Knowledge-based machine indexing from natural language text: Knowledge base design, development, and maintenance

NASA Technical Reports Server (NTRS)

Genuardi, Michael T.

1993-01-01

One strategy for machine-aided indexing (MAI) is to provide a concept-level analysis of the textual elements of documents or document abstracts. In such systems, natural-language phrases are analyzed in order to identify and classify concepts related to a particular subject domain. The overall performance of these MAI systems is largely dependent on the quality and comprehensiveness of their knowledge bases. These knowledge bases function to (1) define the relations between a controlled indexing vocabulary and natural language expressions; (2) provide a simple mechanism for disambiguation and the determination of relevancy; and (3) allow the extension of concept-hierarchical structure to all elements of the knowledge file. After a brief description of the NASA Machine-Aided Indexing system, concerns related to the development and maintenance of MAI knowledge bases are discussed. Particular emphasis is given to statistically-based text analysis tools designed to aid the knowledge base developer. One such tool, the Knowledge Base Building (KBB) program, presents the domain expert with a well-filtered list of synonyms and conceptually-related phrases for each thesaurus concept. Another tool, the Knowledge Base Maintenance (KBM) program, functions to identify areas of the knowledge base affected by changes in the conceptual domain (for example, the addition of a new thesaurus term). An alternate use of the KBM as an aid in thesaurus construction is also discussed.

How Structure Shapes Dynamics: Knowledge Development in Wikipedia - A Network Multilevel Modeling Approach

PubMed Central

Halatchliyski, Iassen; Cress, Ulrike

2014-01-01

Using a longitudinal network analysis approach, we investigate the structural development of the knowledge base of Wikipedia in order to explain the appearance of new knowledge. The data consists of the articles in two adjacent knowledge domains: psychology and education. We analyze the development of networks of knowledge consisting of interlinked articles at seven snapshots from 2006 to 2012 with an interval of one year between them. Longitudinal data on the topological position of each article in the networks is used to model the appearance of new knowledge over time. Thus, the structural dimension of knowledge is related to its dynamics. Using multilevel modeling as well as eigenvector and betweenness measures, we explain the significance of pivotal articles that are either central within one of the knowledge domains or boundary-crossing between the two domains at a given point in time for the future development of new knowledge in the knowledge base. PMID:25365319

Using CLIPS in the domain of knowledge-based massively parallel programming

NASA Technical Reports Server (NTRS)

Dvorak, Jiri J.

1994-01-01

The Program Development Environment (PDE) is a tool for massively parallel programming of distributed-memory architectures. Adopting a knowledge-based approach, the PDE eliminates the complexity introduced by parallel hardware with distributed memory and offers complete transparency in respect of parallelism exploitation. The knowledge-based part of the PDE is realized in CLIPS. Its principal task is to find an efficient parallel realization of the application specified by the user in a comfortable, abstract, domain-oriented formalism. A large collection of fine-grain parallel algorithmic skeletons, represented as COOL objects in a tree hierarchy, contains the algorithmic knowledge. A hybrid knowledge base with rule modules and procedural parts, encoding expertise about application domain, parallel programming, software engineering, and parallel hardware, enables a high degree of automation in the software development process. In this paper, important aspects of the implementation of the PDE using CLIPS and COOL are shown, including the embedding of CLIPS with C++-based parts of the PDE. The appropriateness of the chosen approach and of the CLIPS language for knowledge-based software engineering are discussed.

A feature dictionary supporting a multi-domain medical knowledge base.

PubMed

Naeymi-Rad, F

1989-01-01

Because different terminology is used by physicians of different specialties in different locations to refer to the same feature (signs, symptoms, test results), it is essential that our knowledge development tools provide a means to access a common pool of terms. This paper discusses the design of an online medical dictionary that provides a solution to this problem for developers of multi-domain knowledge bases for MEDAS (Medical Emergency Decision Assistance System). Our Feature Dictionary supports phrase equivalents for features, feature interactions, feature classifications, and translations to the binary features generated by the expert during knowledge creation. It is also used in the conversion of a domain knowledge to the database used by the MEDAS inference diagnostic sessions. The Feature Dictionary also provides capabilities for complex queries across multiple domains using the supported relations. The Feature Dictionary supports three methods for feature representation: (1) for binary features, (2) for continuous valued features, and (3) for derived features.

Knowledge-based approach for generating target system specifications from a domain model

NASA Technical Reports Server (NTRS)

Gomaa, Hassan; Kerschberg, Larry; Sugumaran, Vijayan

1992-01-01

Several institutions in industry and academia are pursuing research efforts in domain modeling to address unresolved issues in software reuse. To demonstrate the concepts of domain modeling and software reuse, a prototype software engineering environment is being developed at George Mason University to support the creation of domain models and the generation of target system specifications. This prototype environment, which is application domain independent, consists of an integrated set of commercial off-the-shelf software tools and custom-developed software tools. This paper describes the knowledge-based tool that was developed as part of the environment to generate target system specifications from a domain model.

PSYCHE: An Object-Oriented Approach to Simulating Medical Education

PubMed Central

Mullen, Jamie A.

1990-01-01

Traditional approaches to computer-assisted instruction (CAI) do not provide realistic simulations of medical education, in part because they do not utilize heterogeneous knowledge bases for their source of domain knowledge. PSYCHE, a CAI program designed to teach hypothetico-deductive psychiatric decision-making to medical students, uses an object-oriented implementation of an intelligent tutoring system (ITS) to model the student, domain expert, and tutor. It models the transactions between the participants in complex transaction chains, and uses heterogeneous knowledge bases to represent both domain and procedural knowledge in clinical medicine. This object-oriented approach is a flexible and dynamic approach to modeling, and represents a potentially valuable tool for the investigation of medical education and decision-making.

Knowledge Base Editor (SharpKBE)

NASA Technical Reports Server (NTRS)

Tikidjian, Raffi; James, Mark; Mackey, Ryan

2007-01-01

The SharpKBE software provides a graphical user interface environment for domain experts to build and manage knowledge base systems. Knowledge bases can be exported/translated to various target languages automatically, including customizable target languages.

Technological Pedagogical Content Knowledge of Prospective Mathematics Teacher in Three Dimensional Material Based on Sex Differences

NASA Astrophysics Data System (ADS)

Aqib, M. A.; Budiarto, M. T.; Wijayanti, P.

2018-01-01

The effectiveness of learning in this era can be seen from 3 factors such as: technology, content, and pedagogy that covered in Technological Pedagogical Content Knowledge (TPCK). This research was a qualitative research which aimed to describe each domain from TPCK include Content Knowledge, Pedagogical Knowledge, Pedagogical Content Knowledge, Technological Knowledge, Technological Content Knowledge, Technological Pedagogical Knowledge and Technological, Pedagogical, and Content Knowledge. The subjects of this research were male and female mathematics college students at least 5th semester who has almost the same ability for some course like innovative learning, innovative learning II, school mathematics I, school mathematics II, computer applications and instructional media. Research began by spreading the questionnaire of subject then continued with the assignment and interview. The obtained data was validated by time triangulation.This research has result that male and female prospective teacher was relatively same for Content Knowledge and Pedagogical Knowledge domain. While it was difference in the Technological Knowledge domain. The difference in this domain certainly has an impact on other domains that has technology components on it. Although it can be minimized by familiarizing the technology.

Knowledge acquisition for temporal abstraction.

PubMed

Stein, A; Musen, M A; Shahar, Y

1996-01-01

Temporal abstraction is the task of detecting relevant patterns in data over time. The knowledge-based temporal-abstraction method uses knowledge about a clinical domain's contexts, external events, and parameters to create meaningful interval-based abstractions from raw time-stamped clinical data. In this paper, we describe the acquisition and maintenance of domain-specific temporal-abstraction knowledge. Using the PROTEGE-II framework, we have designed a graphical tool for acquiring temporal knowledge directly from expert physicians, maintaining the knowledge in a sharable form, and converting the knowledge into a suitable format for use by an appropriate problem-solving method. In initial tests, the tool offered significant gains in our ability to rapidly acquire temporal knowledge and to use that knowledge to perform automated temporal reasoning.

M-and-C Domain Map Maker: an environment complimenting MDE with M-and-C knowledge and ensuring solution completeness

NASA Astrophysics Data System (ADS)

Patwari, Puneet; Choudhury, Subhrojyoti R.; Banerjee, Amar; Swaminathan, N.; Pandey, Shreya

2016-07-01

Model Driven Engineering (MDE) as a key driver to reduce development cost of M&C systems is beginning to find acceptance across scientific instruments such as Radio Telescopes and Nuclear Reactors. Such projects are adopting it to reduce time to integrate, test and simulate their individual controllers and increase reusability and traceability in the process. The creation and maintenance of models is still a significant challenge to realizing MDE benefits. Creating domain-specific modelling environments reduces the barriers, and we have been working along these lines, creating a domain-specific language and environment based on an M&C knowledge model. However, large projects involve several such domains, and there is still a need to interconnect the domain models, in order to ensure modelling completeness. This paper presents a knowledge-centric approach to doing that, by creating a generic system model that underlies the individual domain knowledge models. We present our vision for M&C Domain Map Maker, a set of processes and tools that enables explication of domain knowledge in terms of domain models with mutual consistency relationships to aid MDE.

International trends in solid-state lighting : analyses of the article and patent literature.

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

Tsao, Jeffrey Yeenien; Huey, Mark C.; Boyack, Kevin W.

We present an analysis of the literature of solid-state lighting, based on a comprehensive dataset of 35,851 English-language articles and 12,420 U.S. patents published or issued during the years 1977-2004 in the foundational knowledge domain of electroluminescent materials and phenomena. The dataset was created using a complex, iteratively developed search string. The records in the dataset were then partitioned according to: whether they are articles or patents, their publication or issue date, their national or continental origin, whether the active electroluminescent material was inorganic or organic, and which of a number of emergent knowledge sub-domains they aggregate into on themore » basis of bibliographic coupling. From these partitionings, we performed a number of analyses, including: identification of knowledge sub-domains of historical and recent importance, and trends over time of the contributions of various nations and continents to the knowledge domain and its sub-domains. Among the key results: (1) The knowledge domain as a whole has been growing quickly: the average growth rates of the inorganic and organic knowledge sub-domains have been 8%/yr and 25%/yr, respectively, compared to average growth rates less than 5%/yr for English-language articles and U.S. patents in other knowledge domains. The growth rate of the organic knowledge sub-domain is so high that its historical dominance by the inorganic knowledge sub-domain will, at current trajectories, be reversed in the coming decade. (2) Amongst nations, the U.S. is the largest contributor to the overall knowledge domain, but Japan is on a trajectory to become the largest contributor within the coming half-decade. Amongst continents, Asia became the largest contributor during the past half-decade, overwhelmingly so for the organic knowledge sub-domain. (3) The relative contributions to the article and patent datasets differ for the major continents: North America contributing relatively more patents, Europe contributing relatively more articles, and Asia contributing in a more balanced fashion. (4) For the article dataset, the nations that contribute most in quantity also contribute most in breadth, while the nations that contribute less in quantity concentrate their contributions in particular knowledge sub-domains. For the patent dataset, North America and Europe tend to contribute improvements in end-use applications (e.g., in sensing, phototherapy and communications), while Asia tends to contribute improvements at the materials and chip levels. (5) The knowledge sub-domains that emerge from aggregations based on bibliographic coupling are roughly organized, for articles, by the degree of localization of electrons and holes in the material or phenomenon of interest, and for patents, according to both their emphasis on chips, systems or applications, and their emphasis on organic or inorganic materials. (6) The six 'hottest' topics in the article dataset are: spintronics, AlGaN UV LEDs, nanowires, nanophosphors, polyfluorenes and electrophosphorescence. The nine 'hottest' topics in the patent dataset are: OLED encapsulation, active-matrix displays, multicolor OLEDs, thermal transfer for OLED fabrication, ink-jet printed OLEDs, phosphor-converted LEDs, ornamental LED packages, photocuring and phototherapy, and LED retrofitting lamps. A significant caution in interpreting these results is that they are based on English-language articles and U.S. patents, and hence will tend to over-represent the strength of English-speaking nations (particularly the U.S.), and under-represent the strength of non-English-speaking nations (particularly China).« less

Web-Based Undergraduate Chemistry Problem-Solving: The Interplay of Task Performance, Domain Knowledge and Web-Searching Strategies

ERIC Educational Resources Information Center

She, Hsiao-Ching; Cheng, Meng-Tzu; Li, Ta-Wei; Wang, Chia-Yu; Chiu, Hsin-Tien; Lee, Pei-Zon; Chou, Wen-Chi; Chuang, Ming-Hua

2012-01-01

This study investigates the effect of Web-based Chemistry Problem-Solving, with the attributes of Web-searching and problem-solving scaffolds, on undergraduate students' problem-solving task performance. In addition, the nature and extent of Web-searching strategies students used and its correlation with task performance and domain knowledge also…

Requirements analysis, domain knowledge, and design

NASA Technical Reports Server (NTRS)

Potts, Colin

1988-01-01

Two improvements to current requirements analysis practices are suggested: domain modeling, and the systematic application of analysis heuristics. Domain modeling is the representation of relevant application knowledge prior to requirements specification. Artificial intelligence techniques may eventually be applicable for domain modeling. In the short term, however, restricted domain modeling techniques, such as that in JSD, will still be of practical benefit. Analysis heuristics are standard patterns of reasoning about the requirements. They usually generate questions of clarification or issues relating to completeness. Analysis heuristics can be represented and therefore systematically applied in an issue-based framework. This is illustrated by an issue-based analysis of JSD's domain modeling and functional specification heuristics. They are discussed in the context of the preliminary design of simple embedded systems.

The Meta-Ontology Model of the Fishdisease Diagnostic Knowledge Based on Owl

NASA Astrophysics Data System (ADS)

Shi, Yongchang; Gao, Wen; Hu, Liang; Fu, Zetian

For improving available and reusable of knowledge in fish disease diagnosis (FDD) domain and facilitating knowledge acquisition, an ontology model of FDD knowledge was developed based on owl according to FDD knowledge model. It includes terminology of terms in FDD knowledge and hierarchies of their class.

Cognitive Demand Differences in Causal Inferences: Characters' Plans Are More Difficult to Comprehend than Physical Causation

ERIC Educational Resources Information Center

Shears, Connie; Miller, Vanessa; Ball, Megan; Hawkins, Amanda; Griggs, Janna; Varner, Andria

2007-01-01

Readers may draw knowledge-based inferences to connect sentences in text differently depending on the knowledge domain being accessed. Most prior research has focused on the direction of the causal explanation (predictive vs. backward) without regard to the knowledge domain drawn on to support comprehension. We suggest that less cognitive effort…

The Knowledge Base and Memory Performance: A Comparison of Academically Successful and Unsuccessful Learners. Paper 5/1987.

ERIC Educational Resources Information Center

Schneider, Wolfgang; And Others

The expert-novice paradigm, which demonstrates the outstanding role of domain-specific knowledge in explaining differences in memory behavior and performance, was examined. Two studies are described which compared memory performance of groups equivalent with regard to domain-specific knowledge but differing in intellectual ability. The hypothesis…

Relating GTE and Knowledge-Based Courseware Engineering: Some Epistemological Issues.

ERIC Educational Resources Information Center

De Diana, Italo P. F.; Ladhani, Al-Noor

1998-01-01

Discusses GTE (Generic Tutoring Environment) and knowledge-based courseware engineering from an epistemological point of view and suggests some combination of the two approaches. Topics include intelligent tutoring; courseware authoring; application versus acquisition of knowledge; and domain knowledge. (LRW)

Ontology-based classification of remote sensing images using spectral rules

NASA Astrophysics Data System (ADS)

Andrés, Samuel; Arvor, Damien; Mougenot, Isabelle; Libourel, Thérèse; Durieux, Laurent

2017-05-01

Earth Observation data is of great interest for a wide spectrum of scientific domain applications. An enhanced access to remote sensing images for "domain" experts thus represents a great advance since it allows users to interpret remote sensing images based on their domain expert knowledge. However, such an advantage can also turn into a major limitation if this knowledge is not formalized, and thus is difficult for it to be shared with and understood by other users. In this context, knowledge representation techniques such as ontologies should play a major role in the future of remote sensing applications. We implemented an ontology-based prototype to automatically classify Landsat images based on explicit spectral rules. The ontology is designed in a very modular way in order to achieve a generic and versatile representation of concepts we think of utmost importance in remote sensing. The prototype was tested on four subsets of Landsat images and the results confirmed the potential of ontologies to formalize expert knowledge and classify remote sensing images.

