L.; Athreya, Balaji; Cooper, Laurel; Elser, Justin; Gandolfo, Maria A.; Jaiswal, Pankaj; Mungall, Christopher J.; Preece, Justin; Rensing, Stefan; Smith, Barry; Stevenson, Dennis W.

2012-01-01

Premise of the study Bio-ontologies are essential tools for accessing and analyzing the rapidly growing pool of plant genomic and phenomic data. Ontologies provide structured vocabularies to support consistent aggregation of data and a semantic framework for automated analyses and reasoning. They are a key component of the semantic web. Methods This paper provides background on what bio-ontologies are, why they are relevant to botany, and the principles of ontology development. It includes an overview of ontologies and related resources that are relevant to plant science, with a detailed description of the Plant Ontology (PO). We discuss the challenges of building an ontology that covers all green plants (Viridiplantae). Key results Ontologies can advance plant science in four keys areas: (1) comparative genetics, genomics, phenomics, and development; (2) taxonomy and systematics; (3) semantic applications; and (4) education. Conclusions Bio-ontologies offer a flexible framework for comparative plant biology, based on common botanical understanding. As genomic and phenomic data become available for more species, we anticipate that the annotation of data with ontology terms will become less centralized, while at the same time, the need for cross-species queries will become more common, causing more researchers in plant science to turn to ontologies. PMID:22847540

SPONGY (SPam ONtoloGY): email classification using two-level dynamic ontology.

PubMed

Youn, Seongwook

2014-01-01

Email is one of common communication methods between people on the Internet. However, the increase of email misuse/abuse has resulted in an increasing volume of spam emails over recent years. An experimental system has been designed and implemented with the hypothesis that this method would outperform existing techniques, and the experimental results showed that indeed the proposed ontology-based approach improves spam filtering accuracy significantly. In this paper, two levels of ontology spam filters were implemented: a first level global ontology filter and a second level user-customized ontology filter. The use of the global ontology filter showed about 91% of spam filtered, which is comparable with other methods. The user-customized ontology filter was created based on the specific user's background as well as the filtering mechanism used in the global ontology filter creation. The main contributions of the paper are (1) to introduce an ontology-based multilevel filtering technique that uses both a global ontology and an individual filter for each user to increase spam filtering accuracy and (2) to create a spam filter in the form of ontology, which is user-customized, scalable, and modularized, so that it can be embedded to many other systems for better performance.

Measuring the level of activity in community built bio-ontologies.

PubMed

Malone, James; Stevens, Robert

2013-02-01

In this paper we explore the measurement of activity in ontology projects as an aspect of community ontology building. When choosing whether to use an ontology or whether to participate in its development, having some knowledge of how actively that ontology is developed is an important issue. Our knowledge of biology grows and changes and an ontology must adapt to keep pace with those changes and also adapt with respect to other ontologies and organisational principles. In essence, we need to know if there is an 'active' community involved with a project or whether a given ontology is inactive or moribund. We explore the use of additions, deletions and changes to ontology files, the regularity and frequency of releases, and the number of ontology repository updates to an ontology as the basis for measuring activity in an ontology. We present our results of this study, which show a dramatic range of activity across some of the more prominent community ontologies, illustrating very active and mature efforts through to those which appear to have become dormant for a number of possible reasons. We show that global activity within the community has remained at a similar level over the last 2 years. Measuring additions, deletions and changes, together with release frequency, appear to be useful metrics of activity and useful pointers towards future behaviour. Measuring who is making edits to ontologies is harder to capture; this raises issues of record keeping in ontology projects and in micro-credit, although we have identified one ontologist that appears influential across many community efforts; a Super-Ontologist. We also discuss confounding factors in our activity metric and discuss how it can be improved and adopted as an assessment criterion for community ontology development. Overall, we show that it is possible to objectively measure the activity in an ontology and to make some prediction about future activity. Copyright © 2012 Elsevier Inc. All rights reserved.

Evaluating the Good Ontology Design Guideline (GoodOD) with the Ontology Quality Requirements and Evaluation Method and Metrics (OQuaRE)

PubMed Central

Duque-Ramos, Astrid; Boeker, Martin; Jansen, Ludger; Schulz, Stefan; Iniesta, Miguela; Fernández-Breis, Jesualdo Tomás

2014-01-01

Objective To (1) evaluate the GoodOD guideline for ontology development by applying the OQuaRE evaluation method and metrics to the ontology artefacts that were produced by students in a randomized controlled trial, and (2) informally compare the OQuaRE evaluation method with gold standard and competency questions based evaluation methods, respectively. Background In the last decades many methods for ontology construction and ontology evaluation have been proposed. However, none of them has become a standard and there is no empirical evidence of comparative evaluation of such methods. This paper brings together GoodOD and OQuaRE. GoodOD is a guideline for developing robust ontologies. It was previously evaluated in a randomized controlled trial employing metrics based on gold standard ontologies and competency questions as outcome parameters. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies and has been successfully used for evaluating the quality of ontologies. Methods In this paper, we evaluate the effect of training in ontology construction based on the GoodOD guideline within the OQuaRE quality evaluation framework and compare the results with those obtained for the previous studies based on the same data. Results Our results show a significant effect of the GoodOD training over developed ontologies by topics: (a) a highly significant effect was detected in three topics from the analysis of the ontologies of untrained and trained students; (b) both positive and negative training effects with respect to the gold standard were found for five topics. Conclusion The GoodOD guideline had a significant effect over the quality of the ontologies developed. Our results show that GoodOD ontologies can be effectively evaluated using OQuaRE and that OQuaRE is able to provide additional useful information about the quality of the GoodOD ontologies. PMID:25148262

Light-Weighted Automatic Import of Standardized Ontologies into the Content Management System Drupal.

PubMed

Beger, Christoph; Uciteli, Alexandr; Herre, Heinrich

2017-01-01

The amount of ontologies, which are utilizable for widespread domains, is growing steadily. BioPortal alone, embraces over 500 published ontologies with nearly 8 million classes. In contrast, the vast informative content of these ontologies is only directly intelligible by experts. To overcome this deficiency it could be possible to represent ontologies as web portals, which does not require knowledge about ontologies and their semantics, but still carries as much information as possible to the end-user. Furthermore, the conception of a complex web portal is a sophisticated process. Many entities must be analyzed and linked to existing terminologies. Ontologies are a decent solution for gathering and storing this complex data and dependencies. Hence, automated imports of ontologies into web portals could support both mentioned scenarios. The Content Management System (CMS) Drupal 8 is one of many solutions to develop web presentations with less required knowledge about programming languages and it is suitable to represent ontological entities. We developed the Drupal Upper Ontology (DUO), which models concepts of Drupal's architecture, such as nodes, vocabularies and links. DUO can be imported into ontologies to map their entities to Drupal's concepts. Because of Drupal's lack of import capabilities, we implemented the Simple Ontology Loader in Drupal (SOLID), a Drupal 8 module, which allows Drupal administrators to import ontologies based on DUO. Our module generates content in Drupal from existing ontologies and makes it accessible by the general public. Moreover Drupal offers a tagging system which may be amplified with multiple standardized and established terminologies by importing them with SOLID. Our Drupal module shows that ontologies can be used to model content of a CMS and vice versa CMS are suitable to represent ontologies in a user-friendly way. Ontological entities are presented to the user as discrete pages with all appropriate properties, links and tags.

Evaluating the Good Ontology Design Guideline (GoodOD) with the ontology quality requirements and evaluation method and metrics (OQuaRE).

PubMed

Duque-Ramos, Astrid; Boeker, Martin; Jansen, Ludger; Schulz, Stefan; Iniesta, Miguela; Fernández-Breis, Jesualdo Tomás

2014-01-01

To (1) evaluate the GoodOD guideline for ontology development by applying the OQuaRE evaluation method and metrics to the ontology artefacts that were produced by students in a randomized controlled trial, and (2) informally compare the OQuaRE evaluation method with gold standard and competency questions based evaluation methods, respectively. In the last decades many methods for ontology construction and ontology evaluation have been proposed. However, none of them has become a standard and there is no empirical evidence of comparative evaluation of such methods. This paper brings together GoodOD and OQuaRE. GoodOD is a guideline for developing robust ontologies. It was previously evaluated in a randomized controlled trial employing metrics based on gold standard ontologies and competency questions as outcome parameters. OQuaRE is a method for ontology quality evaluation which adapts the SQuaRE standard for software product quality to ontologies and has been successfully used for evaluating the quality of ontologies. In this paper, we evaluate the effect of training in ontology construction based on the GoodOD guideline within the OQuaRE quality evaluation framework and compare the results with those obtained for the previous studies based on the same data. Our results show a significant effect of the GoodOD training over developed ontologies by topics: (a) a highly significant effect was detected in three topics from the analysis of the ontologies of untrained and trained students; (b) both positive and negative training effects with respect to the gold standard were found for five topics. The GoodOD guideline had a significant effect over the quality of the ontologies developed. Our results show that GoodOD ontologies can be effectively evaluated using OQuaRE and that OQuaRE is able to provide additional useful information about the quality of the GoodOD ontologies.

Ontology for Vector Surveillance and Management

PubMed Central

LOZANO-FUENTES, SAUL; BANDYOPADHYAY, ARITRA; COWELL, LINDSAY G.; GOLDFAIN, ALBERT; EISEN, LARS

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an “umbrella” for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a “term tree” to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has_vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO *.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use. PMID:23427646

Ontology for vector surveillance and management.

PubMed

Lozano-Fuentes, Saul; Bandyopadhyay, Aritra; Cowell, Lindsay G; Goldfain, Albert; Eisen, Lars

2013-01-01

Ontologies, which are made up by standardized and defined controlled vocabulary terms and their interrelationships, are comprehensive and readily searchable repositories for knowledge in a given domain. The Open Biomedical Ontologies (OBO) Foundry was initiated in 2001 with the aims of becoming an "umbrella" for life-science ontologies and promoting the use of ontology development best practices. A software application (OBO-Edit; *.obo file format) was developed to facilitate ontology development and editing. The OBO Foundry now comprises over 100 ontologies and candidate ontologies, including the NCBI organismal classification ontology (NCBITaxon), the Mosquito Insecticide Resistance Ontology (MIRO), the Infectious Disease Ontology (IDO), the IDOMAL malaria ontology, and ontologies for mosquito gross anatomy and tick gross anatomy. We previously developed a disease data management system for dengue and malaria control programs, which incorporated a set of information trees built upon ontological principles, including a "term tree" to promote the use of standardized terms. In the course of doing so, we realized that there were substantial gaps in existing ontologies with regards to concepts, processes, and, especially, physical entities (e.g., vector species, pathogen species, and vector surveillance and management equipment) in the domain of surveillance and management of vectors and vector-borne pathogens. We therefore produced an ontology for vector surveillance and management, focusing on arthropod vectors and vector-borne pathogens with relevance to humans or domestic animals, and with special emphasis on content to support operational activities through inclusion in databases, data management systems, or decision support systems. The Vector Surveillance and Management Ontology (VSMO) includes >2,200 unique terms, of which the vast majority (>80%) were newly generated during the development of this ontology. One core feature of the VSMO is the linkage, through the has vector relation, of arthropod species to the pathogenic microorganisms for which they serve as biological vectors. We also recognized and addressed a potential roadblock for use of the VSMO by the vector-borne disease community: the difficulty in extracting information from OBO-Edit ontology files (*.obo files) and exporting the information to other file formats. A novel ontology explorer tool was developed to facilitate extraction and export of information from the VSMO*.obo file into lists of terms and their associated unique IDs in *.txt or *.csv file formats. These lists can then be imported into a database or data management system for use as select lists with predefined terms. This is an important step to ensure that the knowledge contained in our ontology can be put into practical use.

A UML profile for the OBO relation ontology.

PubMed

Guardia, Gabriela D A; Vêncio, Ricardo Z N; de Farias, Cléver R G

2012-01-01

Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain.

Evaluation of research in biomedical ontologies

PubMed Central

Dumontier, Michel; Gkoutos, Georgios V.

2013-01-01

Ontologies are now pervasive in biomedicine, where they serve as a means to standardize terminology, to enable access to domain knowledge, to verify data consistency and to facilitate integrative analyses over heterogeneous biomedical data. For this purpose, research on biomedical ontologies applies theories and methods from diverse disciplines such as information management, knowledge representation, cognitive science, linguistics and philosophy. Depending on the desired applications in which ontologies are being applied, the evaluation of research in biomedical ontologies must follow different strategies. Here, we provide a classification of research problems in which ontologies are being applied, focusing on the use of ontologies in basic and translational research, and we demonstrate how research results in biomedical ontologies can be evaluated. The evaluation strategies depend on the desired application and measure the success of using an ontology for a particular biomedical problem. For many applications, the success can be quantified, thereby facilitating the objective evaluation and comparison of research in biomedical ontology. The objective, quantifiable comparison of research results based on scientific applications opens up the possibility for systematically improving the utility of ontologies in biomedical research. PMID:22962340

A top-level ontology of functions and its application in the Open Biomedical Ontologies.

PubMed

Burek, Patryk; Hoehndorf, Robert; Loebe, Frank; Visagie, Johann; Herre, Heinrich; Kelso, Janet

2006-07-15

A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

Ontological knowledge engine and health screening data enabled ubiquitous personalized physical fitness (UFIT).

PubMed

Su, Chuan-Jun; Chiang, Chang-Yu; Chih, Meng-Chun

2014-03-07

Good physical fitness generally makes the body less prone to common diseases. A personalized exercise plan that promotes a balanced approach to fitness helps promotes fitness, while inappropriate forms of exercise can have adverse consequences for health. This paper aims to develop an ontology-driven knowledge-based system for generating custom-designed exercise plans based on a user's profile and health status, incorporating international standard Health Level Seven International (HL7) data on physical fitness and health screening. The generated plan exposing Representational State Transfer (REST) style web services which can be accessed from any Internet-enabled device and deployed in cloud computing environments. To ensure the practicality of the generated exercise plans, encapsulated knowledge used as a basis for inference in the system is acquired from domain experts. The proposed Ubiquitous Exercise Plan Generation for Personalized Physical Fitness (UFIT) will not only improve health-related fitness through generating personalized exercise plans, but also aid users in avoiding inappropriate work outs.

Ontological Knowledge Engine and Health Screening Data Enabled Ubiquitous Personalized Physical Fitness (UFIT)

PubMed Central

Su, Chuan-Jun; Chiang, Chang-Yu; Chih, Meng-Chun

2014-01-01

Good physical fitness generally makes the body less prone to common diseases. A personalized exercise plan that promotes a balanced approach to fitness helps promotes fitness, while inappropriate forms of exercise can have adverse consequences for health. This paper aims to develop an ontology-driven knowledge-based system for generating custom-designed exercise plans based on a user's profile and health status, incorporating international standard Health Level Seven International (HL7) data on physical fitness and health screening. The generated plan exposing Representational State Transfer (REST) style web services which can be accessed from any Internet-enabled device and deployed in cloud computing environments. To ensure the practicality of the generated exercise plans, encapsulated knowledge used as a basis for inference in the system is acquired from domain experts. The proposed Ubiquitous Exercise Plan Generation for Personalized Physical Fitness (UFIT) will not only improve health-related fitness through generating personalized exercise plans, but also aid users in avoiding inappropriate work outs. PMID:24608002

A DNA-based semantic fusion model for remote sensing data.

PubMed

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

A DNA-Based Semantic Fusion Model for Remote Sensing Data

PubMed Central

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H.

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology. PMID:24116207

Information Pre-Processing using Domain Meta-Ontology and Rule Learning System

NASA Astrophysics Data System (ADS)

Ranganathan, Girish R.; Biletskiy, Yevgen

Around the globe, extraordinary amounts of documents are being created by Enterprises and by users outside these Enterprises. The documents created in the Enterprises constitute the main focus of the present chapter. These documents are used to perform numerous amounts of machine processing. While using thesedocuments for machine processing, lack of semantics of the information in these documents may cause misinterpretation of the information, thereby inhibiting the productiveness of computer assisted analytical work. Hence, it would be profitable to the Enterprises if they use well defined domain ontologies which will serve as rich source(s) of semantics for the information in the documents. These domain ontologies can be created manually, semi-automatically or fully automatically. The focus of this chapter is to propose an intermediate solution which will enable relatively easy creation of these domain ontologies. The process of extracting and capturing domain ontologies from these voluminous documents requires extensive involvement of domain experts and application of methods of ontology learning that are substantially labor intensive; therefore, some intermediate solutions which would assist in capturing domain ontologies must be developed. This chapter proposes a solution in this direction which involves building a meta-ontology that will serve as an intermediate information source for the main domain ontology. This chapter proposes a solution in this direction which involves building a meta-ontology as a rapid approach in conceptualizing a domain of interest from huge amount of source documents. This meta-ontology can be populated by ontological concepts, attributes and relations from documents, and then refined in order to form better domain ontology either through automatic ontology learning methods or some other relevant ontology building approach.

BioFed: federated query processing over life sciences linked open data.

PubMed

Hasnain, Ali; Mehmood, Qaiser; Sana E Zainab, Syeda; Saleem, Muhammad; Warren, Claude; Zehra, Durre; Decker, Stefan; Rebholz-Schuhmann, Dietrich

2017-03-15

Biomedical data, e.g. from knowledge bases and ontologies, is increasingly made available following open linked data principles, at best as RDF triple data. This is a necessary step towards unified access to biological data sets, but this still requires solutions to query multiple endpoints for their heterogeneous data to eventually retrieve all the meaningful information. Suggested solutions are based on query federation approaches, which require the submission of SPARQL queries to endpoints. Due to the size and complexity of available data, these solutions have to be optimised for efficient retrieval times and for users in life sciences research. Last but not least, over time, the reliability of data resources in terms of access and quality have to be monitored. Our solution (BioFed) federates data over 130 SPARQL endpoints in life sciences and tailors query submission according to the provenance information. BioFed has been evaluated against the state of the art solution FedX and forms an important benchmark for the life science domain. The efficient cataloguing approach of the federated query processing system 'BioFed', the triple pattern wise source selection and the semantic source normalisation forms the core to our solution. It gathers and integrates data from newly identified public endpoints for federated access. Basic provenance information is linked to the retrieved data. Last but not least, BioFed makes use of the latest SPARQL standard (i.e., 1.1) to leverage the full benefits for query federation. The evaluation is based on 10 simple and 10 complex queries, which address data in 10 major and very popular data sources (e.g., Dugbank, Sider). BioFed is a solution for a single-point-of-access for a large number of SPARQL endpoints providing life science data. It facilitates efficient query generation for data access and provides basic provenance information in combination with the retrieved data. BioFed fully supports SPARQL 1.1 and gives access to the endpoint's availability based on the EndpointData graph. Our evaluation of BioFed against FedX is based on 20 heterogeneous federated SPARQL queries and shows competitive execution performance in comparison to FedX, which can be attributed to the provision of provenance information for the source selection. Developing and testing federated query engines for life sciences data is still a challenging task. According to our findings, it is advantageous to optimise the source selection. The cataloguing of SPARQL endpoints, including type and property indexing, leads to efficient querying of data resources over the Web of Data. This could even be further improved through the use of ontologies, e.g., for abstract normalisation of query terms.

Knowledge Discovery, Integration and Communication for Extreme Weather and Flood Resilience Using Artificial Intelligence: Flood AI Alpha

NASA Astrophysics Data System (ADS)

Demir, I.; Sermet, M. Y.

2016-12-01

Nobody is immune from extreme events or natural hazards that can lead to large-scale consequences for the nation and public. One of the solutions to reduce the impacts of extreme events is to invest in improving resilience with the ability to better prepare, plan, recover, and adapt to disasters. The National Research Council (NRC) report discusses the topic of how to increase resilience to extreme events through a vision of resilient nation in the year 2030. The report highlights the importance of data, information, gaps and knowledge challenges that needs to be addressed, and suggests every individual to access the risk and vulnerability information to make their communities more resilient. This abstracts presents our project on developing a resilience framework for flooding to improve societal preparedness with objectives; (a) develop a generalized ontology for extreme events with primary focus on flooding; (b) develop a knowledge engine with voice recognition, artificial intelligence, natural language processing, and inference engine. The knowledge engine will utilize the flood ontology and concepts to connect user input to relevant knowledge discovery outputs on flooding; (c) develop a data acquisition and processing framework from existing environmental observations, forecast models, and social networks. The system will utilize the framework, capabilities and user base of the Iowa Flood Information System (IFIS) to populate and test the system; (d) develop a communication framework to support user interaction and delivery of information to users. The interaction and delivery channels will include voice and text input via web-based system (e.g. IFIS), agent-based bots (e.g. Microsoft Skype, Facebook Messenger), smartphone and augmented reality applications (e.g. smart assistant), and automated web workflows (e.g. IFTTT, CloudWork) to open the knowledge discovery for flooding to thousands of community extensible web workflows.