What can management theories offer evidence-based practice? A comparative analysis of measurement tools for organisational context.

PubMed

French, Beverley; Thomas, Lois H; Baker, Paula; Burton, Christopher R; Pennington, Lindsay; Roddam, Hazel

2009-05-19

Given the current emphasis on networks as vehicles for innovation and change in health service delivery, the ability to conceptualize and measure organisational enablers for the social construction of knowledge merits attention. This study aimed to develop a composite tool to measure the organisational context for evidence-based practice (EBP) in healthcare. A structured search of the major healthcare and management databases for measurement tools from four domains: research utilisation (RU), research activity (RA), knowledge management (KM), and organisational learning (OL). Included studies were reports of the development or use of measurement tools that included organisational factors. Tools were appraised for face and content validity, plus development and testing methods. Measurement tool items were extracted, merged across the four domains, and categorised within a constructed framework describing the absorptive and receptive capacities of organisations. Thirty measurement tools were identified and appraised. Eighteen tools from the four domains were selected for item extraction and analysis. The constructed framework consists of seven categories relating to three core organisational attributes of vision, leadership, and a learning culture, and four stages of knowledge need, acquisition of new knowledge, knowledge sharing, and knowledge use. Measurement tools from RA or RU domains had more items relating to the categories of leadership, and acquisition of new knowledge; while tools from KM or learning organisation domains had more items relating to vision, learning culture, knowledge need, and knowledge sharing. There was equal emphasis on knowledge use in the different domains. If the translation of evidence into knowledge is viewed as socially mediated, tools to measure the organisational context of EBP in healthcare could be enhanced by consideration of related concepts from the organisational and management sciences. Comparison of measurement tools across domains suggests that there is scope within EBP for supplementing the current emphasis on human and technical resources to support information uptake and use by individuals. Consideration of measurement tools from the fields of KM and OL shows more content related to social mechanisms to facilitate knowledge recognition, translation, and transfer between individuals and groups.

What can management theories offer evidence-based practice? A comparative analysis of measurement tools for organisational context

PubMed Central

French, Beverley; Thomas, Lois H; Baker, Paula; Burton, Christopher R; Pennington, Lindsay; Roddam, Hazel

2009-01-01

Background Given the current emphasis on networks as vehicles for innovation and change in health service delivery, the ability to conceptualise and measure organisational enablers for the social construction of knowledge merits attention. This study aimed to develop a composite tool to measure the organisational context for evidence-based practice (EBP) in healthcare. Methods A structured search of the major healthcare and management databases for measurement tools from four domains: research utilisation (RU), research activity (RA), knowledge management (KM), and organisational learning (OL). Included studies were reports of the development or use of measurement tools that included organisational factors. Tools were appraised for face and content validity, plus development and testing methods. Measurement tool items were extracted, merged across the four domains, and categorised within a constructed framework describing the absorptive and receptive capacities of organisations. Results Thirty measurement tools were identified and appraised. Eighteen tools from the four domains were selected for item extraction and analysis. The constructed framework consists of seven categories relating to three core organisational attributes of vision, leadership, and a learning culture, and four stages of knowledge need, acquisition of new knowledge, knowledge sharing, and knowledge use. Measurement tools from RA or RU domains had more items relating to the categories of leadership, and acquisition of new knowledge; while tools from KM or learning organisation domains had more items relating to vision, learning culture, knowledge need, and knowledge sharing. There was equal emphasis on knowledge use in the different domains. Conclusion If the translation of evidence into knowledge is viewed as socially mediated, tools to measure the organisational context of EBP in healthcare could be enhanced by consideration of related concepts from the organisational and management sciences. Comparison of measurement tools across domains suggests that there is scope within EBP for supplementing the current emphasis on human and technical resources to support information uptake and use by individuals. Consideration of measurement tools from the fields of KM and OL shows more content related to social mechanisms to facilitate knowledge recognition, translation, and transfer between individuals and groups. PMID:19454008

Design and Implementation of Hydrologic Process Knowledge-base Ontology: A case study for the Infiltration Process

NASA Astrophysics Data System (ADS)

Elag, M.; Goodall, J. L.

2013-12-01

Hydrologic modeling often requires the re-use and integration of models from different disciplines to simulate complex environmental systems. Component-based modeling introduces a flexible approach for integrating physical-based processes across disciplinary boundaries. Several hydrologic-related modeling communities have adopted the component-based approach for simulating complex physical systems by integrating model components across disciplinary boundaries in a workflow. However, it is not always straightforward to create these interdisciplinary models due to the lack of sufficient knowledge about a hydrologic process. This shortcoming is a result of using informal methods for organizing and sharing information about a hydrologic process. A knowledge-based ontology provides such standards and is considered the ideal approach for overcoming this challenge. The aims of this research are to present the methodology used in analyzing the basic hydrologic domain in order to identify hydrologic processes, the ontology itself, and how the proposed ontology is integrated with the Water Resources Component (WRC) ontology. The proposed ontology standardizes the definitions of a hydrologic process, the relationships between hydrologic processes, and their associated scientific equations. The objective of the proposed Hydrologic Process (HP) Ontology is to advance the idea of creating a unified knowledge framework for components' metadata by introducing a domain-level ontology for hydrologic processes. The HP ontology is a step toward an explicit and robust domain knowledge framework that can be evolved through the contribution of domain users. Analysis of the hydrologic domain is accomplished using the Formal Concept Approach (FCA), in which the infiltration process, an important hydrologic process, is examined. Two infiltration methods, the Green-Ampt and Philip's methods, were used to demonstrate the implementation of information in the HP ontology. Furthermore, a SPARQL service is provided for semantic-based querying of the ontology.

Knowledge-rich temporal relation identification and classification in clinical notes

PubMed Central

D’Souza, Jennifer; Ng, Vincent

2014-01-01

Motivation: We examine the task of temporal relation classification for the clinical domain. Our approach to this task departs from existing ones in that it is (i) ‘knowledge-rich’, employing sophisticated knowledge derived from discourse relations as well as both domain-independent and domain-dependent semantic relations, and (ii) ‘hybrid’, combining the strengths of rule-based and learning-based approaches. Evaluation results on the i2b2 Clinical Temporal Relations Challenge corpus show that our approach yields a 17–24% and 8–14% relative reduction in error over a state-of-the-art learning-based baseline system when gold-standard and automatically identified temporal relations are used, respectively. Database URL: http://www.hlt.utdallas.edu/~jld082000/temporal-relations/ PMID:25414383

Formalizing Knowledge in Multi-Scale Agent-Based Simulations

PubMed Central

Somogyi, Endre; Sluka, James P.; Glazier, James A.

2017-01-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused. PMID:29338063

Formalizing Knowledge in Multi-Scale Agent-Based Simulations.

PubMed

Somogyi, Endre; Sluka, James P; Glazier, James A

2016-10-01

Multi-scale, agent-based simulations of cellular and tissue biology are increasingly common. These simulations combine and integrate a range of components from different domains. Simulations continuously create, destroy and reorganize constituent elements causing their interactions to dynamically change. For example, the multi-cellular tissue development process coordinates molecular, cellular and tissue scale objects with biochemical, biomechanical, spatial and behavioral processes to form a dynamic network. Different domain specific languages can describe these components in isolation, but cannot describe their interactions. No current programming language is designed to represent in human readable and reusable form the domain specific knowledge contained in these components and interactions. We present a new hybrid programming language paradigm that naturally expresses the complex multi-scale objects and dynamic interactions in a unified way and allows domain knowledge to be captured, searched, formalized, extracted and reused.

CARDS: A blueprint and environment for domain-specific software reuse

NASA Technical Reports Server (NTRS)

Wallnau, Kurt C.; Solderitsch, Anne Costa; Smotherman, Catherine

1992-01-01

CARDS (Central Archive for Reusable Defense Software) exploits advances in domain analysis and domain modeling to identify, specify, develop, archive, retrieve, understand, and reuse domain-specific software components. An important element of CARDS is to provide visibility into the domain model artifacts produced by, and services provided by, commercial computer-aided software engineering (CASE) technology. The use of commercial CASE technology is important to provide rich, robust support for the varied roles involved in a reuse process. We refer to this kind of use of knowledge representation systems as supporting 'knowledge-based integration.'

Canadian residents' perceived manager training needs.

PubMed

Stergiopoulos, Vicky; Lieff, Susan; Razack, Saleem; Lee, A Curtis; Maniate, Jerry M; Hyde, Stacey; Taber, Sarah; Frank, Jason R

2010-01-01

Despite widespread endorsement for administrative training during residency, teaching and learning in this area remains intermittent and limited in most programmes. To inform the development of a Manager Train-the-Trainer program for faculty, the Royal College of Physicians and Surgeons of Canada undertook a survey of perceived Manager training needs among postgraduate trainees. A representative sample of Canadian specialty residents received a web-based questionnaire in 2009 assessing their perceived deficiencies in 13 Manager knowledge and 11 Manager skill domains, as determined by gap scores (GSs). GSs were defined as the difference between residents' perceived current and desired level of knowledge or skill in selected Manager domains. Residents' educational preferences for furthering their Manager knowledge and skills were also elicited. Among the 549 residents who were emailed the survey, 199 (36.2%) responded. Residents reported significant gaps in most knowledge and skills domains examined. Residents' preferred educational methods for learning Manager knowledge and skills included workshops, web-based formats and interactive small groups. The results of this national survey, highlighting significant perceived gaps in multiple Manager knowledge and skills domains, may inform the development of Manager curricula and faculty development activities to address deficiencies in training in this important area.

Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods

PubMed Central

Chasin, Rachel; Rumshisky, Anna; Uzuner, Ozlem; Szolovits, Peter

2014-01-01

Objective To evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific knowledge sources. Materials and methods The graph-based methods use variations of PageRank and distance-based similarity metrics, operating over the Unified Medical Language System (UMLS). Topic-modeling methods use unlabeled data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database to derive models for each ambiguous word. We investigate the impact of using different linguistic features for topic models, including UMLS-based and syntactic features. We use a sense-tagged clinical dataset from the Mayo Clinic for evaluation. Results The topic-modeling methods achieve 66.9% accuracy on a subset of the Mayo Clinic's data, while the graph-based methods only reach the 40–50% range, with a most-frequent-sense baseline of 56.5%. Features derived from the UMLS semantic type and concept hierarchies do not produce a gain over bag-of-words features in the topic models, but identifying phrases from UMLS and using syntax does help. Discussion Although topic models outperform graph-based methods, semantic features derived from the UMLS prove too noisy to improve performance beyond bag-of-words. Conclusions Topic modeling for WSD provides superior results in the clinical domain; however, integration of knowledge remains to be effectively exploited. PMID:24441986

A flower image retrieval method based on ROI feature.

PubMed

Hong, An-Xiang; Chen, Gang; Li, Jun-Li; Chi, Zhe-Ru; Zhang, Dan

2004-07-01

Flower image retrieval is a very important step for computer-aided plant species recognition. In this paper, we propose an efficient segmentation method based on color clustering and domain knowledge to extract flower regions from flower images. For flower retrieval, we use the color histogram of a flower region to characterize the color features of flower and two shape-based features sets, Centroid-Contour Distance (CCD) and Angle Code Histogram (ACH), to characterize the shape features of a flower contour. Experimental results showed that our flower region extraction method based on color clustering and domain knowledge can produce accurate flower regions. Flower retrieval results on a database of 885 flower images collected from 14 plant species showed that our Region-of-Interest (ROI) based retrieval approach using both color and shape features can perform better than a method based on the global color histogram proposed by Swain and Ballard (1991) and a method based on domain knowledge-driven segmentation and color names proposed by Das et al.(1999).

Chapter 1: Biomedical knowledge integration.

PubMed

Payne, Philip R O

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.

Chapter 1: Biomedical Knowledge Integration

PubMed Central

Payne, Philip R. O.

2012-01-01

The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems. PMID:23300416

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression

PubMed Central

Jiang, Feng; Han, Ji-zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods. PMID:29623088

A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression.

PubMed

Yu, Xu; Lin, Jun-Yu; Jiang, Feng; Du, Jun-Wei; Han, Ji-Zhong

2018-01-01

Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods.

Why Johnny can't reengineer health care processes with information technology.

PubMed

Webster, C; McLinden, S; Begler, K

1995-01-01

Many educational institutions are developing curricula that integrate computer and business knowledge and skills concerning a specific industry, such as banking or health care. We have developed a curriculum that emphasizes, equally, medical, computer, and business management concepts. Along the way we confronted a formidable obstacle, namely the domain specificity of the reference disciplines. Knowledge within each domain is sufficiently different from other domains that it reduces the leverage of building on preexisting knowledge and skills. We review this problem from the point of view of cognitive science (in particular, knowledge representation and machine learning) to suggest strategies for coping with incommensurate domain ontologies. These strategies include reflective judgment, implicit learning, abstraction, generalization, analogy, multiple inheritance, project-orientation, selectivity, goal- and failure-driven learning, and case- and story-based learning.

Towards a standardised representation of a knowledge base for adverse drug event prevention.

PubMed

Koutkias, Vassilis; Lazou, Katerina; de Clercq, Paul; Maglaveras, Nicos

2011-01-01

Knowledge representation is an important part of knowledge engineering activities that is crucial for enabling knowledge sharing and reuse. In this regard, standardised formalisms and technologies play a significant role. Especially for the medical domain, where knowledge may be tacit, not articulated and highly diverse, the development and adoption of standardised knowledge representations is highly challenging and of outmost importance to achieve knowledge interoperability. To this end, this paper presents a research effort towards the standardised representation of a Knowledge Base (KB) encapsulating rule-based signals and procedures for Adverse Drug Event (ADE) prevention. The KB constitutes an integral part of Clinical Decision Support Systems (CDSSs) to be used at the point of care. The paper highlights the requirements at the domain of discourse with respect to knowledge representation, according to which GELLO (an HL7 and ANSI standard) has been adopted. Results of our prototype implementation are presented along with the advantages and the limitations introduced by the employed approach.

Comprehension of University Texts: Effects of Domain-Knowledge and Summary

ERIC Educational Resources Information Center

Pascual, Gema; Goikoetxea, Edurne

2014-01-01

Our aim is to evaluate reading comprehension strategies based on empirical evidence and applicable to undergraduate students. Our hypotheses were that domain-knowledge or summary would have more influence on local, global, and inferential questions than rereading-question-answering instruction. Results of Experiment 1 were mixed in terms of…

An Ontology Based Approach to Information Security

NASA Astrophysics Data System (ADS)

Pereira, Teresa; Santos, Henrique

The semantically structure of knowledge, based on ontology approaches have been increasingly adopted by several expertise from diverse domains. Recently ontologies have been moved from the philosophical and metaphysics disciplines to be used in the construction of models to describe a specific theory of a domain. The development and the use of ontologies promote the creation of a unique standard to represent concepts within a specific knowledge domain. In the scope of information security systems the use of an ontology to formalize and represent the concepts of security information challenge the mechanisms and techniques currently used. This paper intends to present a conceptual implementation model of an ontology defined in the security domain. The model presented contains the semantic concepts based on the information security standard ISO/IEC_JTC1, and their relationships to other concepts, defined in a subset of the information security domain.

Developing an ontological explosion knowledge base for business continuity planning purposes.