The National Center for Biomedical Ontology

PubMed Central

Noy, Natalya F; Shah, Nigam H; Whetzel, Patricia L; Chute, Christopher G; Story, Margaret-Anne; Smith, Barry

2011-01-01

The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups who develop and use ontologies and terminologies in biomedicine. The centerpiece of the National Center for Biomedical Ontology is a web-based resource known as BioPortal. BioPortal makes available for research in computationally useful forms more than 270 of the world's biomedical ontologies and terminologies, and supports a wide range of web services that enable investigators to use the ontologies to annotate and retrieve data, to generate value sets and special-purpose lexicons, and to perform advanced analytics on a wide range of biomedical data. PMID:22081220

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning.

PubMed

Hoehndorf, Robert; Dumontier, Michel; Oellrich, Anika; Rebholz-Schuhmann, Dietrich; Schofield, Paul N; Gkoutos, Georgios V

2011-01-01

Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect to automated reasoning for large scale data integration and knowledge discovery. We describe a method to improve automated reasoning over biomedical ontologies and identify several thousand contradictory class definitions. Our approach aligns terms in biomedical ontologies with foundational classes in a top-level ontology and formalizes composite relations as class expressions. We describe the semi-automated repair of contradictions and demonstrate expressive queries over interoperable ontologies. Our work forms an important cornerstone for data integration, automatic inference and knowledge discovery based on formal representations of knowledge. Our results and analysis software are available at http://bioonto.de/pmwiki.php/Main/ReasonableOntologies.

Application of Alignment Methodologies to Spatial Ontologies in the Hydro Domain

NASA Astrophysics Data System (ADS)

Lieberman, J. E.; Cheatham, M.; Varanka, D.

2015-12-01

Ontologies are playing an increasing role in facilitating mediation and translation between datasets representing diverse schemas, vocabularies, or knowledge communities. This role is relatively straightforward when there is one ontology comprising all relevant common concepts that can be mapped to entities in each dataset. Frequently, one common ontology has not been agreed to. Either each dataset is represented by a distinct ontology, or there are multiple candidates for commonality. Either the one most appropriate (expressive, relevant, correct) ontology must be chosen, or else concepts and relationships matched across multiple ontologies through an alignment process so that they may be used in concert to carry out mediation or other semantic operations. A resulting alignment can be effective to the extent that entities in in the ontologies represent differing terminology for comparable conceptual knowledge. In cases such as spatial ontologies, though, ontological entities may also represent disparate conceptualizations of space according to the discernment methods and application domains on which they are based. One ontology's wetland concept may overlap in space with another ontology's recharge zone or wildlife range or water feature. In order to evaluate alignment with respect to spatial ontologies, alignment has been applied to a series of ontologies pertaining to surface water that are used variously in hydrography (characterization of water features), hydrology (study of water cycling), and water quality (nutrient and contaminant transport) application domains. There is frequently a need to mediate between datasets in each domain in order to develop broader understanding of surface water systems, so there is a practical as well theoretical value in the alignment. From a domain expertise standpoint, the ontologies under consideration clearly contain some concepts that are spatially as well as conceptually identical and then others with less clear similarities in either sense. Our study serves both to determine the limits of standard methods for aligning spatial ontologies and to suggest new methods of calculating similarity axioms that take into account semantic, spatial, and cognitive criteria relevant to fitness for relevant usage scenarios.

GeoSciGraph: An Ontological Framework for EarthCube Semantic Infrastructure

NASA Astrophysics Data System (ADS)

Gupta, A.; Schachne, A.; Condit, C.; Valentine, D.; Richard, S.; Zaslavsky, I.

2015-12-01

The CINERGI (Community Inventory of EarthCube Resources for Geosciences Interoperability) project compiles an inventory of a wide variety of earth science resources including documents, catalogs, vocabularies, data models, data services, process models, information repositories, domain-specific ontologies etc. developed by research groups and data practitioners. We have developed a multidisciplinary semantic framework called GeoSciGraph semantic ingration of earth science resources. An integrated ontology is constructed with Basic Formal Ontology (BFO) as its upper ontology and currently ingests multiple component ontologies including the SWEET ontology, GeoSciML's lithology ontology, Tematres controlled vocabulary server, GeoNames, GCMD vocabularies on equipment, platforms and institutions, software ontology, CUAHSI hydrology vocabulary, the environmental ontology (ENVO) and several more. These ontologies are connected through bridging axioms; GeoSciGraph identifies lexically close terms and creates equivalence class or subclass relationships between them after human verification. GeoSciGraph allows a community to create community-specific customizations of the integrated ontology. GeoSciGraph uses the Neo4J,a graph database that can hold several billion concepts and relationships. GeoSciGraph provides a number of REST services that can be called by other software modules like the CINERGI information augmentation pipeline. 1) Vocabulary services are used to find exact and approximate terms, term categories (community-provided clusters of terms e.g., measurement-related terms or environmental material related terms), synonyms, term definitions and annotations. 2) Lexical services are used for text parsing to find entities, which can then be included into the ontology by a domain expert. 3) Graph services provide the ability to perform traversal centric operations e.g., finding paths and neighborhoods which can be used to perform ontological operations like computing transitive closure (e.g., finding all subclasses of rocks). 4) Annotation services are used to adorn an arbitrary block of text (e.g., from a NOAA catalog record) with ontology terms. The system has been used to ontologically integrate diverse sources like Science-base, NOAA records, PETDB.

Towards sustainable infrastructure management: knowledge-based service-oriented computing framework for visual analytics

NASA Astrophysics Data System (ADS)

Vatcha, Rashna; Lee, Seok-Won; Murty, Ajeet; Tolone, William; Wang, Xiaoyu; Dou, Wenwen; Chang, Remco; Ribarsky, William; Liu, Wanqiu; Chen, Shen-en; Hauser, Edd

2009-05-01

Infrastructure management (and its associated processes) is complex to understand, perform and thus, hard to make efficient and effective informed decisions. The management involves a multi-faceted operation that requires the most robust data fusion, visualization and decision making. In order to protect and build sustainable critical assets, we present our on-going multi-disciplinary large-scale project that establishes the Integrated Remote Sensing and Visualization (IRSV) system with a focus on supporting bridge structure inspection and management. This project involves specific expertise from civil engineers, computer scientists, geographers, and real-world practitioners from industry, local and federal government agencies. IRSV is being designed to accommodate the essential needs from the following aspects: 1) Better understanding and enforcement of complex inspection process that can bridge the gap between evidence gathering and decision making through the implementation of ontological knowledge engineering system; 2) Aggregation, representation and fusion of complex multi-layered heterogeneous data (i.e. infrared imaging, aerial photos and ground-mounted LIDAR etc.) with domain application knowledge to support machine understandable recommendation system; 3) Robust visualization techniques with large-scale analytical and interactive visualizations that support users' decision making; and 4) Integration of these needs through the flexible Service-oriented Architecture (SOA) framework to compose and provide services on-demand. IRSV is expected to serve as a management and data visualization tool for construction deliverable assurance and infrastructure monitoring both periodically (annually, monthly, even daily if needed) as well as after extreme events.

Ontology Research and Development. Part 1-A Review of Ontology Generation.

ERIC Educational Resources Information Center

Ding, Ying; Foo, Schubert

2002-01-01

Discusses the role of ontology in knowledge representation, including enabling content-based access, interoperability, communications, and new levels of service on the Semantic Web; reviews current ontology generation studies and projects as well as problems facing such research; and discusses ontology mapping, information extraction, natural…

Simple Ontology Format (SOFT)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sorokine, Alexandre

2011-10-01

Simple Ontology Format (SOFT) library and file format specification provides a set of simple tools for developing and maintaining ontologies. The library, implemented as a perl module, supports parsing and verification of the files in SOFt format, operations with ontologies (adding, removing, or filtering of entities), and converting of ontologies into other formats. SOFT allows users to quickly create ontologies using only a basic text editor, verify it, and portray it in a graph layout system using customized styles.

Ontological metaphors for negative energy in an interdisciplinary context

NASA Astrophysics Data System (ADS)

Dreyfus, Benjamin W.; Geller, Benjamin D.; Gouvea, Julia; Sawtelle, Vashti; Turpen, Chandra; Redish, Edward F.

2014-12-01

Teaching about energy in interdisciplinary settings that emphasize coherence among physics, chemistry, and biology leads to a more central role for chemical bond energy. We argue that an interdisciplinary approach to chemical energy leads to modeling chemical bonds in terms of negative energy. While recent work on ontological metaphors for energy has emphasized the affordances of the substance ontology, this ontology is problematic in the context of negative energy. Instead, we apply a dynamic ontologies perspective to argue that blending the substance and location ontologies for energy can be effective in reasoning about negative energy in the context of reasoning about chemical bonds. We present data from an introductory physics for the life sciences course in which both experts and students successfully use this blended ontology. Blending these ontologies is most successful when the substance and location ontologies are combined such that each is strategically utilized in reasoning about particular aspects of energetic processes.

BiNChE: a web tool and library for chemical enrichment analysis based on the ChEBI ontology.