PubMed

Mohammadfam, Iraj; Kalatpour, Omid; Golmohammadi, Rostam; Khotanlou, Hasan

2013-01-01

Industrial accidents are among the most known challenges to business continuity. Many organisations have lost their reputation following devastating accidents. To manage the risks of such accidents, it is necessary to accumulate sufficient knowledge regarding their roots, causes and preventive techniques. The required knowledge might be obtained through various approaches, including databases. Unfortunately, many databases are hampered by (among other things) static data presentations, a lack of semantic features, and the inability to present accident knowledge as discrete domains. This paper proposes the use of Protégé software to develop a knowledge base for the domain of explosion accidents. Such a structure has a higher capability to improve information retrieval compared with common accident databases. To accomplish this goal, a knowledge management process model was followed. The ontological explosion knowledge base (EKB) was built for further applications, including process accident knowledge retrieval and risk management. The paper will show how the EKB has a semantic feature that enables users to overcome some of the search constraints of existing accident databases.

PDA: A coupling of knowledge and memory for case-based reasoning

NASA Technical Reports Server (NTRS)

Bharwani, S.; Walls, J.; Blevins, E.

1988-01-01

Problem solving in most domains requires reference to past knowledge and experience whether such knowledge is represented as rules, decision trees, networks or any variant of attributed graphs. Regardless of the representational form employed, designers of expert systems rarely make a distinction between the static and dynamic aspects of the system's knowledge base. The current paper clearly distinguishes between knowledge-based and memory-based reasoning where the former in its most pure sense is characterized by a static knowledge based resulting in a relatively brittle expert system while the latter is dynamic and analogous to the functions of human memory which learns from experience. The paper discusses the design of an advisory system which combines a knowledge base consisting of domain vocabulary and default dependencies between concepts with a dynamic conceptual memory which stores experimental knowledge in the form of cases. The case memory organizes past experience in the form of MOPs (memory organization packets) and sub-MOPs. Each MOP consists of a context frame and a set of indices. The context frame contains information about the features (norms) common to all the events and sub-MOPs indexed under it.

Embedding Open-domain Common-sense Knowledge from Text

PubMed Central

Goodwin, Travis; Harabagiu, Sanda

2017-01-01

Our ability to understand language often relies on common-sense knowledge – background information the speaker can assume is known by the reader. Similarly, our comprehension of the language used in complex domains relies on access to domain-specific knowledge. Capturing common-sense and domain-specific knowledge can be achieved by taking advantage of recent advances in open information extraction (IE) techniques and, more importantly, of knowledge embeddings, which are multi-dimensional representations of concepts and relations. Building a knowledge graph for representing common-sense knowledge in which concepts discerned from noun phrases are cast as vertices and lexicalized relations are cast as edges leads to learning the embeddings of common-sense knowledge accounting for semantic compositionality as well as implied knowledge. Common-sense knowledge is acquired from a vast collection of blogs and books as well as from WordNet. Similarly, medical knowledge is learned from two large sets of electronic health records. The evaluation results of these two forms of knowledge are promising: the same knowledge acquisition methodology based on learning knowledge embeddings works well both for common-sense knowledge and for medical knowledge Interestingly, the common-sense knowledge that we have acquired was evaluated as being less neutral than than the medical knowledge, as it often reflected the opinion of the knowledge utterer. In addition, the acquired medical knowledge was evaluated as more plausible than the common-sense knowledge, reflecting the complexity of acquiring common-sense knowledge due to the pragmatics and economicity of language. PMID:28649676

A prototype case-based reasoning human assistant for space crew assessment and mission management

NASA Technical Reports Server (NTRS)

Owen, Robert B.; Holland, Albert W.; Wood, Joanna

1993-01-01

We present a prototype human assistant system for space crew assessment and mission management. Our system is based on case episodes from American and Russian space missions and analog environments such as polar stations and undersea habitats. The general domain of small groups in isolated and confined environments represents a near ideal application area for case-based reasoning (CBR) - there are few reliable rules to follow, and most domain knowledge is in the form of cases. We define the problem domain and outline a unique knowledge representation system driven by conflict and communication triggers. The prototype system is able to represent, index, and retrieve case studies of human performance. We index by social, behavioral, and environmental factors. We present the problem domain, our current implementation, our research approach for an operational system, and prototype performance and results.

Knowledge bases built on web languages from the point of view of predicate logics

NASA Astrophysics Data System (ADS)

Vajgl, Marek; Lukasová, Alena; Žáček, Martin

2017-06-01

The article undergoes evaluation of formal systems created on the base of web (ontology/concept) languages by simplifying the usual approach of knowledge representation within the FOPL, but sharing its expressiveness, semantic correct-ness, completeness and decidability. Evaluation of two of them - that one based on description logic and that one built on RDF model principles - identifies some of the lacks of those formal systems and presents, if possible, corrections of them. Possibilities to build an inference system capable to obtain new further knowledge over given knowledge bases including those describing domains by giant linked domain databases has been taken into account. Moreover, the directions towards simplifying FOPL language discussed here has been evaluated from the point of view of a possibility to become a web language for fulfilling an idea of semantic web.

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

The study of co-citation analysis and knowledge structure on healthcare domain

NASA Astrophysics Data System (ADS)

Chu, Kuo-Chung; Liu, Wen-I.; Tsai, Ming-Yu

2012-11-01

With the prevalence of Internet and digital archives, the online e-journal database facilitates scholars to search literature in a research domain, or to cross-search an inter-disciplined field; the key literature can be efficiently traced out. This study intends to build a Web-based citation analysis system, which consists of four modules, they are: 1) literature search module; (2) statistics module; (3) articles analysis module; and (4) co-citation analysis module. The system focuses on PubMed Central dataset that has 170,000 records. In a research domain, a specific keyword searches in terms of authors, journals, and core issues. In addition, we use data mining techniques for co-citation analysis. The results assist researchers with in-depth understanding of the domain knowledge. Having an automated system for co-citation analysis, it helps to understand changes, trends, and knowledge structure of research domain. For the best of our knowledge, the proposed system differentiates from existing online electronic retrieval database analysis function. Perhaps, the proposed system is going to be a value-added database of healthcare domain, and hope to contribute the researchers.

Delivering spacecraft control centers with embedded knowledge-based systems: The methodology issue

NASA Technical Reports Server (NTRS)

Ayache, S.; Haziza, M.; Cayrac, D.

1994-01-01

Matra Marconi Space (MMS) occupies a leading place in Europe in the domain of satellite and space data processing systems. The maturity of the knowledge-based systems (KBS) technology, the theoretical and practical experience acquired in the development of prototype, pre-operational and operational applications, make it possible today to consider the wide operational deployment of KBS's in space applications. In this perspective, MMS has to prepare the introduction of the new methods and support tools that will form the basis of the development of such systems. This paper introduces elements of the MMS methodology initiatives in the domain and the main rationale that motivated the approach. These initiatives develop along two main axes: knowledge engineering methods and tools, and a hybrid method approach for coexisting knowledge-based and conventional developments.

Beyond rules: The next generation of expert systems

NASA Technical Reports Server (NTRS)

Ferguson, Jay C.; Wagner, Robert E.

1987-01-01

The PARAGON Representation, Management, and Manipulation system is introduced. The concepts of knowledge representation, knowledge management, and knowledge manipulation are combined in a comprehensive system for solving real world problems requiring high levels of expertise in a real time environment. In most applications the complexity of the problem and the representation used to describe the domain knowledge tend to obscure the information from which solutions are derived. This inhibits the acquisition of domain knowledge verification/validation, places severe constraints on the ability to extend and maintain a knowledge base while making generic problem solving strategies difficult to develop. A unique hybrid system was developed to overcome these traditional limitations.

A Method for Populating the Knowledge Base of APTAS, a Domain-Oriented Application Composition System

DTIC Science & Technology

1993-12-01

proposed a domain analysis approach called Feature-Oriented Domain Analysis ( FODA ). The approach identifies prominent features (similarities) and...characteristics of software systems in the domain. Unlike the other domain analysis approaches we have summarized, the re- searchers described FODA in...Domain Analysis ( FODA ) Feasibility Study. Technical Report, Software Engineering Institute, Carnegie Mellon University, Novem- ber 1990. 19. Lee, Kenneth

From spoken narratives to domain knowledge: mining linguistic data for medical image understanding.

PubMed

Guo, Xuan; Yu, Qi; Alm, Cecilia Ovesdotter; Calvelli, Cara; Pelz, Jeff B; Shi, Pengcheng; Haake, Anne R

2014-10-01

Extracting useful visual clues from medical images allowing accurate diagnoses requires physicians' domain knowledge acquired through years of systematic study and clinical training. This is especially true in the dermatology domain, a medical specialty that requires physicians to have image inspection experience. Automating or at least aiding such efforts requires understanding physicians' reasoning processes and their use of domain knowledge. Mining physicians' references to medical concepts in narratives during image-based diagnosis of a disease is an interesting research topic that can help reveal experts' reasoning processes. It can also be a useful resource to assist with design of information technologies for image use and for image case-based medical education systems. We collected data for analyzing physicians' diagnostic reasoning processes by conducting an experiment that recorded their spoken descriptions during inspection of dermatology images. In this paper we focus on the benefit of physicians' spoken descriptions and provide a general workflow for mining medical domain knowledge based on linguistic data from these narratives. The challenge of a medical image case can influence the accuracy of the diagnosis as well as how physicians pursue the diagnostic process. Accordingly, we define two lexical metrics for physicians' narratives--lexical consensus score and top N relatedness score--and evaluate their usefulness by assessing the diagnostic challenge levels of corresponding medical images. We also report on clustering medical images based on anchor concepts obtained from physicians' medical term usage. These analyses are based on physicians' spoken narratives that have been preprocessed by incorporating the Unified Medical Language System for detecting medical concepts. The image rankings based on lexical consensus score and on top 1 relatedness score are well correlated with those based on challenge levels (Spearman correlation>0.5 and Kendall correlation>0.4). Clustering results are largely improved based on our anchor concept method (accuracy>70% and mutual information>80%). Physicians' spoken narratives are valuable for the purpose of mining the domain knowledge that physicians use in medical image inspections. We also show that the semantic metrics introduced in the paper can be successfully applied to medical image understanding and allow discussion of additional uses of these metrics. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

A knowledge-based approach to automated flow-field zoning for computational fluid dynamics

NASA Technical Reports Server (NTRS)

Vogel, Alison Andrews

1989-01-01

An automated three-dimensional zonal grid generation capability for computational fluid dynamics is shown through the development of a demonstration computer program capable of automatically zoning the flow field of representative two-dimensional (2-D) aerodynamic configurations. The applicability of a knowledge-based programming approach to the domain of flow-field zoning is examined. Several aspects of flow-field zoning make the application of knowledge-based techniques challenging: the need for perceptual information, the role of individual bias in the design and evaluation of zonings, and the fact that the zoning process is modeled as a constructive, design-type task (for which there are relatively few examples of successful knowledge-based systems in any domain). Engineering solutions to the problems arising from these aspects are developed, and a demonstration system is implemented which can design, generate, and output flow-field zonings for representative 2-D aerodynamic configurations.

An object-relational model for structured representation of medical knowledge.

PubMed

Koch, S; Risch, T; Schneider, W; Wagner, I V

2006-07-01

Domain specific knowledge is often not static but continuously evolving. This is especially true for the medical domain. Furthermore, the lack of standardized structures for presenting knowledge makes it difficult or often impossible to assess new knowledge in the context of existing knowledge. Possibilities to compare knowledge easily and directly are often not given. It is therefore of utmost importance to create a model that allows for comparability, consistency and quality assurance of medical knowledge in specific work situations. For this purpose, we have designed on object-relational model based on structured knowledge elements that are dynamically reusable by different multi-media-based tools for case-based documentation, disease course simulation, and decision support. With this model, high-level components, such as patient case reports or simulations of the course of a disease, and low-level components (e.g., diagnoses, symptoms or treatments) as well as the relationships between these components are modeled. The resulting schema has been implemented in AMOS II, on object-relational multi-database system supporting different views with regard to search and analysis depending on different work situations.

Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications

PubMed Central

Chang, Hang; Han, Ju; Zhong, Cheng; Snijders, Antoine M.; Mao, Jian-Hua

2017-01-01

The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains. PMID:28129148

A Logical Framework for Service Migration Based Survivability

DTIC Science & Technology

2016-06-24

platforms; Service Migration Strategy Fuzzy Inference System Knowledge Base Fuzzy rules representing domain expert knowledge about implications of...service migration strategy. Our approach uses expert knowledge as linguistic reasoning rules and takes service programs damage assessment, service...programs complexity, and available network capability as input. The fuzzy inference system includes four components as shown in Figure 5: (1) a knowledge

Applying data mining techniques to medical time series: an empirical case study in electroencephalography and stabilometry.

PubMed

Anguera, A; Barreiro, J M; Lara, J A; Lizcano, D

2016-01-01

One of the major challenges in the medical domain today is how to exploit the huge amount of data that this field generates. To do this, approaches are required that are capable of discovering knowledge that is useful for decision making in the medical field. Time series are data types that are common in the medical domain and require specialized analysis techniques and tools, especially if the information of interest to specialists is concentrated within particular time series regions, known as events. This research followed the steps specified by the so-called knowledge discovery in databases (KDD) process to discover knowledge from medical time series derived from stabilometric (396 series) and electroencephalographic (200) patient electronic health records (EHR). The view offered in the paper is based on the experience gathered as part of the VIIP project. Knowledge discovery in medical time series has a number of difficulties and implications that are highlighted by illustrating the application of several techniques that cover the entire KDD process through two case studies. This paper illustrates the application of different knowledge discovery techniques for the purposes of classification within the above domains. The accuracy of this application for the two classes considered in each case is 99.86% and 98.11% for epilepsy diagnosis in the electroencephalography (EEG) domain and 99.4% and 99.1% for early-age sports talent classification in the stabilometry domain. The KDD techniques achieve better results than other traditional neural network-based classification techniques.

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.

Semantics-driven modelling of user preferences for information retrieval in the biomedical domain.

PubMed

Gladun, Anatoly; Rogushina, Julia; Valencia-García, Rafael; Béjar, Rodrigo Martínez

2013-03-01

A large amount of biomedical and genomic data are currently available on the Internet. However, data are distributed into heterogeneous biological information sources, with little or even no organization. Semantic technologies provide a consistent and reliable basis with which to confront the challenges involved in the organization, manipulation and visualization of data and knowledge. One of the knowledge representation techniques used in semantic processing is the ontology, which is commonly defined as a formal and explicit specification of a shared conceptualization of a domain of interest. The work presented here introduces a set of interoperable algorithms that can use domain and ontological information to improve information-retrieval processes. This work presents an ontology-based information-retrieval system for the biomedical domain. This system, with which some experiments have been carried out that are described in this paper, is based on the use of domain ontologies for the creation and normalization of lightweight ontologies that represent user preferences in a determined domain in order to improve information-retrieval processes.

Adding Learning to Knowledge-Based Systems: Taking the "Artificial" Out of AI

Treesearch

Daniel L. Schmoldt

1997-01-01

Both, knowledge-based systems (KBS) development and maintenance require time-consuming analysis of domain knowledge. Where example cases exist, KBS can be built, and later updated, by incorporating learning capabilities into their architecture. This applies to both supervised and unsupervised learning scenarios. In this paper, the important issues for learning systems-...

Building the Knowledge Base to Support the Automatic Animation Generation of Chinese Traditional Architecture

NASA Astrophysics Data System (ADS)

Wei, Gongjin; Bai, Weijing; Yin, Meifang; Zhang, Songmao

We present a practice of applying the Semantic Web technologies in the domain of Chinese traditional architecture. A knowledge base consisting of one ontology and four rule bases is built to support the automatic generation of animations that demonstrate the construction of various Chinese timber structures based on the user's input. Different Semantic Web formalisms are used, e.g., OWL DL, SWRL and Jess, to capture the domain knowledge, including the wooden components needed for a given building, construction sequence, and the 3D size and position of every piece of wood. Our experience in exploiting the current Semantic Web technologies in real-world application systems indicates their prominent advantages (such as the reasoning facilities and modeling tools) as well as the limitations (such as low efficiency).