PubMed

Moreno, Pablo; Beisken, Stephan; Harsha, Bhavana; Muthukrishnan, Venkatesh; Tudose, Ilinca; Dekker, Adriano; Dornfeldt, Stefanie; Taruttis, Franziska; Grosse, Ivo; Hastings, Janna; Neumann, Steffen; Steinbeck, Christoph

2015-02-21

Ontology-based enrichment analysis aids in the interpretation and understanding of large-scale biological data. Ontologies are hierarchies of biologically relevant groupings. Using ontology annotations, which link ontology classes to biological entities, enrichment analysis methods assess whether there is a significant over or under representation of entities for ontology classes. While many tools exist that run enrichment analysis for protein sets annotated with the Gene Ontology, there are only a few that can be used for small molecules enrichment analysis. We describe BiNChE, an enrichment analysis tool for small molecules based on the ChEBI Ontology. BiNChE displays an interactive graph that can be exported as a high-resolution image or in network formats. The tool provides plain, weighted and fragment analysis based on either the ChEBI Role Ontology or the ChEBI Structural Ontology. BiNChE aids in the exploration of large sets of small molecules produced within Metabolomics or other Systems Biology research contexts. The open-source tool provides easy and highly interactive web access to enrichment analysis with the ChEBI ontology tool and is additionally available as a standalone library.

BiOSS: A system for biomedical ontology selection.

PubMed

Martínez-Romero, Marcos; Vázquez-Naya, José M; Pereira, Javier; Pazos, Alejandro

2014-04-01

In biomedical informatics, ontologies are considered a key technology for annotating, retrieving and sharing the huge volume of publicly available data. Due to the increasing amount, complexity and variety of existing biomedical ontologies, choosing the ones to be used in a semantic annotation problem or to design a specific application is a difficult task. As a consequence, the design of approaches and tools addressed to facilitate the selection of biomedical ontologies is becoming a priority. In this paper we present BiOSS, a novel system for the selection of biomedical ontologies. BiOSS evaluates the adequacy of an ontology to a given domain according to three different criteria: (1) the extent to which the ontology covers the domain; (2) the semantic richness of the ontology in the domain; (3) the popularity of the ontology in the biomedical community. BiOSS has been applied to 5 representative problems of ontology selection. It also has been compared to existing methods and tools. Results are promising and show the usefulness of BiOSS to solve real-world ontology selection problems. BiOSS is openly available both as a web tool and a web service. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Construction of a Clinical Decision Support System for Undergoing Surgery Based on Domain Ontology and Rules Reasoning

PubMed Central

Bau, Cho-Tsan; Huang, Chung-Yi

2014-01-01

Abstract Objective: To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. Materials and Methods: The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé–Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. Results: The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. Conclusions: The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia. PMID:24730353

Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning.

PubMed

Bau, Cho-Tsan; Chen, Rung-Ching; Huang, Chung-Yi

2014-05-01

To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé-Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia.

OWL reasoning framework over big biological knowledge network.

PubMed

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity.

OWL Reasoning Framework over Big Biological Knowledge Network

PubMed Central

Chen, Huajun; Chen, Xi; Gu, Peiqin; Wu, Zhaohui; Yu, Tong

2014-01-01

Recently, huge amounts of data are generated in the domain of biology. Embedded with domain knowledge from different disciplines, the isolated biological resources are implicitly connected. Thus it has shaped a big network of versatile biological knowledge. Faced with such massive, disparate, and interlinked biological data, providing an efficient way to model, integrate, and analyze the big biological network becomes a challenge. In this paper, we present a general OWL (web ontology language) reasoning framework to study the implicit relationships among biological entities. A comprehensive biological ontology across traditional Chinese medicine (TCM) and western medicine (WM) is used to create a conceptual model for the biological network. Then corresponding biological data is integrated into a biological knowledge network as the data model. Based on the conceptual model and data model, a scalable OWL reasoning method is utilized to infer the potential associations between biological entities from the biological network. In our experiment, we focus on the association discovery between TCM and WM. The derived associations are quite useful for biologists to promote the development of novel drugs and TCM modernization. The experimental results show that the system achieves high efficiency, accuracy, scalability, and effectivity. PMID:24877076

Design and Implementation of a Threaded Search Engine for Tour Recommendation Systems

NASA Astrophysics Data System (ADS)

Lee, Junghoon; Park, Gyung-Leen; Ko, Jin-Hee; Shin, In-Hye; Kang, Mikyung

This paper implements a threaded scan engine for the O(n!) search space and measures its performance, aiming at providing a responsive tour recommendation and scheduling service. As a preliminary step of integrating POI ontology, mobile object database, and personalization profile for the development of new vehicular telematics services, this implementation can give a useful guideline to design a challenging and computation-intensive vehicular telematics service. The implemented engine allocates the subtree to the respective threads and makes them run concurrently exploiting the primitives provided by the operating system and the underlying multiprocessor architecture. It also makes it easy to add a variety of constraints, for example, the search tree is pruned if the cost of partial allocation already exceeds the current best. The performance measurement result shows that the service can run even in the low-power telematics device when the number of destinations does not exceed 15, with an appropriate constraint processing.

PDON: Parkinson's disease ontology for representation and modeling of the Parkinson's disease knowledge domain.

PubMed

Younesi, Erfan; Malhotra, Ashutosh; Gündel, Michaela; Scordis, Phil; Kodamullil, Alpha Tom; Page, Matt; Müller, Bernd; Springstubbe, Stephan; Wüllner, Ullrich; Scheller, Dieter; Hofmann-Apitius, Martin

2015-09-22

Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology. The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson's disease. Parkinson's disease ontology delivers the knowledge domain of Parkinson's disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson's-related computable models. A practical version of the Parkinson's disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON .

Ontology for Life-Cycle Modeling of Heating, Ventilating, and Air Conditioning (HVAC) Systems: Experimental Applications Using Revit

DTIC Science & Technology

2012-03-01

Revit object IFCExportType IFCExportAs Radiator Radiator IfcSpaceHeaterType Pump Circulator IfcPumpType Boiler Water IfcBoilerType Fan VaneAxial...modeling is assumed to be a traditional water-based system comprised of boilers and fan coil units (heating) and chillers and air handling units...the properties that a particular engineer would want to specify as part of the BIM model. For instance, the default pump families in Revit do not

Toxicology ontology perspectives.

PubMed

Hardy, Barry; Apic, Gordana; Carthew, Philip; Clark, Dominic; Cook, David; Dix, Ian; Escher, Sylvia; Hastings, Janna; Heard, David J; Jeliazkova, Nina; Judson, Philip; Matis-Mitchell, Sherri; Mitic, Dragana; Myatt, Glenn; Shah, Imran; Spjuth, Ola; Tcheremenskaia, Olga; Toldo, Luca; Watson, David; White, Andrew; Yang, Chihae

2012-01-01

The field of predictive toxicology requires the development of open, public, computable, standardized toxicology vocabularies and ontologies to support the applications required by in silico, in vitro, and in vivo toxicology methods and related analysis and reporting activities. In this article we review ontology developments based on a set of perspectives showing how ontologies are being used in predictive toxicology initiatives and applications. Perspectives on resources and initiatives reviewed include OpenTox, eTOX, Pistoia Alliance, ToxWiz, Virtual Liver, EU-ADR, BEL, ToxML, and Bioclipse. We also review existing ontology developments in neighboring fields that can contribute to establishing an ontological framework for predictive toxicology. A significant set of resources is already available to provide a foundation for an ontological framework for 21st century mechanistic-based toxicology research. Ontologies such as ToxWiz provide a basis for application to toxicology investigations, whereas other ontologies under development in the biological, chemical, and biomedical communities could be incorporated in an extended future framework. OpenTox has provided a semantic web framework for the implementation of such ontologies into software applications and linked data resources. Bioclipse developers have shown the benefit of interoperability obtained through ontology by being able to link their workbench application with remote OpenTox web services. Although these developments are promising, an increased international coordination of efforts is greatly needed to develop a more unified, standardized, and open toxicology ontology framework.

Exploring biomedical ontology mappings with graph theory methods.

PubMed

Kocbek, Simon; Kim, Jin-Dong

2017-01-01

In the era of semantic web, life science ontologies play an important role in tasks such as annotating biological objects, linking relevant data pieces, and verifying data consistency. Understanding ontology structures and overlapping ontologies is essential for tasks such as ontology reuse and development. We present an exploratory study where we examine structure and look for patterns in BioPortal, a comprehensive publicly available repository of live science ontologies. We report an analysis of biomedical ontology mapping data over time. We apply graph theory methods such as Modularity Analysis and Betweenness Centrality to analyse data gathered at five different time points. We identify communities, i.e., sets of overlapping ontologies, and define similar and closest communities. We demonstrate evolution of identified communities over time and identify core ontologies of the closest communities. We use BioPortal project and category data to measure community coherence. We also validate identified communities with their mutual mentions in scientific literature. With comparing mapping data gathered at five different time points, we identified similar and closest communities of overlapping ontologies, and demonstrated evolution of communities over time. Results showed that anatomy and health ontologies tend to form more isolated communities compared to other categories. We also showed that communities contain all or the majority of ontologies being used in narrower projects. In addition, we identified major changes in mapping data after migration to BioPortal Version 4.

Semantic technologies improving the recall and precision of the Mercury metadata search engine

NASA Astrophysics Data System (ADS)

Pouchard, L. C.; Cook, R. B.; Green, J.; Palanisamy, G.; Noy, N.

2011-12-01

The Mercury federated metadata system [1] was developed at the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), a NASA-sponsored effort holding datasets about biogeochemical dynamics, ecological data, and environmental processes. Mercury currently indexes over 100,000 records from several data providers conforming to community standards, e.g. EML, FGDC, FGDC Biological Profile, ISO 19115 and DIF. With the breadth of sciences represented in Mercury, the potential exists to address some key interdisciplinary scientific challenges related to climate change, its environmental and ecological impacts, and mitigation of these impacts. However, this wealth of metadata also hinders pinpointing datasets relevant to a particular inquiry. We implemented a semantic solution after concluding that traditional search approaches cannot improve the accuracy of the search results in this domain because: a) unlike everyday queries, scientific queries seek to return specific datasets with numerous parameters that may or may not be exposed to search (Deep Web queries); b) the relevance of a dataset cannot be judged by its popularity, as each scientific inquiry tends to be unique; and c)each domain science has its own terminology, more or less curated, consensual, and standardized depending on the domain. The same terms may refer to different concepts across domains (homonyms), but different terms mean the same thing (synonyms). Interdisciplinary research is arduous because an expert in a domain must become fluent in the language of another, just to find relevant datasets. Thus, we decided to use scientific ontologies because they can provide a context for a free-text search, in a way that string-based keywords never will. With added context, relevant datasets are more easily discoverable. To enable search and programmatic access to ontology entities in Mercury, we are using an instance of the BioPortal ontology repository. Mercury accesses ontology entities using the BioPortal REST API by passing a search parameter to BioPortal that may return domain context, parameter attribute, or entity annotations depending on the entity's associated ontological relationships. As Mercury's facetted search is popular with users, the results are displayed as facets. Unlike a facetted search however, the ontology-based solution implements both restrictions (improving precision) and expansions (improving recall) on the results of the initial search. For instance, "carbon" acquires a scientific context and additional key terms or phrases for discovering domain-specific datasets. A limitation of our solution is that the user must perform an additional step. Another limitation is that the quality of the newly discovered metadata is contingent upon the quality of the ontologies we use. Our solution leverages Mercury's federated capabilities to collect records from heterogeneous domains, and BioPortal's storage, curation and access capabilities for ontology entities. With minimal additional development, our approach builds on two mature systems for finding relevant datasets for interdisciplinary inquiries. We thus indicate a path forward for linking environmental, ecological and biological sciences. References: [1] Devarakonda, R., Palanisamy, G., Wilson, B. E., & Green, J. M. (2010). Mercury: reusable metadata management, data discovery and access system. Earth Science Informatics, 3(1-2), 87-94.