A reusable knowledge acquisition shell: KASH

NASA Technical Reports Server (NTRS)

Westphal, Christopher; Williams, Stephen; Keech, Virginia

1991-01-01

KASH (Knowledge Acquisition SHell) is proposed to assist a knowledge engineer by providing a set of utilities for constructing knowledge acquisition sessions based on interviewing techniques. The information elicited from domain experts during the sessions is guided by a question dependency graph (QDG). The QDG defined by the knowledge engineer, consists of a series of control questions about the domain that are used to organize the knowledge of an expert. The content information supplies by the expert, in response to the questions, is represented in the form of a concept map. These maps can be constructed in a top-down or bottom-up manner by the QDG and used by KASH to generate the rules for a large class of expert system domains. Additionally, the concept maps can support the representation of temporal knowledge. The high degree of reusability encountered in the QDG and concept maps can vastly reduce the development times and costs associated with producing intelligent decision aids, training programs, and process control functions.

Does prior domain-specific content knowledge influence students' recall of arguments surrounding interdisciplinary topics?

PubMed

Schmidt, Hiemke K; Rothgangel, Martin; Grube, Dietmar

2017-12-01

Awareness of various arguments can help interactants present opinions, stress points, and build counterarguments during discussions. At school, some topics are taught in a way that students learn to accumulate knowledge and gather arguments, and later employ them during debates. Prior knowledge may facilitate recalling information on well structured, fact-based topics, but does it facilitate recalling arguments during discussions on complex, interdisciplinary topics? We assessed the prior knowledge in domains related to a bioethical topic of 277 students from Germany (approximately 15 years old), their interest in the topic, and their general knowledge. The students read a text with arguments for and against prenatal diagnostics and tried to recall the arguments one week later and again six weeks later. Prior knowledge in various domains related to the topic individually and separately helped students recall the arguments. These relationships were independent of students' interest in the topic and their general knowledge. Copyright © 2017 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

The intellectual structure and substance of the knowledge utilization field: a longitudinal author co-citation analysis, 1945 to 2004.

PubMed

Estabrooks, Carole A; Derksen, Linda; Winther, Connie; Lavis, John N; Scott, Shannon D; Wallin, Lars; Profetto-McGrath, Joanne

2008-11-13

It has been argued that science and society are in the midst of a far-reaching renegotiation of the social contract between science and society, with society becoming a far more active partner in the creation of knowledge. On the one hand, new forms of knowledge production are emerging, and on the other, both science and society are experiencing a rapid acceleration in new forms of knowledge utilization. Concomitantly since the Second World War, the science underpinning the knowledge utilization field has had exponential growth. Few in-depth examinations of this field exist, and no comprehensive analyses have used bibliometric methods. Using bibliometric analysis, specifically first author co-citation analysis, our group undertook a domain analysis of the knowledge utilization field, tracing its historical development between 1945 and 2004. Our purposes were to map the historical development of knowledge utilization as a field, and to identify the changing intellectual structure of its scientific domains. We analyzed more than 5,000 articles using citation data drawn from the Web of Science. Search terms were combinations of knowledge, research, evidence, guidelines, ideas, science, innovation, technology, information theory and use, utilization, and uptake. We provide an overview of the intellectual structure and how it changed over six decades. The field does not become large enough to represent with a co-citation map until the mid-1960s. Our findings demonstrate vigorous growth from the mid-1960s through 2004, as well as the emergence of specialized domains reflecting distinct collectives of intellectual activity and thought. Until the mid-1980s, the major domains were focused on innovation diffusion, technology transfer, and knowledge utilization. Beginning slowly in the mid-1980s and then growing rapidly, a fourth scientific domain, evidence-based medicine, emerged. The field is dominated in all decades by one individual, Everett Rogers, and by one paradigm, innovation diffusion. We conclude that the received view that social science disciplines are in a state where no accepted set of principles or theories guide research (i.e., that they are pre-paradigmatic) could not be supported for this field. Second, we document the emergence of a new domain within the knowledge utilization field, evidence-based medicine. Third, we conclude that Everett Rogers was the dominant figure in the field and, until the emergence of evidence-based medicine, his representation of the general diffusion model was the dominant paradigm in the field.

The intellectual structure and substance of the knowledge utilization field: A longitudinal author co-citation analysis, 1945 to 2004

PubMed Central

Estabrooks, Carole A; Derksen, Linda; Winther, Connie; Lavis, John N; Scott, Shannon D; Wallin, Lars; Profetto-McGrath, Joanne

2008-01-01

Background It has been argued that science and society are in the midst of a far-reaching renegotiation of the social contract between science and society, with society becoming a far more active partner in the creation of knowledge. On the one hand, new forms of knowledge production are emerging, and on the other, both science and society are experiencing a rapid acceleration in new forms of knowledge utilization. Concomitantly since the Second World War, the science underpinning the knowledge utilization field has had exponential growth. Few in-depth examinations of this field exist, and no comprehensive analyses have used bibliometric methods. Methods Using bibliometric analysis, specifically first author co-citation analysis, our group undertook a domain analysis of the knowledge utilization field, tracing its historical development between 1945 and 2004. Our purposes were to map the historical development of knowledge utilization as a field, and to identify the changing intellectual structure of its scientific domains. We analyzed more than 5,000 articles using citation data drawn from the Web of Science®. Search terms were combinations of knowledge, research, evidence, guidelines, ideas, science, innovation, technology, information theory and use, utilization, and uptake. Results We provide an overview of the intellectual structure and how it changed over six decades. The field does not become large enough to represent with a co-citation map until the mid-1960s. Our findings demonstrate vigorous growth from the mid-1960s through 2004, as well as the emergence of specialized domains reflecting distinct collectives of intellectual activity and thought. Until the mid-1980s, the major domains were focused on innovation diffusion, technology transfer, and knowledge utilization. Beginning slowly in the mid-1980s and then growing rapidly, a fourth scientific domain, evidence-based medicine, emerged. The field is dominated in all decades by one individual, Everett Rogers, and by one paradigm, innovation diffusion. Conclusion We conclude that the received view that social science disciplines are in a state where no accepted set of principles or theories guide research (i.e., that they are pre-paradigmatic) could not be supported for this field. Second, we document the emergence of a new domain within the knowledge utilization field, evidence-based medicine. Third, we conclude that Everett Rogers was the dominant figure in the field and, until the emergence of evidence-based medicine, his representation of the general diffusion model was the dominant paradigm in the field. PMID:19014512

Specificity of Structural Assessment of Knowledge

ERIC Educational Resources Information Center

Trumpower, David L.; Sharara, Harold; Goldsmith, Timothy E.

2010-01-01

This study examines the specificity of information provided by structural assessment of knowledge (SAK). SAK is a technique which uses the Pathfinder scaling algorithm to transform ratings of concept relatedness into network representations (PFnets) of individuals' knowledge. Inferences about individuals' overall domain knowledge based on the…

A knowledge engineering taxonomy for intelligent tutoring system development

NASA Technical Reports Server (NTRS)

Fink, Pamela K.; Herren, L. Tandy

1993-01-01

This paper describes a study addressing the issue of developing an appropriate mapping of knowledge acquisition methods to problem types for intelligent tutoring system development. Recent research has recognized that knowledge acquisition methodologies are not general across problem domains; the effectiveness of a method for obtaining knowledge depends on the characteristics of the domain and problem solving task. Southwest Research Institute developed a taxonomy of problem types by evaluating the characteristics that discriminate between problems and grouping problems that share critical characteristics. Along with the problem taxonomy, heuristics that guide the knowledge acquisition process based on the characteristics of the class are provided.

A web-based system architecture for ontology-based data integration in the domain of IT benchmarking

NASA Astrophysics Data System (ADS)

Pfaff, Matthias; Krcmar, Helmut

2018-03-01

In the domain of IT benchmarking (ITBM), a variety of data and information are collected. Although these data serve as the basis for business analyses, no unified semantic representation of such data yet exists. Consequently, data analysis across different distributed data sets and different benchmarks is almost impossible. This paper presents a system architecture and prototypical implementation for an integrated data management of distributed databases based on a domain-specific ontology. To preserve the semantic meaning of the data, the ITBM ontology is linked to data sources and functions as the central concept for database access. Thus, additional databases can be integrated by linking them to this domain-specific ontology and are directly available for further business analyses. Moreover, the web-based system supports the process of mapping ontology concepts to external databases by introducing a semi-automatic mapping recommender and by visualizing possible mapping candidates. The system also provides a natural language interface to easily query linked databases. The expected result of this ontology-based approach of knowledge representation and data access is an increase in knowledge and data sharing in this domain, which will enhance existing business analysis methods.

COM3/369: Knowledge-based Information Systems: A new approach for the representation and retrieval of medical information

PubMed Central

Mann, G; Birkmann, C; Schmidt, T; Schaeffler, V

1999-01-01

Introduction Present solutions for the representation and retrieval of medical information from online sources are not very satisfying. Either the retrieval process lacks of precision and completeness the representation does not support the update and maintenance of the represented information. Most efforts are currently put into improving the combination of search engines and HTML based documents. However, due to the current shortcomings of methods for natural language understanding there are clear limitations to this approach. Furthermore, this approach does not solve the maintenance problem. At least medical information exceeding a certain complexity seems to afford approaches that rely on structured knowledge representation and corresponding retrieval mechanisms. Methods Knowledge-based information systems are based on the following fundamental ideas. The representation of information is based on ontologies that define the structure of the domain's concepts and their relations. Views on domain models are defined and represented as retrieval schemata. Retrieval schemata can be interpreted as canonical query types focussing on specific aspects of the provided information (e.g. diagnosis or therapy centred views). Based on these retrieval schemata it can be decided which parts of the information in the domain model must be represented explicitly and formalised to support the retrieval process. As representation language propositional logic is used. All other information can be represented in a structured but informal way using text, images etc. Layout schemata are used to assign layout information to retrieved domain concepts. Depending on the target environment HTML or XML can be used. Results Based on this approach two knowledge-based information systems have been developed. The 'Ophthalmologic Knowledge-based Information System for Diabetic Retinopathy' (OKIS-DR) provides information on diagnoses, findings, examinations, guidelines, and reference images related to diabetic retinopathy. OKIS-DR uses combinations of findings to specify the information that must be retrieved. The second system focuses on nutrition related allergies and intolerances. Information on allergies and intolerances of a patient are used to retrieve general information on the specified combination of allergies and intolerances. As a special feature the system generates tables showing food types and products that are tolerated or not tolerated by patients. Evaluation by external experts and user groups showed that the described approach of knowledge-based information systems increases the precision and completeness of knowledge retrieval. Due to the structured and non-redundant representation of information the maintenance and update of the information can be simplified. Both systems are available as WWW based online knowledge bases and CD-ROMs (cf. http://mta.gsf.de topic: products).

Disentangling the Role of Domain-Specific Knowledge in Student Modeling

NASA Astrophysics Data System (ADS)

Ruppert, John; Duncan, Ravit Golan; Chinn, Clark A.

2017-08-01

This study explores the role of domain-specific knowledge in students' modeling practice and how this knowledge interacts with two domain-general modeling strategies: use of evidence and developing a causal mechanism. We analyzed models made by middle school students who had a year of intensive model-based instruction. These models were made to explain a familiar but unstudied biological phenomenon: late onset muscle pain. Students were provided with three pieces of evidence related to this phenomenon and asked to construct a model to account for this evidence. Findings indicate that domain-specific resources play a significant role in the extent to which the models accounted for provided evidence. On the other hand, familiarity with the situation appeared to contribute to the mechanistic character of models. Our results indicate that modeling strategies alone are insufficient for the development of a mechanistic model that accounts for provided evidence and that, while learners can develop a tentative model with a basic familiarity of the situation, scaffolding certain domain-specific knowledge is necessary to assist students with incorporating evidence in modeling tasks.

OWL2 benchmarking for the evaluation of knowledge based systems.

PubMed

Khan, Sher Afgun; Qadir, Muhammad Abdul; Abbas, Muhammad Azeem; Afzal, Muhammad Tanvir

2017-01-01

OWL2 semantics are becoming increasingly popular for the real domain applications like Gene engineering and health MIS. The present work identifies the research gap that negligible attention has been paid to the performance evaluation of Knowledge Base Systems (KBS) using OWL2 semantics. To fulfil this identified research gap, an OWL2 benchmark for the evaluation of KBS is proposed. The proposed benchmark addresses the foundational blocks of an ontology benchmark i.e. data schema, workload and performance metrics. The proposed benchmark is tested on memory based, file based, relational database and graph based KBS for performance and scalability measures. The results show that the proposed benchmark is able to evaluate the behaviour of different state of the art KBS on OWL2 semantics. On the basis of the results, the end users (i.e. domain expert) would be able to select a suitable KBS appropriate for his domain.

Semantic technologies in a decision support system

NASA Astrophysics Data System (ADS)

Wasielewska, K.; Ganzha, M.; Paprzycki, M.; Bǎdicǎ, C.; Ivanovic, M.; Lirkov, I.

2015-10-01

The aim of our work is to design a decision support system based on ontological representation of domain(s) and semantic technologies. Specifically, we consider the case when Grid / Cloud user describes his/her requirements regarding a "resource" as a class expression from an ontology, while the instances of (the same) ontology represent available resources. The goal is to help the user to find the best option with respect to his/her requirements, while remembering that user's knowledge may be "limited." In this context, we discuss multiple approaches based on semantic data processing, which involve different "forms" of user interaction with the system. Specifically, we consider: (a) ontological matchmaking based on SPARQL queries and class expression, (b) graph-based semantic closeness of instances representing user requirements (constructed from the class expression) and available resources, and (c) multicriterial analysis based on the AHP method, which utilizes expert domain knowledge (also ontologically represented).

The role of professional knowledge in case-based reasoning in practical ethics.

PubMed

Pinkus, Rosa Lynn; Gloeckner, Claire; Fortunato, Angela

2015-06-01

The use of case-based reasoning in teaching professional ethics has come of age. The fields of medicine, engineering, and business all have incorporated ethics case studies into leading textbooks and journal articles, as well as undergraduate and graduate professional ethics courses. The most recent guidelines from the National Institutes of Health recognize case studies and face-to-face discussion as best practices to be included in training programs for the Responsible Conduct of Research. While there is a general consensus that case studies play a central role in the teaching of professional ethics, there is still much to be learned regarding how professionals learn ethics using case-based reasoning. Cases take many forms, and there are a variety of ways to write them and use them in teaching. This paper reports the results of a study designed to investigate one of the issues in teaching case-based ethics: the role of one's professional knowledge in learning methods of moral reasoning. Using a novel assessment instrument, we compared case studies written and analyzed by three groups of students whom we classified as: (1) Experts in a research domain in bioengineering. (2) Novices in a research domain in bioengineering. (3) The non-research group--students using an engineering domain in which they were interested but had no in-depth knowledge. This study demonstrates that a student's level of understanding of a professional knowledge domain plays a significant role in learning moral reasoning skills.

Semi-automated knowledge discovery: identifying and profiling human trafficking

NASA Astrophysics Data System (ADS)

Poelmans, Jonas; Elzinga, Paul; Ignatov, Dmitry I.; Kuznetsov, Sergei O.

2012-11-01

We propose an iterative and human-centred knowledge discovery methodology based on formal concept analysis. The proposed approach recognizes the important role of the domain expert in mining real-world enterprise applications and makes use of specific domain knowledge, including human intelligence and domain-specific constraints. Our approach was empirically validated at the Amsterdam-Amstelland police to identify suspects and victims of human trafficking in 266,157 suspicious activity reports. Based on guidelines of the Attorney Generals of the Netherlands, we first defined multiple early warning indicators that were used to index the police reports. Using concept lattices, we revealed numerous unknown human trafficking and loverboy suspects. In-depth investigation by the police resulted in a confirmation of their involvement in illegal activities resulting in actual arrestments been made. Our human-centred approach was embedded into operational policing practice and is now successfully used on a daily basis to cope with the vastly growing amount of unstructured information.

Towards a knowledge-based system to assist the Brazilian data-collecting system operation

NASA Technical Reports Server (NTRS)

Rodrigues, Valter; Simoni, P. O.; Oliveira, P. P. B.; Oliveira, C. A.; Nogueira, C. A. M.