The Domain Shared by Computational and Digital Ontology: A Phenomenological Exploration and Analysis

ERIC Educational Resources Information Center

Compton, Bradley Wendell

2009-01-01

The purpose of this dissertation is to explore and analyze a domain of research thought to be shared by two areas of philosophy: computational and digital ontology. Computational ontology is philosophy used to develop information systems also called computational ontologies. Digital ontology is philosophy dealing with our understanding of Being…

A four stage approach for ontology-based health information system design.

PubMed

Kuziemsky, Craig E; Lau, Francis

2010-11-01

To describe and illustrate a four stage methodological approach to capture user knowledge in a biomedical domain area, use that knowledge to design an ontology, and then implement and evaluate the ontology as a health information system (HIS). A hybrid participatory design-grounded theory (GT-PD) method was used to obtain data and code them for ontology development. Prototyping was used to implement the ontology as a computer-based tool. Usability testing evaluated the computer-based tool. An empirically derived domain ontology and set of three problem-solving approaches were developed as a formalized model of the concepts and categories from the GT coding. The ontology and problem-solving approaches were used to design and implement a HIS that tested favorably in usability testing. The four stage approach illustrated in this paper is useful for designing and implementing an ontology as the basis for a HIS. The approach extends existing ontology development methodologies by providing an empirical basis for theory incorporated into ontology design. Copyright © 2010 Elsevier B.V. All rights reserved.

Toward a general ontology for digital forensic disciplines.

PubMed

Karie, Nickson M; Venter, Hein S

2014-09-01

Ontologies are widely used in different disciplines as a technique for representing and reasoning about domain knowledge. However, despite the widespread ontology-related research activities and applications in different disciplines, the development of ontologies and ontology research activities is still wanting in digital forensics. This paper therefore presents the case for establishing an ontology for digital forensic disciplines. Such an ontology would enable better categorization of the digital forensic disciplines, as well as assist in the development of methodologies and specifications that can offer direction in different areas of digital forensics. This includes such areas as professional specialization, certifications, development of digital forensic tools, curricula, and educational materials. In addition, the ontology presented in this paper can be used, for example, to better organize the digital forensic domain knowledge and explicitly describe the discipline's semantics in a common way. Finally, this paper is meant to spark discussions and further research on an internationally agreed ontological distinction of the digital forensic disciplines. Digital forensic disciplines ontology is a novel approach toward organizing the digital forensic domain knowledge and constitutes the main contribution of this paper. © 2014 American Academy of Forensic Sciences.

Supporting ontology adaptation and versioning based on a graph of relevance

NASA Astrophysics Data System (ADS)

Sassi, Najla; Jaziri, Wassim; Alharbi, Saad

2016-11-01

Ontologies recently have become a topic of interest in computer science since they are seen as a semantic support to explicit and enrich data-models as well as to ensure interoperability of data. Moreover, supporting ontology adaptation becomes essential and extremely important, mainly when using ontologies in changing environments. An important issue when dealing with ontology adaptation is the management of several versions. Ontology versioning is a complex and multifaceted problem as it should take into account change management, versions storage and access, consistency issues, etc. The purpose of this paper is to propose an approach and tool for ontology adaptation and versioning. A series of techniques are proposed to 'safely' evolve a given ontology and produce a new consistent version. The ontology versions are ordered in a graph according to their relevance. The relevance is computed based on four criteria: conceptualisation, usage frequency, abstraction and completeness. The techniques to carry out the versioning process are implemented in the Consistology tool, which has been developed to assist users in expressing adaptation requirements and managing ontology versions.

Research Prototype: Automated Analysis of Scientific and Engineering Semantics

NASA Technical Reports Server (NTRS)

Stewart, Mark E. M.; Follen, Greg (Technical Monitor)

2001-01-01

Physical and mathematical formulae and concepts are fundamental elements of scientific and engineering software. These classical equations and methods are time tested, universally accepted, and relatively unambiguous. The existence of this classical ontology suggests an ideal problem for automated comprehension. This problem is further motivated by the pervasive use of scientific code and high code development costs. To investigate code comprehension in this classical knowledge domain, a research prototype has been developed. The prototype incorporates scientific domain knowledge to recognize code properties (including units, physical, and mathematical quantity). Also, the procedure implements programming language semantics to propagate these properties through the code. This prototype's ability to elucidate code and detect errors will be demonstrated with state of the art scientific codes.

Spanish language generation engine to enhance the syntactic quality of AAC systems

NASA Astrophysics Data System (ADS)

Narváez A., Cristian; Sastoque H., Sebastián.; Iregui G., Marcela

2015-12-01

People with Complex Communication Needs (CCN) face difficulties to communicate their ideas, feelings and needs. Augmentative and Alternative Communication (AAC) approaches aim to provide support to enhance socialization of these individuals. However, there are many limitations in current applications related with systems operation, target scenarios and language consistency. This work presents an AAC approach to enhance produced messages by applying elements of Natural Language Generation. Specifically, a Spanish language engine, composed of a grammar ontology and a set of linguistic rules, is proposed to improve the naturalness in the communication process, when persons with CCN tell stories about their daily activities to non-disabled receivers. The assessment of the proposed method confirms the validity of the model to improve messages quality.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae; Song, Tae-Min

2017-07-24

Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, "academic stresses" and "suicide" contributed negatively to the sentiment of adolescent depression. The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. ©Hyesil Jung, Hyeoun-Ae Park, Tae-Min Song. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.07.2017.

Ontology-Based Approach to Social Data Sentiment Analysis: Detection of Adolescent Depression Signals

PubMed Central

Jung, Hyesil; Song, Tae-Min

2017-01-01

Background Social networking services (SNSs) contain abundant information about the feelings, thoughts, interests, and patterns of behavior of adolescents that can be obtained by analyzing SNS postings. An ontology that expresses the shared concepts and their relationships in a specific field could be used as a semantic framework for social media data analytics. Objective The aim of this study was to refine an adolescent depression ontology and terminology as a framework for analyzing social media data and to evaluate description logics between classes and the applicability of this ontology to sentiment analysis. Methods The domain and scope of the ontology were defined using competency questions. The concepts constituting the ontology and terminology were collected from clinical practice guidelines, the literature, and social media postings on adolescent depression. Class concepts, their hierarchy, and the relationships among class concepts were defined. An internal structure of the ontology was designed using the entity-attribute-value (EAV) triplet data model, and superclasses of the ontology were aligned with the upper ontology. Description logics between classes were evaluated by mapping concepts extracted from the answers to frequently asked questions (FAQs) onto the ontology concepts derived from description logic queries. The applicability of the ontology was validated by examining the representability of 1358 sentiment phrases using the ontology EAV model and conducting sentiment analyses of social media data using ontology class concepts. Results We developed an adolescent depression ontology that comprised 443 classes and 60 relationships among the classes; the terminology comprised 1682 synonyms of the 443 classes. In the description logics test, no error in relationships between classes was found, and about 89% (55/62) of the concepts cited in the answers to FAQs mapped onto the ontology class. Regarding applicability, the EAV triplet models of the ontology class represented about 91.4% of the sentiment phrases included in the sentiment dictionary. In the sentiment analyses, “academic stresses” and “suicide” contributed negatively to the sentiment of adolescent depression. Conclusions The ontology and terminology developed in this study provide a semantic foundation for analyzing social media data on adolescent depression. To be useful in social media data analysis, the ontology, especially the terminology, needs to be updated constantly to reflect rapidly changing terms used by adolescents in social media postings. In addition, more attributes and value sets reflecting depression-related sentiments should be added to the ontology. PMID:28739560

The Gene Ontology (GO) Cellular Component Ontology: integration with SAO (Subcellular Anatomy Ontology) and other recent developments

PubMed Central

2013-01-01

Background The Gene Ontology (GO) (http://www.geneontology.org/) contains a set of terms for describing the activity and actions of gene products across all kingdoms of life. Each of these activities is executed in a location within a cell or in the vicinity of a cell. In order to capture this context, the GO includes a sub-ontology called the Cellular Component (CC) ontology (GO-CCO). The primary use of this ontology is for GO annotation, but it has also been used for phenotype annotation, and for the annotation of images. Another ontology with similar scope to the GO-CCO is the Subcellular Anatomy Ontology (SAO), part of the Neuroscience Information Framework Standard (NIFSTD) suite of ontologies. The SAO also covers cell components, but in the domain of neuroscience. Description Recently, the GO-CCO was enriched in content and links to the Biological Process and Molecular Function branches of GO as well as to other ontologies. This was achieved in several ways. We carried out an amalgamation of SAO terms with GO-CCO ones; as a result, nearly 100 new neuroscience-related terms were added to the GO. The GO-CCO also contains relationships to GO Biological Process and Molecular Function terms, as well as connecting to external ontologies such as the Cell Ontology (CL). Terms representing protein complexes in the Protein Ontology (PRO) reference GO-CCO terms for their species-generic counterparts. GO-CCO terms can also be used to search a variety of databases. Conclusions In this publication we provide an overview of the GO-CCO, its overall design, and some recent extensions that make use of additional spatial information. One of the most recent developments of the GO-CCO was the merging in of the SAO, resulting in a single unified ontology designed to serve the needs of GO annotators as well as the specific needs of the neuroscience community. PMID:24093723

Semantic SenseLab: implementing the vision of the Semantic Web in neuroscience

PubMed Central

Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi

2011-01-01

Summary Objective Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Methods Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. Conclusion We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/ PMID:20006477

The Proteasix Ontology.

PubMed

Arguello Casteleiro, Mercedes; Klein, Julie; Stevens, Robert

2016-06-04

The Proteasix Ontology (PxO) is an ontology that supports the Proteasix tool; an open-source peptide-centric tool that can be used to predict automatically and in a large-scale fashion in silico the proteases involved in the generation of proteolytic cleavage fragments (peptides) The PxO re-uses parts of the Protein Ontology, the three Gene Ontology sub-ontologies, the Chemical Entities of Biological Interest Ontology, the Sequence Ontology and bespoke extensions to the PxO in support of a series of roles: 1. To describe the known proteases and their target cleaveage sites. 2. To enable the description of proteolytic cleaveage fragments as the outputs of observed and predicted proteolysis. 3. To use knowledge about the function, species and cellular location of a protease and protein substrate to support the prioritisation of proteases in observed and predicted proteolysis. The PxO is designed to describe the biological underpinnings of the generation of peptides. The peptide-centric PxO seeks to support the Proteasix tool by separating domain knowledge from the operational knowledge used in protease prediction by Proteasix and to support the confirmation of its analyses and results. The Proteasix Ontology may be found at: http://bioportal.bioontology.org/ontologies/PXO . This ontology is free and open for use by everyone.

Construction of ontology augmented networks for protein complex prediction.

PubMed

Zhang, Yijia; Lin, Hongfei; Yang, Zhihao; Wang, Jian

2013-01-01

Protein complexes are of great importance in understanding the principles of cellular organization and function. The increase in available protein-protein interaction data, gene ontology and other resources make it possible to develop computational methods for protein complex prediction. Most existing methods focus mainly on the topological structure of protein-protein interaction networks, and largely ignore the gene ontology annotation information. In this article, we constructed ontology augmented networks with protein-protein interaction data and gene ontology, which effectively unified the topological structure of protein-protein interaction networks and the similarity of gene ontology annotations into unified distance measures. After constructing ontology augmented networks, a novel method (clustering based on ontology augmented networks) was proposed to predict protein complexes, which was capable of taking into account the topological structure of the protein-protein interaction network, as well as the similarity of gene ontology annotations. Our method was applied to two different yeast protein-protein interaction datasets and predicted many well-known complexes. The experimental results showed that (i) ontology augmented networks and the unified distance measure can effectively combine the structure closeness and gene ontology annotation similarity; (ii) our method is valuable in predicting protein complexes and has higher F1 and accuracy compared to other competing methods.

Semantic SenseLab: Implementing the vision of the Semantic Web in neuroscience.

PubMed

Samwald, Matthias; Chen, Huajun; Ruttenberg, Alan; Lim, Ernest; Marenco, Luis; Miller, Perry; Shepherd, Gordon; Cheung, Kei-Hoi

2010-01-01

Integrative neuroscience research needs a scalable informatics framework that enables semantic integration of diverse types of neuroscience data. This paper describes the use of the Web Ontology Language (OWL) and other Semantic Web technologies for the representation and integration of molecular-level data provided by several of SenseLab suite of neuroscience databases. Based on the original database structure, we semi-automatically translated the databases into OWL ontologies with manual addition of semantic enrichment. The SenseLab ontologies are extensively linked to other biomedical Semantic Web resources, including the Subcellular Anatomy Ontology, Brain Architecture Management System, the Gene Ontology, BIRNLex and UniProt. The SenseLab ontologies have also been mapped to the Basic Formal Ontology and Relation Ontology, which helps ease interoperability with many other existing and future biomedical ontologies for the Semantic Web. In addition, approaches to representing contradictory research statements are described. The SenseLab ontologies are designed for use on the Semantic Web that enables their integration into a growing collection of biomedical information resources. We demonstrate that our approach can yield significant potential benefits and that the Semantic Web is rapidly becoming mature enough to realize its anticipated promises. The ontologies are available online at http://neuroweb.med.yale.edu/senselab/. 2009 Elsevier B.V. All rights reserved.

Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support

PubMed Central

Bodenreider, O.

2008-01-01

Summary Objectives To provide typical examples of biomedical ontologies in action, emphasizing the role played by biomedical ontologies in knowledge management, data integration and decision support. Methods Biomedical ontologies selected for their practical impact are examined from a functional perspective. Examples of applications are taken from operational systems and the biomedical literature, with a bias towards recent journal articles. Results The ontologies under investigation in this survey include SNOMED CT, the Logical Observation Identifiers, Names, and Codes (LOINC), the Foundational Model of Anatomy, the Gene Ontology, RxNorm, the National Cancer Institute Thesaurus, the International Classification of Diseases, the Medical Subject Headings (MeSH) and the Unified Medical Language System (UMLS). The roles played by biomedical ontologies are classified into three major categories: knowledge management (indexing and retrieval of data and information, access to information, mapping among ontologies); data integration, exchange and semantic interoperability; and decision support and reasoning (data selection and aggregation, decision support, natural language processing applications, knowledge discovery). Conclusions Ontologies play an important role in biomedical research through a variety of applications. While ontologies are used primarily as a source of vocabulary for standardization and integration purposes, many applications also use them as a source of computable knowledge. Barriers to the use of ontologies in biomedical applications are discussed. PMID:18660879

Owlready: Ontology-oriented programming in Python with automatic classification and high level constructs for biomedical ontologies.

PubMed

Lamy, Jean-Baptiste

2017-07-01

Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies. From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated "role-filler" constraints. We also propose an algorithm for performing local closed world reasoning in simple situations. We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning. Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation. Copyright © 2017 Elsevier B.V. All rights reserved.

Ontology-supported research on vaccine efficacy, safety and integrative biological networks.

PubMed

He, Yongqun

2014-07-01

While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including Vaccine Ontology, Ontology of Adverse Events and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network ('OneNet') Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms.

War of Ontology Worlds: Mathematics, Computer Code, or Esperanto?

PubMed Central

Rzhetsky, Andrey; Evans, James A.

2011-01-01

The use of structured knowledge representations—ontologies and terminologies—has become standard in biomedicine. Definitions of ontologies vary widely, as do the values and philosophies that underlie them. In seeking to make these views explicit, we conducted and summarized interviews with a dozen leading ontologists. Their views clustered into three broad perspectives that we summarize as mathematics, computer code, and Esperanto. Ontology as mathematics puts the ultimate premium on rigor and logic, symmetry and consistency of representation across scientific subfields, and the inclusion of only established, non-contradictory knowledge. Ontology as computer code focuses on utility and cultivates diversity, fitting ontologies to their purpose. Like computer languages C++, Prolog, and HTML, the code perspective holds that diverse applications warrant custom designed ontologies. Ontology as Esperanto focuses on facilitating cross-disciplinary communication, knowledge cross-referencing, and computation across datasets from diverse communities. We show how these views align with classical divides in science and suggest how a synthesis of their concerns could strengthen the next generation of biomedical ontologies. PMID:21980276

Ontology-supported Research on Vaccine Efficacy, Safety, and Integrative Biological Networks

PubMed Central

He, Yongqun

2016-01-01

Summary While vaccine efficacy and safety research has dramatically progressed with the methods of in silico prediction and data mining, many challenges still exist. A formal ontology is a human- and computer-interpretable set of terms and relations that represent entities in a specific domain and how these terms relate to each other. Several community-based ontologies (including the Vaccine Ontology, Ontology of Adverse Events, and Ontology of Vaccine Adverse Events) have been developed to support vaccine and adverse event representation, classification, data integration, literature mining of host-vaccine interaction networks, and analysis of vaccine adverse events. The author further proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network (“OneNet”) Theory of Life, and ontology-based approaches to study and apply the OneNet theory. In the Big Data era, these proposed strategies provide a novel framework for advanced data integration and analysis of fundamental biological networks including vaccine immune mechanisms. PMID:24909153

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies

PubMed Central

Ochs, Christopher; Geller, James; Perl, Yehoshua; Musen, Mark A.

2016-01-01

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving “live partial-area taxonomies” is demonstrated. PMID:27345947

Development and Evaluation of a Low Fertility Ontology for Analyzing Social Data in Korea.

PubMed

Lee, Ji-Hyun; Park, Hyeoun-Ae; Song, Tae-Min

2016-01-01

The purpose of this study is to develop a low fertility ontology for collecting and analyzing social data. A low fertility ontology was developed according to Ontology Development 101 and formally represented using Protégé. The content coverage of the ontology was evaluated using 1,387 narratives posted by the public and 63 narratives posted by public servants. Six super-classes of the ontology were developed based on Bronfenbrenner's ecological system theory with an individual in the center and environmental systems impacting their as surroundings. In total, 568 unique concepts were extracted from the narratives. Out of these concepts, 424(74.6%) concepts were lexically or semantically mapped, 67(11.8%) were either broadly or narrowly mapped to the ontology concepts. Remaining 77(13.6%) concepts were not mapped to any of the ontology concepts. This ontology can be used as a framework to understand low fertility problems using social data in Korea.

Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions.

PubMed

Christen, Victor; Hartung, Michael; Groß, Anika

2015-01-01

A large number of life science ontologies has been developed to support different application scenarios such as gene annotation or functional analysis. The continuous accumulation of new insights and knowledge affects specific portions in ontologies and thus leads to their adaptation. Therefore, it is valuable to study which ontology parts have been extensively modified or remained unchanged. Users can monitor the evolution of an ontology to improve its further development or apply the knowledge in their applications. Here we present REX (Region Evolution eXplorer) a web-based system for exploring the evolution of ontology parts (regions). REX provides an analysis platform for currently about 1,000 versions of 16 well-known life science ontologies. Interactive workflows allow an explorative analysis of changing ontology regions and can be used to study evolution trends for long-term periods. REX is a web application providing an interactive and user-friendly interface to identify (un)stable regions in large life science ontologies. It is available at http://www.izbi.de/rex.