1988-01-01

A study is reported which was carried out to show how a knowledge-based approach would lead to a flexible tool to assist the operation task in a satellite-based environmental data collection system. Some characteristics of a hypothesized system comprised of a satellite and a network of Interrogable Data Collecting Platforms (IDCPs) are pointed out. The Knowledge-Based Planning Assistant System (KBPAS) and some aspects about how knowledge is organized in the IDCP's domain are briefly described.

Measuring Teachers' Pedagogical Content Knowledge in Primary Technology Education

ERIC Educational Resources Information Center

Rohaan, Ellen J.; Taconis, Ruurd; Jochems, Wim M. G.

2009-01-01

Pedagogical content knowledge is found to be a crucial part of the knowledge base for teaching. Studies in the field of primary technology education showed that this domain of teacher knowledge is related to pupils' increased learning, motivation, and interest. The common methods to investigate teachers' pedagogical content knowledge are often…

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning.

PubMed

Chung, Younjin; Salvador-Carulla, Luis; Salinas-Pérez, José A; Uriarte-Uriarte, Jose J; Iruin-Sanz, Alvaro; García-Alonso, Carlos R

2018-04-25

Decision-making in mental health systems should be supported by the evidence-informed knowledge transfer of data. Since mental health systems are inherently complex, involving interactions between its structures, processes and outcomes, decision support systems (DSS) need to be developed using advanced computational methods and visual tools to allow full system analysis, whilst incorporating domain experts in the analysis process. In this study, we use a DSS model developed for interactive data mining and domain expert collaboration in the analysis of complex mental health systems to improve system knowledge and evidence-informed policy planning. We combine an interactive visual data mining approach, the self-organising map network (SOMNet), with an operational expert knowledge approach, expert-based collaborative analysis (EbCA), to develop a DSS model. The SOMNet was applied to the analysis of healthcare patterns and indicators of three different regional mental health systems in Spain, comprising 106 small catchment areas and providing healthcare for over 9 million inhabitants. Based on the EbCA, the domain experts in the development team guided and evaluated the analytical processes and results. Another group of 13 domain experts in mental health systems planning and research evaluated the model based on the analytical information of the SOMNet approach for processing information and discovering knowledge in a real-world context. Through the evaluation, the domain experts assessed the feasibility and technology readiness level (TRL) of the DSS model. The SOMNet, combined with the EbCA, effectively processed evidence-based information when analysing system outliers, explaining global and local patterns, and refining key performance indicators with their analytical interpretations. The evaluation results showed that the DSS model was feasible by the domain experts and reached level 7 of the TRL (system prototype demonstration in operational environment). This study supports the benefits of combining health systems engineering (SOMNet) and expert knowledge (EbCA) to analyse the complexity of health systems research. The use of the SOMNet approach contributes to the demonstration of DSS for mental health planning in practice.

An adaptive signal-processing approach to online adaptive tutoring.

PubMed

Bergeron, Bryan; Cline, Andrew

2011-01-01

Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

Major accident prevention through applying safety knowledge management approach.

PubMed

Kalatpour, Omid

2016-01-01

Many scattered resources of knowledge are available to use for chemical accident prevention purposes. The common approach to management process safety, including using databases and referring to the available knowledge has some drawbacks. The main goal of this article was to devise a new emerged knowledge base (KB) for the chemical accident prevention domain. The scattered sources of safety knowledge were identified and scanned. Then, the collected knowledge was formalized through a computerized program. The Protégé software was used to formalize and represent the stored safety knowledge. The domain knowledge retrieved as well as data and information. This optimized approach improved safety and health knowledge management (KM) process and resolved some typical problems in the KM process. Upgrading the traditional resources of safety databases into the KBs can improve the interaction between the users and knowledge repository.

Parental behavioral control in academic and non-academic domains: a three-year longitudinal study in the Chinese culture.

PubMed

Shek, Daniel T L; Lee, Tak Yan

2007-01-01

For over three consecutive years, 2559 Chinese adolescents (mean age = 12.65 years at Wave 1) responded to instruments assessing their perceived parental behavioral control based on measures of parental knowledge, expectation, monitoring, and discipline. The results show that compared with parental control in the academic domain, parental control in the non-academic domain (peer relations domain) was relatively weaker, using parental knowledge, parental expectation, parental monitoring, and parental discipline as indicators, and a decline in parental behavioral control occurred over time. Although domain (academic domain versus non-academic domain) X time (Time 1, Time 2 versus Time 3) interaction effects were found, the findings mirrored the main effects of domain and time. Parental education and economic sufficiency were linearly related to differences in parental behavioral control in the academic domain and non-academic domain. The present findings suggest that traditional Chinese cultural emphasis on academic excellence still prevails in the contemporary Chinese culture.

Domain-Specific Knowledge and Why Teaching Generic Skills Does Not Work

ERIC Educational Resources Information Center

Tricot, André; Sweller, John

2014-01-01

Domain-general cognitive knowledge has frequently been used to explain skill when domain-specific knowledge held in long-term memory may provide a better explanation. An emphasis on domain-general knowledge may be misplaced if domain-specific knowledge is the primary factor driving acquired intellectual skills. We trace the long history of…

Patient understanding of emergency department discharge instructions: where are knowledge deficits greatest?

PubMed

Engel, Kirsten G; Buckley, Barbara A; Forth, Victoria E; McCarthy, Danielle M; Ellison, Emily P; Schmidt, Michael J; Adams, James G

2012-09-01

Many patients are discharged from the emergency department (ED) with an incomplete understanding of the information needed to safely care for themselves at home. Patients have demonstrated particular difficulty in understanding post-ED care instructions (including medications, home care, and follow-up). The objective of this study was to further characterize these deficits and identify gaps in knowledge that may place the patient at risk for complications or poor outcomes. This was a prospective cohort, phone interview-based study of 159 adult English-speaking patients within 24 to 36 hours of ED discharge. Patient knowledge was assessed for five diagnoses (ankle sprain, back pain, head injury, kidney stone, and laceration) across the following five domains: diagnosis, medications, home care, follow-up, and return instructions. Knowledge was determined based on the concordance between direct patient recall and diagnosis-specific discharge instructions combined with chart review. Two authors scored each case independently and discussed discrepancies before providing a final score for each domain (no, minimal, partial, or complete comprehension). Descriptive statistics were used for the analyses. The study population was 50% female with a median age of 41 years (interquartile range [IQR] = 29 to 53 years). Knowledge deficits were demonstrated by the majority of patients in the domain of home care instructions (80%) and return instructions (79%). Less frequent deficits were found for the domains of follow-up (39%), medications (22%), and diagnosis (14%). Minimal or no understanding in at least one domain was demonstrated by greater than two-thirds of patients and was found in 40% of cases for home care and 51% for return instructions. These deficits occurred less frequently for domains of follow-up (18%), diagnosis (3%), and medications (3%). Patients demonstrate the most frequent knowledge deficits for home care and return instructions, raising significant concerns for adherence and outcomes. © 2012 by the Society for Academic Emergency Medicine.

Co-occurrence graphs for word sense disambiguation in the biomedical domain.

PubMed

Duque, Andres; Stevenson, Mark; Martinez-Romo, Juan; Araujo, Lourdes

2018-05-01

Word sense disambiguation is a key step for many natural language processing tasks (e.g. summarization, text classification, relation extraction) and presents a challenge to any system that aims to process documents from the biomedical domain. In this paper, we present a new graph-based unsupervised technique to address this problem. The knowledge base used in this work is a graph built with co-occurrence information from medical concepts found in scientific abstracts, and hence adapted to the specific domain. Unlike other unsupervised approaches based on static graphs such as UMLS, in this work the knowledge base takes the context of the ambiguous terms into account. Abstracts downloaded from PubMed are used for building the graph and disambiguation is performed using the personalized PageRank algorithm. Evaluation is carried out over two test datasets widely explored in the literature. Different parameters of the system are also evaluated to test robustness and scalability. Results show that the system is able to outperform state-of-the-art knowledge-based systems, obtaining more than 10% of accuracy improvement in some cases, while only requiring minimal external resources. Copyright © 2018 Elsevier B.V. All rights reserved.

Machine learning research 1989-90

NASA Technical Reports Server (NTRS)

Porter, Bruce W.; Souther, Arthur

1990-01-01

Multifunctional knowledge bases offer a significant advance in artificial intelligence because they can support numerous expert tasks within a domain. As a result they amortize the costs of building a knowledge base over multiple expert systems and they reduce the brittleness of each system. Due to the inevitable size and complexity of multifunctional knowledge bases, their construction and maintenance require knowledge engineering and acquisition tools that can automatically identify interactions between new and existing knowledge. Furthermore, their use requires software for accessing those portions of the knowledge base that coherently answer questions. Considerable progress was made in developing software for building and accessing multifunctional knowledge bases. A language was developed for representing knowledge, along with software tools for editing and displaying knowledge, a machine learning program for integrating new information into existing knowledge, and a question answering system for accessing the knowledge base.

Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study

PubMed Central

Geary, David C.; Nicholas, Alan; Li, Yaoran; Sun, Jianguo

2016-01-01

The contributions of domain-general abilities and domain-specific knowledge to subsequent mathematics achievement were longitudinally assessed (n = 167) through 8th grade. First grade intelligence and working memory and prior grade reading achievement indexed domain-general effects and domain-specific effects were indexed by prior grade mathematics achievement and mathematical cognition measures of prior grade number knowledge, addition skills, and fraction knowledge. Use of functional data analysis enabled grade-by-grade estimation of overall domain-general and domain-specific effects on subsequent mathematics achievement, the relative importance of individual domain-general and domain-specific variables on this achievement, and linear and non-linear across-grade estimates of these effects. The overall importance of domain-general abilities for subsequent achievement was stable across grades, with working memory emerging as the most important domain-general ability in later grades. The importance of prior mathematical competencies on subsequent mathematics achievement increased across grades, with number knowledge and arithmetic skills critical in all grades and fraction knowledge in later grades. Overall, domain-general abilities were more important than domain-specific knowledge for mathematics learning in early grades but general abilities and domain-specific knowledge were equally important in later grades. PMID:28781382

The HEP Software and Computing Knowledge Base

NASA Astrophysics Data System (ADS)

Wenaus, T.

2017-10-01

HEP software today is a rich and diverse domain in itself and exists within the mushrooming world of open source software. As HEP software developers and users we can be more productive and effective if our work and our choices are informed by a good knowledge of what others in our community have created or found useful. The HEP Software and Computing Knowledge Base, hepsoftware.org, was created to facilitate this by serving as a collection point and information exchange on software projects and products, services, training, computing facilities, and relating them to the projects, experiments, organizations and science domains that offer them or use them. It was created as a contribution to the HEP Software Foundation, for which a HEP S&C knowledge base was a much requested early deliverable. This contribution will motivate and describe the system, what it offers, its content and contributions both existing and needed, and its implementation (node.js based web service and javascript client app) which has emphasized ease of use for both users and contributors.

Vivir Con Un Corazón Saludable: a Community-Based Educational Program Aimed at Increasing Cardiovascular Health Knowledge in High-Risk Hispanic Women.

PubMed

Romero, Daniela C; Sauris, Aileen; Rodriguez, Fátima; Delgado, Daniela; Reddy, Ankita; Foody, JoAnne M

2016-03-01

Hispanic women suffer from high rates of cardiometabolic risk factors and an increasingly disproportionate burden of cardiovascular disease (CVD). Particularly, Hispanic women with limited English proficiency suffer from low levels of CVD knowledge associated with adverse CVD health outcomes. Thirty-two predominantly Spanish-speaking Hispanic women completed, Vivir Con un Corazón Saludable (VCUCS), a culturally tailored Spanish language-based 6-week intensive community program targeting CVD health knowledge through weekly interactive health sessions. A 30-question CVD knowledge questionnaire was used to assess mean changes in CVD knowledge at baseline and postintervention across five major knowledge domains including CVD epidemiology, dietary knowledge, medical information, risk factors, and heart attack symptoms. Completion of the program was associated with a statistically significant (p < 0.001) increase in total mean CVD knowledge scores from 39 % (mean 11.7/30.0) to 66 % (mean 19.8/30.0) postintervention consistent with a 68 % increase in overall mean CVD scores. There was a statistically significant (p < 0.001) increase in mean knowledge scores across all five CVD domains. A culturally tailored Spanish language-based health program is effective in increasing CVD awareness among high CVD risk Hispanic women with low English proficiency and low baseline CVD knowledge.

Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.

PubMed

Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

2013-06-01

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

AMPHION: Specification-based programming for scientific subroutine libraries

NASA Technical Reports Server (NTRS)

Lowry, Michael; Philpot, Andrew; Pressburger, Thomas; Underwood, Ian; Waldinger, Richard; Stickel, Mark

1994-01-01

AMPHION is a knowledge-based software engineering (KBSE) system that guides a user in developing a diagram representing a formal problem specification. It then automatically implements a solution to this specification as a program consisting of calls to subroutines from a library. The diagram provides an intuitive domain oriented notation for creating a specification that also facilitates reuse and modification. AMPHION'S architecture is domain independent. AMPHION is specialized to an application domain by developing a declarative domain theory. Creating a domain theory is an iterative process that currently requires the joint expertise of domain experts and experts in automated formal methods for software development.

The Knowledge Base of the Evaluation Domain.

ERIC Educational Resources Information Center

Seels, Barbara

This paper is concerned with defining evaluation as a domain in instructional technology, and with specifying the sub-areas of the domain. In education, evaluation is the process of determining the adequacy of instruction. It begins with problem analysis, which refers to determining the nature of the solution and the parameters of the problem. A…

A Mixed-Response Intelligent Tutoring System Based on Learning from Demonstration

ERIC Educational Resources Information Center

Alvarez Xochihua, Omar

2012-01-01

Intelligent Tutoring Systems (ITS) have a significant educational impact on student's learning. However, researchers report time intensive interaction is needed between ITS developers and domain-experts to gather and represent domain knowledge. The challenge is augmented when the target domain is ill-defined. The primary problem resides in…

Case-based reasoning for space applications: Utilization of prior experience in knowledge-based systems

NASA Technical Reports Server (NTRS)

King, James A.

1987-01-01

The goal is to explain Case-Based Reasoning as a vehicle to establish knowledge-based systems based on experimental reasoning for possible space applications. This goal will be accomplished through an examination of reasoning based on prior experience in a sample domain, and also through a presentation of proposed space applications which could utilize Case-Based Reasoning techniques.

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain.

PubMed

Hassanpour, Saeed; O'Connor, Martin J; Das, Amar K

2013-08-12

A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text. Using an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test. Our work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.

Building Capacity for Work-Readiness: Bridging the Cognitive and Affective Domains

ERIC Educational Resources Information Center

Bandaranaike, Suniti; Willison, John

2015-01-01

Teaching for work-integrated learning (WIL) competency is largely directed at delivering knowledge based cognitive skills with little emphasis on affective skills. This study looks at empirical evidence of WIL students through their understanding of the cognitive and affective domains. The research is based on a validated employability framework,…

Text Processing of Domain-Related Information for Individuals with High and Low Domain Knowledge.

ERIC Educational Resources Information Center

Spilich, George J.; And Others

1979-01-01

The way in which previously acquired knowledge affects the processing on new domain-related information was investigated. Text processing was studied in two groups differing in knowledge of the domain of baseball. A knowledge structure for the domain was constructed, and text propositions were classified. (SW)

Description of research interests and current work related to automating software design

NASA Technical Reports Server (NTRS)

Kaindl, Hermann

1992-01-01

Enclosed is a list of selected and recent publications. Most of these publications concern applied research in the areas of software engineering and human-computer interaction. It is felt that domain-specific knowledge plays a major role in software development. Additionally, it is believed that improvements in the general software development process (e.g., object-oriented approaches) will have to be combined with the use of large domain-specific knowledge bases.

Knowledge-based requirements analysis for automating software development

NASA Technical Reports Server (NTRS)

Markosian, Lawrence Z.