War of ontology worlds: mathematics, computer code, or Esperanto?

PubMed

Rzhetsky, Andrey; Evans, James A

2011-09-01

The use of structured knowledge representations-ontologies and terminologies-has become standard in biomedicine. Definitions of ontologies vary widely, as do the values and philosophies that underlie them. In seeking to make these views explicit, we conducted and summarized interviews with a dozen leading ontologists. Their views clustered into three broad perspectives that we summarize as mathematics, computer code, and Esperanto. Ontology as mathematics puts the ultimate premium on rigor and logic, symmetry and consistency of representation across scientific subfields, and the inclusion of only established, non-contradictory knowledge. Ontology as computer code focuses on utility and cultivates diversity, fitting ontologies to their purpose. Like computer languages C++, Prolog, and HTML, the code perspective holds that diverse applications warrant custom designed ontologies. Ontology as Esperanto focuses on facilitating cross-disciplinary communication, knowledge cross-referencing, and computation across datasets from diverse communities. We show how these views align with classical divides in science and suggest how a synthesis of their concerns could strengthen the next generation of biomedical ontologies.

A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.

PubMed

Ochs, Christopher; Geller, James; Perl, Yehoshua; Musen, Mark A

2016-08-01

Software tools play a critical role in the development and maintenance of biomedical ontologies. One important task that is difficult without software tools is ontology quality assurance. In previous work, we have introduced different kinds of abstraction networks to provide a theoretical foundation for ontology quality assurance tools. Abstraction networks summarize the structure and content of ontologies. One kind of abstraction network that we have used repeatedly to support ontology quality assurance is the partial-area taxonomy. It summarizes structurally and semantically similar concepts within an ontology. However, the use of partial-area taxonomies was ad hoc and not generalizable. In this paper, we describe the Ontology Abstraction Framework (OAF), a unified framework and software system for deriving, visualizing, and exploring partial-area taxonomy abstraction networks. The OAF includes support for various ontology representations (e.g., OWL and SNOMED CT's relational format). A Protégé plugin for deriving "live partial-area taxonomies" is demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

Integration of the social environment in a mobility ontology for people with motor disabilities.

PubMed

Gharebaghi, Amin; Mostafavi, Mir-Abolfazl; Edwards, Geoffrey; Fougeyrollas, Patrick; Gamache, Stéphanie; Grenier, Yan

2017-07-07

Our contemporary understanding of disability is rooted in the idea that disability is the product of human-environment interaction processes. People may be functionally limited, but this becomes a disability only when they engage with their immediate social and physical environments. Any attempt to address issues of mobility in relation to people with disabilities should be grounded in an ontology that encompasses this understanding. The objective of this study is to provide a methodology to integrate the social and physical environments in the development of a mobility ontology for people with motor disabilities (PWMD). We propose to create subclasses of concepts based on a Nature-Development distinction rather than creating separate social and physical subclasses. This allows the relationships between social and physical elements to be modelled in a more compact and efficient way by specifying them locally within each entity, and better accommodates the complexities of the human-environment interaction as well. Based on this approach, an ontology for mobility of PWMD considering four main elements - the social and physical environmental factors, human factors, life habits related to mobility and possible goals of mobility - is presented. We demonstrate that employing the Nature-Development perspective facilitates the process of developing useful ontologies, especially for defining the relationships between the social and physical parts of the environment. This is a fundamental issue for modelling the interaction between humans and their social and physical environments for a broad range of applications, including the development of geospatial assistive technologies for navigation of PWMD. Implications for rehabilitation The proposed perspective may actually have much broader interests beyond the issue of disability - much of the interesting dynamics in city development arises from the interaction between human-developed components - the built environment and its associated entities - and natural or organic components. The proposed approach facilitates the process of developing useful ontologies, especially for defining the relationships between the social and physical parts of the environment. This is a fundamental issue for modeling the interaction between human -specially people with disabilities -and his social and physical environments in a broad range of domains and applications, such as Geographic Information Systems and the development of geospatial assistive technologies for navigation of people with disabilities, respectively.

PhenoTips: patient phenotyping software for clinical and research use.

PubMed

Girdea, Marta; Dumitriu, Sergiu; Fiume, Marc; Bowdin, Sarah; Boycott, Kym M; Chénier, Sébastien; Chitayat, David; Faghfoury, Hanna; Meyn, M Stephen; Ray, Peter N; So, Joyce; Stavropoulos, Dimitri J; Brudno, Michael

2013-08-01

We have developed PhenoTips: open source software for collecting and analyzing phenotypic information for patients with genetic disorders. Our software combines an easy-to-use interface, compatible with any device that runs a Web browser, with a standardized database back end. The PhenoTips' user interface closely mirrors clinician workflows so as to facilitate the recording of observations made during the patient encounter. Collected data include demographics, medical history, family history, physical and laboratory measurements, physical findings, and additional notes. Phenotypic information is represented using the Human Phenotype Ontology; however, the complexity of the ontology is hidden behind a user interface, which combines simple selection of common phenotypes with error-tolerant, predictive search of the entire ontology. PhenoTips supports accurate diagnosis by analyzing the entered data, then suggesting additional clinical investigations and providing Online Mendelian Inheritance in Man (OMIM) links to likely disorders. By collecting, classifying, and analyzing phenotypic information during the patient encounter, PhenoTips allows for streamlining of clinic workflow, efficient data entry, improved diagnosis, standardization of collected patient phenotypes, and sharing of anonymized patient phenotype data for the study of rare disorders. Our source code and a demo version of PhenoTips are available at http://phenotips.org. © 2013 WILEY PERIODICALS, INC.

Modern architectures for intelligent systems: reusable ontologies and problem-solving methods.

PubMed Central

Musen, M. A.

1998-01-01

When interest in intelligent systems for clinical medicine soared in the 1970s, workers in medical informatics became particularly attracted to rule-based systems. Although many successful rule-based applications were constructed, development and maintenance of large rule bases remained quite problematic. In the 1980s, an entire industry dedicated to the marketing of tools for creating rule-based systems rose and fell, as workers in medical informatics began to appreciate deeply why knowledge acquisition and maintenance for such systems are difficult problems. During this time period, investigators began to explore alternative programming abstractions that could be used to develop intelligent systems. The notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) domain-independent problem-solving methods-standard algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper will highlight how intelligent systems for diverse tasks can be efficiently automated using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community. PMID:9929181

Modern architectures for intelligent systems: reusable ontologies and problem-solving methods.

PubMed

Musen, M A

1998-01-01

When interest in intelligent systems for clinical medicine soared in the 1970s, workers in medical informatics became particularly attracted to rule-based systems. Although many successful rule-based applications were constructed, development and maintenance of large rule bases remained quite problematic. In the 1980s, an entire industry dedicated to the marketing of tools for creating rule-based systems rose and fell, as workers in medical informatics began to appreciate deeply why knowledge acquisition and maintenance for such systems are difficult problems. During this time period, investigators began to explore alternative programming abstractions that could be used to develop intelligent systems. The notions of "generic tasks" and of reusable problem-solving methods became extremely influential. By the 1990s, academic centers were experimenting with architectures for intelligent systems based on two classes of reusable components: (1) domain-independent problem-solving methods-standard algorithms for automating stereotypical tasks--and (2) domain ontologies that captured the essential concepts (and relationships among those concepts) in particular application areas. This paper will highlight how intelligent systems for diverse tasks can be efficiently automated using these kinds of building blocks. The creation of domain ontologies and problem-solving methods is the fundamental end product of basic research in medical informatics. Consequently, these concepts need more attention by our scientific community.

Improving automation standards via semantic modelling: Application to ISA88.

PubMed

Dombayci, Canan; Farreres, Javier; Rodríguez, Horacio; Espuña, Antonio; Graells, Moisès

2017-03-01

Standardization is essential for automation. Extensibility, scalability, and reusability are important features for automation software that rely in the efficient modelling of the addressed systems. The work presented here is from the ongoing development of a methodology for semi-automatic ontology construction methodology from technical documents. The main aim of this work is to systematically check the consistency of technical documents and support the improvement of technical document consistency. The formalization of conceptual models and the subsequent writing of technical standards are simultaneously analyzed, and guidelines proposed for application to future technical standards. Three paradigms are discussed for the development of domain ontologies from technical documents, starting from the current state of the art, continuing with the intermediate method presented and used in this paper, and ending with the suggested paradigm for the future. The ISA88 Standard is taken as a representative case study. Linguistic techniques from the semi-automatic ontology construction methodology is applied to the ISA88 Standard and different modelling and standardization aspects that are worth sharing with the automation community is addressed. This study discusses different paradigms for developing and sharing conceptual models for the subsequent development of automation software, along with presenting the systematic consistency checking method. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Benchmarking Ontologies: Bigger or Better?

PubMed Central

Yao, Lixia; Divoli, Anna; Mayzus, Ilya; Evans, James A.; Rzhetsky, Andrey

2011-01-01

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them. PMID:21249231

Use artificial neural network to align biological ontologies.

PubMed

Huang, Jingshan; Dang, Jiangbo; Huhns, Michael N; Zheng, W Jim

2008-09-16

Being formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics. In this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others. The promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

Improving the interoperability of biomedical ontologies with compound alignments.

PubMed

Oliveira, Daniela; Pesquita, Catia

2018-01-09

Ontologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration and interoperability among heterogeneous data sources, when these sources are annotated with distinct ontologies, bridging this gap can be challenging. In the last decade, ontology matching systems have been evolving and are now capable of producing high-quality mappings for life sciences ontologies, usually limited to the equivalence between two ontologies. However, life sciences research is becoming increasingly transdisciplinary and integrative, fostering the need to develop matching strategies that are able to handle multiple ontologies and more complex relations between their concepts. We have developed ontology matching algorithms that are able to find compound mappings between multiple biomedical ontologies, in the form of ternary mappings, finding for instance that "aortic valve stenosis"(HP:0001650) is equivalent to the intersection between "aortic valve"(FMA:7236) and "constricted" (PATO:0001847). The algorithms take advantage of search space filtering based on partial mappings between ontology pairs, to be able to handle the increased computational demands. The evaluation of the algorithms has shown that they are able to produce meaningful results, with precision in the range of 60-92% for new mappings. The algorithms were also applied to the potential extension of logical definitions of the OBO and the matching of several plant-related ontologies. This work is a first step towards finding more complex relations between multiple ontologies. The evaluation shows that the results produced are significant and that the algorithms could satisfy specific integration needs.