1988-01-01

We present a new software development paradigm that automates the derivation of implementations from requirements. In this paradigm, informally-stated requirements are expressed in a domain-specific requirements specification language. This language is machine-understable and requirements expressed in it are captured in a knowledge base. Once the requirements are captured, more detailed specifications and eventually implementations are derived by the system using transformational synthesis. A key characteristic of the process is that the required human intervention is in the form of providing problem- and domain-specific engineering knowledge, not in writing detailed implementations. We describe a prototype system that applies the paradigm in the realm of communication engineering: the prototype automatically generates implementations of buffers following analysis of the requirements on each buffer.

A diagnostic prototype of the potable water subsystem of the Space Station Freedom ECLSS

NASA Technical Reports Server (NTRS)

Lukefahr, Brenda D.; Rochowiak, Daniel M.; Benson, Brian L.; Rogers, John S.; Mckee, James W.

1989-01-01

In analyzing the baseline Environmental Control and Life Support System (ECLSS) command and control architecture, various processes are found which would be enhanced by the use of knowledge based system methods of implementation. The most suitable process for prototyping using rule based methods are documented, while domain knowledge resources and other practical considerations are examined. Requirements for a prototype rule based software system are documented. These requirements reflect Space Station Freedom ECLSS software and hardware development efforts, and knowledge based system requirements. A quick prototype knowledge based system environment is researched and developed.

DOLCE ROCKS: Integrating Foundational and Geoscience Ontologies--Preliminary Results for the Integration of Concepts from DOLCE, GeoSciML, and SWEET

NASA Astrophysics Data System (ADS)

Brodaric, B.; Probst, F.

2007-12-01

Ontologies are being developed bottom-up in many geoscience domains to aid semantic-enabled computing. The contents of these ontologies are typically partitioned along domain boundaries, such as geology, geophsyics, hydrology, or are developed for specific data sets or processing needs. At the same time, very general foundational ontologies are being independently developed top-down to help facilitate integration of knowledge across such domains, and to provide homogeneity to the organization of knowledge within the domains. In this work we investigate the suitability of integrating the DOLCE foundational ontology with concepts from two prominent geoscience knowledge representations, GeoSciML and SWEET, to investigate the alignment of the concepts found within the foundational and domain representations. The geoscience concepts are partially mapped to each other and to those in the foundational ontology, via the subclass and other relations, resulting in an integrated OWL-based ontology called DOLCE ROCKS. These preliminary results demonstrate variable alignment between the foundational and domain concepts, and also between the domain concepts. Further work is required to ascertain the impact of this integrated ontology approach on broader geoscience ontology design, on the unification of domain ontologies, as well as their use within semantic-enabled geoscience applications.

Competence-Based Knowledge Structures for Personalised Learning

ERIC Educational Resources Information Center

Heller, Jurgen; Steiner, Christina; Hockemeyer, Cord; Albert, Dietrich

2006-01-01

Competence-based extensions of Knowledge Space Theory are suggested as a formal framework for implementing key features of personalised learning in technology-enhanced learning. The approach links learning objects and assessment problems to the relevant skills that are taught or required. Various ways to derive these skills from domain ontologies…

Finding Environmental Knowledge in SCUBA-Based Textual Materials

ERIC Educational Resources Information Center

Gündogdu, Cemal; Aygün, Yalin; Ilkim, Mehmet

2018-01-01

As marine environments within the adventure domain are future key-settings for recreational SCUBA diving experience, SCUBA-based textual materials should provide insight into environmental knowledge that is well connected to the novice divers' behaviour and attitude. This research is concerned with a major recreational SCUBA diver manual for…

EVA: Collaborative Distributed Learning Environment Based in Agents.

ERIC Educational Resources Information Center

Sheremetov, Leonid; Tellez, Rolando Quintero

In this paper, a Web-based learning environment developed within the project called Virtual Learning Spaces (EVA, in Spanish) is presented. The environment is composed of knowledge, collaboration, consulting, experimentation, and personal spaces as a collection of agents and conventional software components working over the knowledge domains. All…

EVA: An Interactive Web-Based Collaborative Learning Environment

ERIC Educational Resources Information Center

Sheremetov, Leonid; Arenas, Adolfo Guzman

2002-01-01

In this paper, a Web-based learning environment developed within the project called Virtual Learning Spaces (EVA, in Spanish) is described. The environment is composed of knowledge, collaboration, consulting and experimentation spaces as a collection of agents and conventional software components working over the knowledge domains. All user…

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

An automated knowledge-based textual summarization system for longitudinal, multivariate clinical data.

PubMed

Goldstein, Ayelet; Shahar, Yuval

2016-06-01

Design and implement an intelligent free-text summarization system: The system's input includes large numbers of longitudinal, multivariate, numeric and symbolic clinical raw data, collected over varying periods of time, and in different complex contexts, and a suitable medical knowledge base. The system then automatically generates a textual summary of the data. We aim to prove the feasibility of implementing such a system, and to demonstrate its potential benefits for clinicians and for enhancement of quality of care. We have designed a new, domain-independent, knowledge-based system, the CliniText system, for automated summarization in free text of longitudinal medical records of any duration, in any context. The system is composed of six components: (1) A temporal abstraction module generates all possible abstractions from the patient's raw data using a temporal-abstraction knowledge base; (2) The abductive reasoning module infers abstractions or events from the data, which were not explicitly included in the database; (3) The pruning module filters out raw or abstract data based on predefined heuristics; (4) The document structuring module organizes the remaining raw or abstract data, according to the desired format; (5) The microplanning module, groups the raw or abstract data and creates referring expressions; (6) The surface realization module, generates the text, and applies the grammar rules of the chosen language. We have performed an initial technical evaluation of the system in the cardiac intensive-care and diabetes domains. We also summarize the results of a more detailed evaluation study that we have performed in the intensive-care domain that assessed the completeness, correctness, and overall quality of the system's generated text, and its potential benefits to clinical decision making. We assessed these measures for 31 letters originally composed by clinicians, and for the same letters when generated by the CliniText system. We have successfully implemented all of the components of the CliniText system in software. We have also been able to create a comprehensive temporal-abstraction knowledge base to support its functionality, mostly in the intensive-care domain. The initial technical evaluation of the system in the cardiac intensive-care and diabetes domains has shown great promise, proving the feasibility of constructing and operating such systems. The detailed results of the evaluation in the intensive-care domain are out of scope of the current paper, and we refer the reader to a more detailed source. In all of the letters composed by clinicians, there were at least two important items per letter missed that were included by the CliniText system. The clinicians' letters got a significantly better grade in three out of four measured quality parameters, as judged by an expert; however, the variance in the quality was much higher in the clinicians' letters. In addition, three clinicians answered questions based on the discharge letter 40% faster, and answered four out of the five questions equally well or significantly better, when using the CliniText-generated letters, than when using the clinician-composed letters. Constructing a working system for automated summarization in free text of large numbers of varying periods of multivariate longitudinal clinical data is feasible. So is the construction of a large knowledge base, designed to support such a system, in a complex clinical domain, such as the intensive-care domain. The integration of the quality and functionality results suggests that the optimal discharge letter should exploit both human and machine, possibly by creating a machine-generated draft that will be polished by a human clinician. Copyright © 2016 Elsevier Inc. All rights reserved.

Capturing domain knowledge from multiple sources: the rare bone disorders use case.

PubMed

Groza, Tudor; Tudorache, Tania; Robinson, Peter N; Zankl, Andreas

2015-01-01

Lately, ontologies have become a fundamental building block in the process of formalising and storing complex biomedical information. The community-driven ontology curation process, however, ignores the possibility of multiple communities building, in parallel, conceptualisations of the same domain, and thus providing slightly different perspectives on the same knowledge. The individual nature of this effort leads to the need of a mechanism to enable us to create an overarching and comprehensive overview of the different perspectives on the domain knowledge. We introduce an approach that enables the loose integration of knowledge emerging from diverse sources under a single coherent interoperable resource. To accurately track the original knowledge statements, we record the provenance at very granular levels. We exemplify the approach in the rare bone disorders domain by proposing the Rare Bone Disorders Ontology (RBDO). Using RBDO, researchers are able to answer queries, such as: "What phenotypes describe a particular disorder and are common to all sources?" or to understand similarities between disorders based on divergent groupings (classifications) provided by the underlying sources. RBDO is available at http://purl.org/skeletome/rbdo. In order to support lightweight query and integration, the knowledge captured by RBDO has also been made available as a SPARQL Endpoint at http://bio-lark.org/se_skeldys.html.

Automation of energy demand forecasting

NASA Astrophysics Data System (ADS)

Siddique, Sanzad

Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning models are tested with synthetic data as well as with natural gas and electricity demand signals. Experimental results show that the model searching technique is capable of finding an appropriate forecasting model. Further experimental results demonstrate an improved forecasting accuracy achieved by using the novel machine learning techniques introduced in this thesis. This thesis presents an analysis of how the machine learning techniques learn domain knowledge. The learned domain knowledge is used to improve the forecast accuracy.

A cross-disciplinary response to improve test activities: The corporate memory capitalization in Ariane4 test domain

NASA Technical Reports Server (NTRS)

Vo, Dinh Phuoc; Soler, Christian; Aussenac, N.; Macchion, D.

1993-01-01

The Assembly, Integration, Test, and Validation (AIT/AIV) of the Ariane4 Vehicle Equipment Bay was held at Matra Marconi Space (MMS) site of Toulouse for several years. For this activity, incident interpretation necessitates a great deal of different knowledge. When complex faults occur, particularly those appearing during overall control tests, experts of various domains (EGSE, software, on-board equipment) have to join for investigation sessions. Thus, an assistance tool for the identification of faulty equipment will improve the efficiency of diagnosis and the overall productivity of test activities. As a solution, the Aramiihs laboratory proposed considering the opportunity of a knowledge based system intended to assist the tester in diagnosis. This knowledge based system is, in fact, a short-term achievement of a long-term goal which is the capitalization of corporate memory in the Ariane4 test domain. Aramiihs is a research unit where engineers from MMS and researchers from the IRIT-CNRS cooperate on problems concerning new types of man-system interaction.

Evolving Expert Knowledge Bases: Applications of Crowdsourcing and Serious Gaming to Advance Knowledge Development for Intelligent Tutoring Systems

ERIC Educational Resources Information Center

Floryan, Mark

2013-01-01

This dissertation presents a novel effort to develop ITS technologies that adapt by observing student behavior. In particular, we define an evolving expert knowledge base (EEKB) that structures a domain's information as a set of nodes and the relationships that exist between those nodes. The structure of this model is not the particularly novel…

Enhancing Learning Outcomes with an Interactive Knowledge-Based Learning Environment Providing Narrative Feedback

ERIC Educational Resources Information Center

Stranieri, Andrew; Yearwood, John

2008-01-01

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a…

Automatic Domain Adaptation of Word Sense Disambiguation Based on Sublanguage Semantic Schemata Applied to Clinical Narrative

ERIC Educational Resources Information Center

Patterson, Olga

2012-01-01

Domain adaptation of natural language processing systems is challenging because it requires human expertise. While manual effort is effective in creating a high quality knowledge base, it is expensive and time consuming. Clinical text adds another layer of complexity to the task due to privacy and confidentiality restrictions that hinder the…

Workplace nutrition knowledge questionnaire: psychometric validation and application.

PubMed

Guadagnin, Simone C; Nakano, Eduardo Y; Dutra, Eliane S; de Carvalho, Kênia M B; Ito, Marina K

2016-11-01

Workplace dietary intervention studies in low- and middle-income countries using psychometrically sound measures are scarce. This study aimed to validate a nutrition knowledge questionnaire (NQ) and its utility in evaluating the changes in knowledge among participants of a Nutrition Education Program (NEP) conducted at the workplace. A NQ was tested for construct validity, internal consistency and discriminant validity. It was applied in a NEP conducted at six workplaces, in order to evaluate the effect of an interactive or a lecture-based education programme on nutrition knowledge. Four knowledge domains comprising twenty-three items were extracted in the final version of the NQ. Internal consistency of each domain was significant, with Kuder-Richardson formula values>0·60. These four domains presented a good fit in the confirmatory factor analysis. In the discriminant validity test, both the Expert and Lay groups scored>0·52, but the Expert group scores were significantly higher than those of the Lay group in all domains. When the NQ was applied in the NEP, the overall questionnaire scores increased significantly because of the NEP intervention, in both groups (P<0·001). However, the increase in NQ scores was significantly higher in the interactive group than in the lecture group, in the overall score (P=0·008) and in the healthy eating domain (P=0·009). The validated NQ is a short and useful tool to assess gain in nutrition knowledge among participants of NEP at the workplace. According to the NQ, an interactive nutrition education had a higher impact on nutrition knowledge than a lecture programme.

A Knowledge-Based System Developer for aerospace applications

NASA Technical Reports Server (NTRS)

Shi, George Z.; Wu, Kewei; Fensky, Connie S.; Lo, Ching F.

1993-01-01

A prototype Knowledge-Based System Developer (KBSD) has been developed for aerospace applications by utilizing artificial intelligence technology. The KBSD directly acquires knowledge from domain experts through a graphical interface then builds expert systems from that knowledge. This raises the state of the art of knowledge acquisition/expert system technology to a new level by lessening the need for skilled knowledge engineers. The feasibility, applicability , and efficiency of the proposed concept was established, making a continuation which would develop the prototype to a full-scale general-purpose knowledge-based system developer justifiable. The KBSD has great commercial potential. It will provide a marketable software shell which alleviates the need for knowledge engineers and increase productivity in the workplace. The KBSD will therefore make knowledge-based systems available to a large portion of industry.

Advanced Software Development Workstation Project

NASA Technical Reports Server (NTRS)

Lee, Daniel

1989-01-01

The Advanced Software Development Workstation Project, funded by Johnson Space Center, is investigating knowledge-based techniques for software reuse in NASA software development projects. Two prototypes have been demonstrated and a third is now in development. The approach is to build a foundation that provides passive reuse support, add a layer that uses domain-independent programming knowledge, add a layer that supports the acquisition of domain-specific programming knowledge to provide active support, and enhance maintainability and modifiability through an object-oriented approach. The development of new application software would use specification-by-reformulation, based on a cognitive theory of retrieval from very long-term memory in humans, and using an Ada code library and an object base. Current tasks include enhancements to the knowledge representation of Ada packages and abstract data types, extensions to support Ada package instantiation knowledge acquisition, integration with Ada compilers and relational databases, enhancements to the graphical user interface, and demonstration of the system with a NASA contractor-developed trajectory simulation package. Future work will focus on investigating issues involving scale-up and integration.

Changing Conception of Sources of Memory Development. 1985/23.

ERIC Educational Resources Information Center

Chi, Michelene T. H.

1985-01-01

Explanations for memory development have tended to focus on acquistion of general strategies and metaknowledge. Recently, emphasis has been given to the knowledge base as a whole, including general world-knowledge and domain-specific knowledge and procedures. Evidence is presented from the memory development literature showing why strategies and…

Intersecting Domains of Assessment Knowledge: School Typologies Based on Interviews with Secondary Teachers

ERIC Educational Resources Information Center

Howley, Marged D.; Howley, Aimee; Henning, John E.; Gillam, Mary Beth; Weade, Ginger

2013-01-01

This study used qualitative interviewing with teachers at three high schools to answer research questions about teachers' assessment knowledge, school-specific assessment cultures, and teachers' perceptions of the assessment literacy of other key stakeholders. Data analysis revealed shared knowledge and practices across schools--use of formative…

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.

Fuzzy ontologies for semantic interpretation of remotely sensed images

NASA Astrophysics Data System (ADS)

Djerriri, Khelifa; Malki, Mimoun

2015-10-01

Object-based image classification consists in the assignment of object that share similar attributes to object categories. To perform such a task the remote sensing expert uses its personal knowledge, which is rarely formalized. Ontologies have been proposed as solution to represent domain knowledge agreed by domain experts in a formal and machine readable language. Classical ontology languages are not appropriate to deal with imprecision or vagueness in knowledge. Fortunately, Description Logics for the semantic web has been enhanced by various approaches to handle such knowledge. This paper presents the extension of the traditional ontology-based interpretation with fuzzy ontology of main land-cover classes in Landsat8-OLI scenes (vegetation, built-up areas, water bodies, shadow, clouds, forests) objects. A good classification of image objects was obtained and the results highlight the potential of the method to be replicated over time and space in the perspective of transferability of the procedure.