Developing a semantically rich ontology for the biobank-administration domain

PubMed Central

2013-01-01

Background Biobanks are a critical resource for translational science. Recently, semantic web technologies such as ontologies have been found useful in retrieving research data from biobanks. However, recent research has also shown that there is a lack of data about the administrative aspects of biobanks. These data would be helpful to answer research-relevant questions such as what is the scope of specimens collected in a biobank, what is the curation status of the specimens, and what is the contact information for curators of biobanks. Our use cases include giving researchers the ability to retrieve key administrative data (e.g. contact information, contact's affiliation, etc.) about the biobanks where specific specimens of interest are stored. Thus, our goal is to provide an ontology that represents the administrative entities in biobanking and their relations. We base our ontology development on a set of 53 data attributes called MIABIS, which were in part the result of semantic integration efforts of the European Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). The previous work on MIABIS provided the domain analysis for our ontology. We report on a test of our ontology against competency questions that we derived from the initial BBMRI use cases. Future work includes additional ontology development to answer additional competency questions from these use cases. Results We created an open-source ontology of biobank administration called Ontologized MIABIS (OMIABIS) coded in OWL 2.0 and developed according to the principles of the OBO Foundry. It re-uses pre-existing ontologies when possible in cooperation with developers of other ontologies in related domains, such as the Ontology of Biomedical Investigation. OMIABIS provides a formalized representation of biobanks and their administration. Using the ontology and a set of Description Logic queries derived from the competency questions that we identified, we were able to retrieve test data with perfect accuracy. In addition, we began development of a mapping from the ontology to pre-existing biobank data structures commonly used in the U.S. Conclusions In conclusion, we created OMIABIS, an ontology of biobank administration. We found that basing its development on pre-existing resources to meet the BBMRI use cases resulted in a biobanking ontology that is re-useable in environments other than BBMRI. Our ontology retrieved all true positives and no false positives when queried according to the competency questions we derived from the BBMRI use cases. Mapping OMIABIS to a data structure used for biospecimen collections in a medical center in Little Rock, AR showed adequate coverage of our ontology. PMID:24103726

Efficiently Selecting the Best Web Services

NASA Astrophysics Data System (ADS)

Goncalves, Marlene; Vidal, Maria-Esther; Regalado, Alfredo; Yacoubi Ayadi, Nadia

Emerging technologies and linking data initiatives have motivated the publication of a large number of datasets, and provide the basis for publishing Web services and tools to manage the available data. This wealth of resources opens a world of possibilities to satisfy user requests. However, Web services may have similar functionality and assess different performance; therefore, it is required to identify among the Web services that satisfy a user request, the ones with the best quality. In this paper we propose a hybrid approach that combines reasoning tasks with ranking techniques to aim at the selection of the Web services that best implement a user request. Web service functionalities are described in terms of input and output attributes annotated with existing ontologies, non-functionality is represented as Quality of Services (QoS) parameters, and user requests correspond to conjunctive queries whose sub-goals impose restrictions on the functionality and quality of the services to be selected. The ontology annotations are used in different reasoning tasks to infer service implicit properties and to augment the size of the service search space. Furthermore, QoS parameters are considered by a ranking metric to classify the services according to how well they meet a user non-functional condition. We assume that all the QoS parameters of the non-functional condition are equally important, and apply the Top-k Skyline approach to select the k services that best meet this condition. Our proposal relies on a two-fold solution which fires a deductive-based engine that performs different reasoning tasks to discover the services that satisfy the requested functionality, and an efficient implementation of the Top-k Skyline approach to compute the top-k services that meet the majority of the QoS constraints. Our Top-k Skyline solution exploits the properties of the Skyline Frequency metric and identifies the top-k services by just analyzing a subset of the services that meet the non-functional condition. We report on the effects of the proposed reasoning tasks, the quality of the top-k services selected by the ranking metric, and the performance of the proposed ranking techniques. Our results suggest that the number of services can be augmented by up two orders of magnitude. In addition, our ranking techniques are able to identify services that have the best values in at least half of the QoS parameters, while the performance is improved.

Towards Ontology-Driven Information Systems: Guidelines to the Creation of New Methodologies to Build Ontologies

ERIC Educational Resources Information Center

Soares, Andrey

2009-01-01

This research targeted the area of Ontology-Driven Information Systems, where ontology plays a central role both at development time and at run time of Information Systems (IS). In particular, the research focused on the process of building domain ontologies for IS modeling. The motivation behind the research was the fact that researchers have…

Vaccine and Drug Ontology Studies (VDOS 2014).

PubMed

Tao, Cui; He, Yongqun; Arabandi, Sivaram

2016-01-01

The "Vaccine and Drug Ontology Studies" (VDOS) international workshop series focuses on vaccine- and drug-related ontology modeling and applications. Drugs and vaccines have been critical to prevent and treat human and animal diseases. Work in both (drugs and vaccines) areas is closely related - from preclinical research and development to manufacturing, clinical trials, government approval and regulation, and post-licensure usage surveillance and monitoring. Over the last decade, tremendous efforts have been made in the biomedical ontology community to ontologically represent various areas associated with vaccines and drugs - extending existing clinical terminology systems such as SNOMED, RxNorm, NDF-RT, and MedDRA, developing new models such as the Vaccine Ontology (VO) and Ontology of Adverse Events (OAE), vernacular medical terminologies such as the Consumer Health Vocabulary (CHV). The VDOS workshop series provides a platform for discussing innovative solutions as well as the challenges in the development and applications of biomedical ontologies for representing and analyzing drugs and vaccines, their administration, host immune responses, adverse events, and other related topics. The five full-length papers included in this 2014 thematic issue focus on two main themes: (i) General vaccine/drug-related ontology development and exploration, and (ii) Interaction and network-related ontology studies.

Predicting the Extension of Biomedical Ontologies

PubMed Central

Pesquita, Catia; Couto, Francisco M.

2012-01-01

Developing and extending a biomedical ontology is a very demanding task that can never be considered complete given our ever-evolving understanding of the life sciences. Extension in particular can benefit from the automation of some of its steps, thus releasing experts to focus on harder tasks. Here we present a strategy to support the automation of change capturing within ontology extension where the need for new concepts or relations is identified. Our strategy is based on predicting areas of an ontology that will undergo extension in a future version by applying supervised learning over features of previous ontology versions. We used the Gene Ontology as our test bed and obtained encouraging results with average f-measure reaching 0.79 for a subset of biological process terms. Our strategy was also able to outperform state of the art change capturing methods. In addition we have identified several issues concerning prediction of ontology evolution, and have delineated a general framework for ontology extension prediction. Our strategy can be applied to any biomedical ontology with versioning, to help focus either manual or semi-automated extension methods on areas of the ontology that need extension. PMID:23028267

Integrating HL7 RIM and ontology for unified knowledge and data representation in clinical decision support systems.

PubMed

Zhang, Yi-Fan; Tian, Yu; Zhou, Tian-Shu; Araki, Kenji; Li, Jing-Song

2016-01-01

The broad adoption of clinical decision support systems within clinical practice has been hampered mainly by the difficulty in expressing domain knowledge and patient data in a unified formalism. This paper presents a semantic-based approach to the unified representation of healthcare domain knowledge and patient data for practical clinical decision making applications. A four-phase knowledge engineering cycle is implemented to develop a semantic healthcare knowledge base based on an HL7 reference information model, including an ontology to model domain knowledge and patient data and an expression repository to encode clinical decision making rules and queries. A semantic clinical decision support system is designed to provide patient-specific healthcare recommendations based on the knowledge base and patient data. The proposed solution is evaluated in the case study of type 2 diabetes mellitus inpatient management. The knowledge base is successfully instantiated with relevant domain knowledge and testing patient data. Ontology-level evaluation confirms model validity. Application-level evaluation of diagnostic accuracy reaches a sensitivity of 97.5%, a specificity of 100%, and a precision of 98%; an acceptance rate of 97.3% is given by domain experts for the recommended care plan orders. The proposed solution has been successfully validated in the case study as providing clinical decision support at a high accuracy and acceptance rate. The evaluation results demonstrate the technical feasibility and application prospect of our approach. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

A task-based support architecture for developing point-of-care clinical decision support systems for the emergency department.

PubMed

Wilk, S; Michalowski, W; O'Sullivan, D; Farion, K; Sayyad-Shirabad, J; Kuziemsky, C; Kukawka, B

2013-01-01

The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter. The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate. The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE--a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED. The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.

Unintended consequences of existential quantifications in biomedical ontologies

PubMed Central

2011-01-01

Background The Open Biomedical Ontologies (OBO) Foundry is a collection of freely available ontologically structured controlled vocabularies in the biomedical domain. Most of them are disseminated via both the OBO Flatfile Format and the semantic web format Web Ontology Language (OWL), which draws upon formal logic. Based on the interpretations underlying OWL description logics (OWL-DL) semantics, we scrutinize the OWL-DL releases of OBO ontologies to assess whether their logical axioms correspond to the meaning intended by their authors. Results We analyzed ontologies and ontology cross products available via the OBO Foundry site http://www.obofoundry.org for existential restrictions (someValuesFrom), from which we examined a random sample of 2,836 clauses. According to a rating done by four experts, 23% of all existential restrictions in OBO Foundry candidate ontologies are suspicious (Cohens' κ = 0.78). We found a smaller proportion of existential restrictions in OBO Foundry cross products are suspicious, but in this case an accurate quantitative judgment is not possible due to a low inter-rater agreement (κ = 0.07). We identified several typical modeling problems, for which satisfactory ontology design patterns based on OWL-DL were proposed. We further describe several usability issues with OBO ontologies, including the lack of ontological commitment for several common terms, and the proliferation of domain-specific relations. Conclusions The current OWL releases of OBO Foundry (and Foundry candidate) ontologies contain numerous assertions which do not properly describe the underlying biological reality, or are ambiguous and difficult to interpret. The solution is a better anchoring in upper ontologies and a restriction to relatively few, well defined relation types with given domain and range constraints. PMID:22115278