Reuse: A knowledge-based approach

NASA Technical Reports Server (NTRS)

Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui

1992-01-01

This paper describes our research in automating the reuse process through the use of application domain models. Application domain models are explicit formal representations of the application knowledge necessary to understand, specify, and generate application programs. Furthermore, they provide a unified repository for the operational structure, rules, policies, and constraints of a specific application area. In our approach, domain models are expressed in terms of a transaction-based meta-modeling language. This paper has described in detail the creation and maintenance of hierarchical structures. These structures are created through a process that includes reverse engineering of data models with supplementary enhancement from application experts. Source code is also reverse engineered but is not a major source of domain model instantiation at this time. In the second phase of the software synthesis process, program specifications are interactively synthesized from an instantiated domain model. These specifications are currently integrated into a manual programming process but will eventually be used to derive executable code with mechanically assisted transformations. This research is performed within the context of programming-in-the-large types of systems. Although our goals are ambitious, we are implementing the synthesis system in an incremental manner through which we can realize tangible results. The client/server architecture is capable of supporting 16 simultaneous X/Motif users and tens of thousands of attributes and classes. Domain models have been partially synthesized from five different application areas. As additional domain models are synthesized and additional knowledge is gathered, we will inevitably add to and modify our representation. However, our current experience indicates that it will scale and expand to meet our modeling needs.

Understanding natural language for spacecraft sequencing

NASA Technical Reports Server (NTRS)

Katz, Boris; Brooks, Robert N., Jr.

1987-01-01

The paper describes a natural language understanding system, START, that translates English text into a knowledge base. The understanding and the generating modules of START share a Grammar which is built upon reversible transformations. Users can retrieve information by querying the knowledge base in English; the system then produces an English response. START can be easily adapted to many different domains. One such domain is spacecraft sequencing. A high-level overview of sequencing as it is practiced at JPL is presented in the paper, and three areas within this activity are identified for potential application of the START system. Examples are given of an actual dialog with START based on simulated data for the Mars Observer mission.

Diagnosis by integrating model-based reasoning with knowledge-based reasoning

NASA Technical Reports Server (NTRS)

Bylander, Tom

1988-01-01

Our research investigates how observations can be categorized by integrating a qualitative physical model with experiential knowledge. Our domain is diagnosis of pathologic gait in humans, in which the observations are the gait motions, muscle activity during gait, and physical exam data, and the diagnostic hypotheses are the potential muscle weaknesses, muscle mistimings, and joint restrictions. Patients with underlying neurological disorders typically have several malfunctions. Among the problems that need to be faced are: the ambiguity of the observations, the ambiguity of the qualitative physical model, correspondence of the observations and hypotheses to the qualitative physical model, the inherent uncertainty of experiential knowledge, and the combinatorics involved in forming composite hypotheses. Our system divides the work so that the knowledge-based reasoning suggests which hypotheses appear more likely than others, the qualitative physical model is used to determine which hypotheses explain which observations, and another process combines these functionalities to construct a composite hypothesis based on explanatory power and plausibility. We speculate that the reasoning architecture of our system is generally applicable to complex domains in which a less-than-perfect physical model and less-than-perfect experiential knowledge need to be combined to perform diagnosis.

Public health situation awareness: toward a semantic approach

NASA Astrophysics Data System (ADS)

Mirhaji, Parsa; Richesson, Rachel L.; Turley, James P.; Zhang, Jiajie; Smith, Jack W.

2004-04-01

We propose a knowledge-based public health situation awareness system. The basis for this system is an explicit representation of public health situation awareness concepts and their interrelationships. This representation is based upon the users" (public health decision makers) cognitive model of the world, and optimized towards the efficacy of performance and relevance to the public health situation awareness processes and tasks. In our approach, explicit domain knowledge is the foundation for interpretation of public health data, as apposed to conventional systems where the statistical methods are the essence of the processes. Objectives: To develop a prototype knowledge-based system for public health situation awareness and to demonstrate the utility of knowledge intensive approaches in integration of heterogeneous information, eliminating the effects of incomplete and poor quality surveillance data, uncertainty in syndrome and aberration detection and visualization of complex information structures in public health surveillance settings, particularly in the context of bioterrorism (BT) preparedness. The system employs the Resource Definition Framework (RDF) and additional layers of more expressive languages to explicate the knowledge of domain experts into machine interpretable and computable problem-solving modules that can then guide users and computer systems in sifting through the most "relevant" data for syndrome and outbreak detection and investigation of root cause of the event. The Center for Biosecurity and Public Health Informatics Research is developing a prototype knowledge-based system around influenza, which has complex natural disease patterns, many public health implications, and is a potential agent for bioterrorism. The preliminary data from this effort may demonstrate superior performance in information integration, syndrome and aberration detection, information access through information visualization, and cross-domain investigation of the root causes of public health events.

Effects of Domain Knowledge, Working Memory Capacity, and Age on Cognitive Performance: An Investigation of the Knowledge-Is-Power Hypothesis

ERIC Educational Resources Information Center

Hambrick, David Z.; Engle, Randall W.

2002-01-01

Domain knowledge facilitates performance in many cognitive tasks. However, very little is known about the interplay between domain knowledge and factors that are believed to reflect general, and relatively stable, characteristics of the individual. The primary goal of this study was to investigate the interplay between domain knowledge and one…

Effects of domain knowledge, working memory capacity, and age on cognitive performance: an investigation of the knowledge-is-power hypothesis.

PubMed

Hambrick, David Z; Engle, Randall W

2002-06-01

Domain knowledge facilitates performance in many cognitive tasks. However, very little is known about the interplay between domain knowledge and factors that are believed to reflect general, and relatively stable, characteristics of the individual. The primary goal of this study was to investigate the interplay between domain knowledge and one such factor: working memory capacity. Adults from wide ranges of working memory capacity, age, and knowledge about the game of baseball listened to, and then answered questions about, simulated radio broadcasts of baseball games. There was a strong facilitative effect of preexisting knowledge of baseball on memory performance, particularly for information judged to be directly relevant to the baseball games. However, there was a positive effect of working memory capacity on memory performance as well, and there was no indication that domain knowledge attenuated this effect. That is, working memory capacity contributed to memory performance even at high levels of domain knowledge. Similarly, there was no evidence that domain knowledge attenuated age-related differences (favoring young adults) in memory performance. We discuss implications of the results for understanding proficiency in cognitive domains from an individual-differences perspective. Copyright 2001 Elsevier Science (USA).

Domains and naïve theories.

PubMed

Gelman, Susan A; Noles, Nicholaus S

2011-09-01

Human cognition entails domain-specific cognitive processes that influence memory, attention, categorization, problem-solving, reasoning, and knowledge organization. This article examines domain-specific causal theories, which are of particular interest for permitting an examination of how knowledge structures change over time. We first describe the properties of commonsense theories, and how commonsense theories differ from scientific theories, illustrating with children's classification of biological and nonbiological kinds. We next consider the implications of domain-specificity for broader issues regarding cognitive development and conceptual change. We then examine the extent to which domain-specific theories interact, and how people reconcile competing causal frameworks. Future directions for research include examining how different content domains interact, the nature of theory change, the role of context (including culture, language, and social interaction) in inducing different frameworks, and the neural bases for domain-specific reasoning. WIREs Cogni Sci 2011 2 490-502 DOI: 10.1002/wcs.124 This article is categorized under: Psychology > Reasoning and Decision Making. Copyright © 2010 John Wiley & Sons, Ltd.

Towards Semantic e-Science for Traditional Chinese Medicine

PubMed Central

Chen, Huajun; Mao, Yuxin; Zheng, Xiaoqing; Cui, Meng; Feng, Yi; Deng, Shuiguang; Yin, Aining; Zhou, Chunying; Tang, Jinming; Jiang, Xiaohong; Wu, Zhaohui

2007-01-01

Background Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science. Results We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research. Conclusion Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline. PMID:17493289

Towards organizing health knowledge on community-based health services.

PubMed

Akbari, Mohammad; Hu, Xia; Nie, Liqiang; Chua, Tat-Seng

2016-12-01

Online community-based health services accumulate a huge amount of unstructured health question answering (QA) records at a continuously increasing pace. The ability to organize these health QA records has been found to be effective for data access. The existing approaches for organizing information are often not applicable to health domain due to its domain nature as characterized by complex relation among entities, large vocabulary gap, and heterogeneity of users. To tackle these challenges, we propose a top-down organization scheme, which can automatically assign the unstructured health-related records into a hierarchy with prior domain knowledge. Besides automatic hierarchy prototype generation, it also enables each data instance to be associated with multiple leaf nodes and profiles each node with terminologies. Based on this scheme, we design a hierarchy-based health information retrieval system. Experiments on a real-world dataset demonstrate the effectiveness of our scheme in organizing health QA into a topic hierarchy and retrieving health QA records from the topic hierarchy.

Is risk analysis scientific?

PubMed

Hansson, Sven Ove; Aven, Terje

2014-07-01

This article discusses to what extent risk analysis is scientific in view of a set of commonly used definitions and criteria. We consider scientific knowledge to be characterized by its subject matter, its success in developing the best available knowledge in its fields of study, and the epistemic norms and values that guide scientific investigations. We proceed to assess the field of risk analysis according to these criteria. For this purpose, we use a model for risk analysis in which science is used as a base for decision making on risks, which covers the five elements evidence, knowledge base, broad risk evaluation, managerial review and judgment, and the decision; and that relates these elements to the domains experts and decisionmakers, and to the domains fact-based or value-based. We conclude that risk analysis is a scientific field of study, when understood as consisting primarily of (i) knowledge about risk-related phenomena, processes, events, etc., and (ii) concepts, theories, frameworks, approaches, principles, methods and models to understand, assess, characterize, communicate, and manage risk, in general and for specific applications (the instrumental part). © 2014 Society for Risk Analysis.

Grid workflow validation using ontology-based tacit knowledge: A case study for quantitative remote sensing applications

NASA Astrophysics Data System (ADS)

Liu, Jia; Liu, Longli; Xue, Yong; Dong, Jing; Hu, Yingcui; Hill, Richard; Guang, Jie; Li, Chi

2017-01-01

Workflow for remote sensing quantitative retrieval is the ;bridge; between Grid services and Grid-enabled application of remote sensing quantitative retrieval. Workflow averts low-level implementation details of the Grid and hence enables users to focus on higher levels of application. The workflow for remote sensing quantitative retrieval plays an important role in remote sensing Grid and Cloud computing services, which can support the modelling, construction and implementation of large-scale complicated applications of remote sensing science. The validation of workflow is important in order to support the large-scale sophisticated scientific computation processes with enhanced performance and to minimize potential waste of time and resources. To research the semantic correctness of user-defined workflows, in this paper, we propose a workflow validation method based on tacit knowledge research in the remote sensing domain. We first discuss the remote sensing model and metadata. Through detailed analysis, we then discuss the method of extracting the domain tacit knowledge and expressing the knowledge with ontology. Additionally, we construct the domain ontology with Protégé. Through our experimental study, we verify the validity of this method in two ways, namely data source consistency error validation and parameters matching error validation.

[Trends of doctoral dissertations in nursing science: focused on studies submitted since 2000].

PubMed

Shin, Hyunsook; Sung, Kyung-Mi; Jeong, Seok Hee; Kim, Dae-Ran

2008-02-01

The purpose of this study was to identify the characteristics of doctoral dissertations in nursing science submitted since 2000. Three-hundred and five dissertations of six schools of nursing published from 2000 to 2006 in Korea were analyzed with the categories of philosophy, method, body of knowledge, research design, and nursing domain. In philosophy, 82% of all dissertations were identified as scientific realism, 15% were relativism, and 3% were practicism. Two-hundred and fifty dissertations (82%) were divided into a quantitative methodology and 55 dissertations (18%) were qualitative methodology. Specifically, 45% were experimental, 23% methodological, 13% survey and 17% qualitative designed researches. Prescriptive knowledge was created in 47% of dissertations, explanatory knowledge in 29%, and descriptive knowledge in 24%. Over 50% of all research was studied with a community-based population. In the nursing domain, dissertations of the practice domain were highest (48.2%). Dissertations since 2000 were markedly different from the characteristics of the previous studies (1982-1999) in the increase of situation-related, prescriptive and community-based population studies. A picture of current nursing science identified in this study may provide a future guideline for the doctoral education for nursing.

Use of metaknowledge in the verification of knowledge-based systems

NASA Technical Reports Server (NTRS)

Morell, Larry J.

1989-01-01

Knowledge-based systems are modeled as deductive systems. The model indicates that the two primary areas of concern in verification are demonstrating consistency and completeness. A system is inconsistent if it asserts something that is not true of the modeled domain. A system is incomplete if it lacks deductive capability. Two forms of consistency are discussed along with appropriate verification methods. Three forms of incompleteness are discussed. The use of metaknowledge, knowledge about knowledge, is explored in connection to each form of incompleteness.

Knowledge acquisition in the fuzzy knowledge representation framework of a medical consultation system.

PubMed

Boegl, Karl; Adlassnig, Klaus-Peter; Hayashi, Yoichi; Rothenfluh, Thomas E; Leitich, Harald

2004-01-01

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily.

A metamodel for mobile forensics investigation domain

PubMed Central

Abd Razak, Shukor; Othman, Siti Hajar; Mohammed, Arafat; Saeed, Faisal

2017-01-01

With the rapid development of technology, mobile phones have become an essential tool in terms of crime fighting and criminal investigation. However, many mobile forensics investigators face difficulties with the investigation process in their domain. These difficulties are due to the heavy reliance of the forensics field on knowledge which, although a valuable resource, is scattered and widely dispersed. The wide dispersion of mobile forensics knowledge not only makes investigation difficult for new investigators, resulting in substantial waste of time, but also leads to ambiguity in the concepts and terminologies of the mobile forensics domain. This paper developed an approach for mobile forensics domain based on metamodeling. The developed approach contributes to identify common concepts of mobile forensics through a development of the Mobile Forensics Metamodel (MFM). In addion, it contributes to simplifying the investigation process and enables investigation teams to capture and reuse specialized forensic knowledge, thereby supporting the training and knowledge management activities. Furthermore, it reduces the difficulty and ambiguity in the mobile forensics domain. A validation process was performed to ensure the completeness and correctness of the MFM. The validation was conducted using two techniques for improvements and adjustments to the metamodel. The last version of the adjusted metamodel was named MFM 1.2. PMID:28445486

A Novel Domain Assembly Routine for Creating Full-Length Models of Membrane Proteins from Known Domain Structures.

PubMed

Koehler Leman, Julia; Bonneau, Richard

2018-04-03

Membrane proteins composed of soluble and membrane domains are often studied one domain at a time. However, to understand the biological function of entire protein systems and their interactions with each other and drugs, knowledge of full-length structures or models is required. Although few computational methods exist that could potentially be used to model full-length constructs of membrane proteins, none of these methods are perfectly suited for the problem at hand. Existing methods require an interface or knowledge of the relative orientations of the domains or are not designed for domain assembly, and none of them are developed for membrane proteins. Here we describe the first domain assembly protocol specifically designed for membrane proteins that assembles intra- and extracellular soluble domains and the transmembrane domain into models of the full-length membrane protein. Our protocol does not require an interface between the domains and samples possible domain orientations based on backbone dihedrals in the flexible linker regions, created via fragment insertion, while keeping the transmembrane domain fixed in the membrane. For five examples tested, our method mp_domain_assembly, implemented in RosettaMP, samples domain orientations close to the known structure and is best used in conjunction with experimental data to reduce the conformational search space.

A knowledge-driven approach to biomedical document conceptualization.

PubMed

Zheng, Hai-Tao; Borchert, Charles; Jiang, Yong

2010-06-01

Biomedical document conceptualization is the process of clustering biomedical documents based on ontology-represented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective of this work is to develop an effective method to cluster biomedical documents based on various user-specified ontologies, so that users can exploit the concept structures of documents more effectively. We develop a flexible framework to allow users to specify the knowledge bases, in the form of ontologies. Based on the user-specified ontologies, we develop a key concept induction algorithm, which uses latent semantic analysis to identify key concepts and cluster documents. A corpus-related ontology generation algorithm is developed to generate the concept structures of documents. Based on two biomedical datasets, we evaluate the proposed method and five other clustering algorithms. The clustering results of the proposed method outperform the five other algorithms, in terms of key concept identification. With respect to the first biomedical dataset, our method has the F-measure values 0.7294 and 0.5294 based on the MeSH ontology and gene ontology (GO), respectively. With respect to the second biomedical dataset, our method has the F-measure values 0.6751 and 0.6746 based on the MeSH ontology and GO, respectively. Both results outperforms the five other algorithms in terms of F-measure. Based on the MeSH ontology and GO, the generated corpus-related ontologies show informative conceptual structures. The proposed method enables users to specify the domain knowledge to exploit the conceptual structures of biomedical document collections. In addition, the proposed method is able to extract the key concepts and cluster the documents with a relatively high precision. Copyright 2010 Elsevier B.V. All rights reserved.

The KASE approach to domain-specific software systems

NASA Technical Reports Server (NTRS)

Bhansali, Sanjay; Nii, H. Penny

1992-01-01

Designing software systems, like all design activities, is a knowledge-intensive task. Several studies have found that the predominant cause of failures among system designers is lack of knowledge: knowledge about the application domain, knowledge about design schemes, knowledge about design processes, etc. The goal of domain-specific software design systems is to explicitly represent knowledge relevant to a class of applications and use it to partially or completely automate various aspects of the designing systems within that domain. The hope is that this would reduce the intellectual burden on the human designers and lead to more efficient software development. In this paper, we present a domain-specific system built on top of KASE, a knowledge-assisted software engineering environment being developed at the Stanford Knowledge Systems Laboratory. We introduce the main ideas underlying the construction of domain specific systems within KASE, illustrate the application of the idea in the synthesis of a system for tracking aircraft from radar signals, and discuss some of the issues in constructing domain-specific systems.

Methodology for testing and validating knowledge bases

NASA Technical Reports Server (NTRS)

Krishnamurthy, C.; Padalkar, S.; Sztipanovits, J.; Purves, B. R.

1987-01-01

A test and validation toolset developed for artificial intelligence programs is described. The basic premises of this method are: (1) knowledge bases have a strongly declarative character and represent mostly structural information about different domains, (2) the conditions for integrity, consistency, and correctness can be transformed into structural properties of knowledge bases, and (3) structural information and structural properties can be uniformly represented by graphs and checked by graph algorithms. The interactive test and validation environment have been implemented on a SUN workstation.

Intelligent systems for human resources.

PubMed

Kline, K B

1988-11-01

An intelligent system contains knowledge about some domain; it has sophisticated decision-making processes and the ability to explain its actions. The most important aspect of an intelligent system is its ability to effectively interact with humans to teach or assist complex information processing. Two intelligent systems are Intelligent Tutoring Systems (ITs) and Expert Systems. The ITSs provide instruction to a student similar to a human tutor. The ITSs capture individual performance and tutor deficiencies. These systems consist of an expert module, which contains the knowledge or material to be taught; the student module, which contains a representation of the knowledge the student knows and does not know about the domain; and the instructional or teaching module, which selects specific knowledge to teach, the instructional strategy, and provides assistance to the student to tutor deficiencies. Expert systems contain an expert's knowledge about some domain and perform specialized tasks or aid a novice in the performance of certain tasks. The most important part of an expert system is the knowledge base. This knowledge base contains all the specialized and technical knowledge an expert possesses. For an expert system to interact effectively with humans, it must have the ability to explain its actions. Use of intelligent systems can have a profound effect on human resources. The ITSs can provide better training by tutoring on an individual basis, and the expert systems can make better use of human resources through job aiding and performing complex tasks. With increasing training requirements and "doing more with less," intelligent systems can have a positive effect on human resources.

An Ada Based Expert System for the Ada Version of SAtool II. Volume 1 and 2

DTIC Science & Technology

1991-06-06

Integrated Computer-Aided Manufacturing (ICAM) (20). In fact, IDEF 0 stands for ICAM Definition Method Zero . IDEF0 defines a subset of SA that omits...reasoning that has been programmed). An expert’s knowledge is specific to one problem domain as opposed to knowledge about general problem-solving...techniques. General problem domains are medicine, finance, science or engineering and so forth in which an expert can solve specific problems very well

Cognitive flexibility and undergraduate physiology students: increasing advanced knowledge acquisition within an ill-structured domain.

PubMed

Rhodes, Ashley E; Rozell, Timothy G

2017-09-01

Cognitive flexibility is defined as the ability to assimilate previously learned information and concepts to generate novel solutions to new problems. This skill is crucial for success within ill-structured domains such as biology, physiology, and medicine, where many concepts are simultaneously required for understanding a complex problem, yet the problem consists of patterns or combinations of concepts that are not consistently used or needed across all examples. To succeed within ill-structured domains, a student must possess a certain level of cognitive flexibility: rigid thought processes and prepackaged informational retrieval schemes relying on rote memorization will not suffice. In this study, we assessed the cognitive flexibility of undergraduate physiology students using a validated instrument entitled Student's Approaches to Learning (SAL). The SAL evaluates how deeply and in what way information is processed, as well as the investment of time and mental energy that a student is willing to expend by measuring constructs such as elaboration and memorization. Our results indicate that students who rely primarily on memorization when learning new information have a smaller knowledge base about physiological concepts, as measured by a prior knowledge assessment and unit exams. However, students who rely primarily on elaboration when learning new information have a more well-developed knowledge base about physiological concepts, which is displayed by higher scores on a prior knowledge assessment and increased performance on unit exams. Thus students with increased elaboration skills possibly possess a higher level of cognitive flexibility and are more likely to succeed within ill-structured domains. Copyright © 2017 the American Physiological Society.

The Knowledge Work of Professional Associations: Approaches to Standardisation and Forms of Legitimisation

ERIC Educational Resources Information Center

Nerland, Monika; Karseth, Berit

2015-01-01

This paper examines how professional associations engage themselves in efforts to develop, regulate and secure knowledge in their respective domains, with special emphasis on standardisation. The general emphasis on science in society brings renewed attention to the knowledge base of professionals, and positions professional bodies as key…

Widening the Knowledge Acquisition Bottleneck for Constraint-Based Tutors

ERIC Educational Resources Information Center

Suraweera, Pramuditha; Mitrovic, Antonija; Martin, Brent

2010-01-01

Intelligent Tutoring Systems (ITS) are effective tools for education. However, developing them is a labour-intensive and time-consuming process. A major share of the effort is devoted to acquiring the domain knowledge that underlies the system's intelligence. The goal of this research is to reduce this knowledge acquisition bottleneck and better…

Blurring the Boundaries between Vocational Education, Business and Research in the Agri-Food Domain

ERIC Educational Resources Information Center

Wals, Arjen E. J.; Lans, Thomas; Kupper, Hendrik

2012-01-01

This article discusses the emergence and significance of new knowledge configurations within the Dutch agri-food context. Knowledge configurations can be characterised as arrangements between VET and (often regional) partners in business and research aimed at improving knowledge transfer, circulation or co-creation. Based on a literature review…

Domain knowledge patterns in pedagogical diagnostics

NASA Astrophysics Data System (ADS)

Miarka, Rostislav

2017-07-01

This paper shows a proposal of representation of knowledge patterns in RDF(S) language. Knowledge patterns are used for reuse of knowledge. They can be divided into two groups - Top-level knowledge patterns and Domain knowledge patterns. Pedagogical diagnostics is aimed at testing of knowledge of students at primary and secondary school. An example of domain knowledge pattern from pedagogical diagnostics is part of this paper.

A Rules-Based Service for Suggesting Visualizations to Analyze Earth Science Phenomena.

NASA Astrophysics Data System (ADS)

Prabhu, A.; Zednik, S.; Fox, P. A.; Ramachandran, R.; Maskey, M.; Shie, C. L.; Shen, S.

2016-12-01

Current Earth Science Information Systems lack support for new or interdisciplinary researchers, who may be unfamiliar with the domain vocabulary or the breadth of relevant data available. We need to evolve the current information systems, to reduce the time required for data preparation, processing and analysis. This can be done by effectively salvaging the "dark" resources in Earth Science. We assert that Earth science metadata assets are dark resources, information resources that organizations collect, process, and store for regular business or operational activities but fail to utilize for other purposes. In order to effectively use these dark resources, especially for data processing and visualization, we need a combination of domain, data product and processing knowledge, i.e. a knowledge base from which specific data operations can be performed. In this presentation, we describe a semantic, rules based approach to provide i.e. a service to visualize Earth Science phenomena, based on the data variables extracted using the "dark" metadata resources. We use Jena rules to make assertions about compatibility between a phenomena and various visualizations based on multiple factors. We created separate orthogonal rulesets to map each of these factors to the various phenomena. Some of the factors we have considered include measurements, spatial resolution and time intervals. This approach enables easy additions and deletions based on newly obtained domain knowledge or phenomena related information and thus improving the accuracy of the rules service overall.

Knowledge-Based Reinforcement Learning for Data Mining

NASA Astrophysics Data System (ADS)

Kudenko, Daniel; Grzes, Marek

Data Mining is the process of extracting patterns from data. Two general avenues of research in the intersecting areas of agents and data mining can be distinguished. The first approach is concerned with mining an agent’s observation data in order to extract patterns, categorize environment states, and/or make predictions of future states. In this setting, data is normally available as a batch, and the agent’s actions and goals are often independent of the data mining task. The data collection is mainly considered as a side effect of the agent’s activities. Machine learning techniques applied in such situations fall into the class of supervised learning. In contrast, the second scenario occurs where an agent is actively performing the data mining, and is responsible for the data collection itself. For example, a mobile network agent is acquiring and processing data (where the acquisition may incur a certain cost), or a mobile sensor agent is moving in a (perhaps hostile) environment, collecting and processing sensor readings. In these settings, the tasks of the agent and the data mining are highly intertwined and interdependent (or even identical). Supervised learning is not a suitable technique for these cases. Reinforcement Learning (RL) enables an agent to learn from experience (in form of reward and punishment for explorative actions) and adapt to new situations, without a teacher. RL is an ideal learning technique for these data mining scenarios, because it fits the agent paradigm of continuous sensing and acting, and the RL agent is able to learn to make decisions on the sampling of the environment which provides the data. Nevertheless, RL still suffers from scalability problems, which have prevented its successful use in many complex real-world domains. The more complex the tasks, the longer it takes a reinforcement learning algorithm to converge to a good solution. For many real-world tasks, human expert knowledge is available. For example, human experts have developed heuristics that help them in planning and scheduling resources in their work place. However, this domain knowledge is often rough and incomplete. When the domain knowledge is used directly by an automated expert system, the solutions are often sub-optimal, due to the incompleteness of the knowledge, the uncertainty of environments, and the possibility to encounter unexpected situations. RL, on the other hand, can overcome the weaknesses of the heuristic domain knowledge and produce optimal solutions. In the talk we propose two techniques, which represent first steps in the area of knowledge-based RL (KBRL). The first technique [1] uses high-level STRIPS operator knowledge in reward shaping to focus the search for the optimal policy. Empirical results show that the plan-based reward shaping approach outperforms other RL techniques, including alternative manual and MDP-based reward shaping when it is used in its basic form. We showed that MDP-based reward shaping may fail and successful experiments with STRIPS-based shaping suggest modifications which can overcome encountered problems. The STRIPSbased method we propose allows expressing the same domain knowledge in a different way and the domain expert can choose whether to define an MDP or STRIPS planning task. We also evaluated the robustness of the proposed STRIPS-based technique to errors in the plan knowledge. In case that STRIPS knowledge is not available, we propose a second technique [2] that shapes the reward with hierarchical tile coding. Where the Q-function is represented with low-level tile coding, a V-function with coarser tile coding can be learned in parallel and used to approximate the potential for ground states. In the context of data mining, our KBRL approaches can also be used for any data collection task where the acquisition of data may incur considerable cost. In addition, observing the data collection agent in specific scenarios may lead to new insights into optimal data collection behaviour in the respective domains. In future work, we intend to demonstrate and evaluate our techniques on concrete real-world data mining applications.

A RULE-BASED SYSTEM FOR EVALUATING FINAL COVERS FOR HAZARDOUS WASTE LANDFILLS

EPA Science Inventory

This chapter examines how rules are used as a knowledge representation formalism in the domain of hazardous waste management. A specific example from this domain involves performance evaluation of final covers used to close hazardous waste landfills. Final cover design and associ...

A Technological Pedagogical Content Knowledge Based Instructional Design Model: A Third Version Implementation Study in a Technology Integration Course

ERIC Educational Resources Information Center

Lee, Chia-Jung; Kim, ChanMin

2017-01-01

This paper presents the third version of a technological pedagogical content knowledge (TPACK) based instructional design model that incorporates the distinctive, transformative, and integrative views of TPACK into a comprehensive actionable framework. Strategies of relating TPACK domains to real-life learning experiences, role-playing, and…

Individual Differences in Learning Entrepreneurship and Their Implications for Web-Based Instruction in E-Business and E-Commerce.

ERIC Educational Resources Information Center

Foster, Jonathan; Lin, Angela

2003-01-01

Discusses results from a survey of graduates following a module in e-business and e-commerce at the University of Sheffield that suggest differences in prior knowledge and cultural background impact students' acquisition of domain knowledge and intellectual and information research skills. Considers implications for Web-based instruction.…

Argumentation to Foster Pre-Service Science Teachers' Knowledge, Competency, and Attitude on the Domains of Chemical Literacy of Acids and Bases

ERIC Educational Resources Information Center

Cigdemoglu, C.; Arslan, H. O.; Cam, A.

2017-01-01

Argumentative practices have the potential to contribute to scientific literacy. However, these practices are not widely incorporated in science classrooms and so their effect on the domains of literacy is still not revealed. Therefore, this study proposes to reveal the effect of argumentation on the three domains of chemical literacy related to…

Online Knowledge-Based Model for Big Data Topic Extraction.

PubMed

Khan, Muhammad Taimoor; Durrani, Mehr; Khalid, Shehzad; Aziz, Furqan

2016-01-01

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems.

PubMed

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A

2017-03-01

The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

Liberal Entity Extraction: Rapid Construction of Fine-Grained Entity Typing Systems

PubMed Central

Huang, Lifu; May, Jonathan; Pan, Xiaoman; Ji, Heng; Ren, Xiang; Han, Jiawei; Zhao, Lin; Hendler, James A.

2017-01-01

Abstract The ability of automatically recognizing and typing entities in natural language without prior knowledge (e.g., predefined entity types) is a major challenge in processing such data. Most existing entity typing systems are limited to certain domains, genres, and languages. In this article, we propose a novel unsupervised entity-typing framework by combining symbolic and distributional semantics. We start from learning three types of representations for each entity mention: general semantic representation, specific context representation, and knowledge representation based on knowledge bases. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representations. This framework does not rely on any annotated data, predefined typing schema, or handcrafted features; therefore, it can be quickly adapted to a new domain, genre, and/or language. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework. PMID:28328252

Young Children Bet On Their Numerical Skills: Metacognition in the Numerical Domain

PubMed Central

Vo, Vy A.; Li, Rosa; Kornell, Nate; Pouget, Alexandre; Cantlon, Jessica F.

2014-01-01

Metacognition, the ability to assess one’s own knowledge, has been targeted as a critical learning mechanism in mathematics education. Yet, the early childhood origins of metacognition have proven difficult to study. Using a novel nonverbal task and a comprehensive set of metacognitive measures, we provide the strongest evidence to date that young children are metacognitive. We show that children as young as 5 years make metacognitive “bets” on their numerical discriminations in a wagering task. However, contrary to previous reports from adults, children’s metacognition proved to be domain-specific: children’s metacognition in the numerical domain was unrelated to their metacognition in another domain (emotion discrimination). Moreover, children’s metacognitive ability in only the numerical domain predicted their school-based mathematics knowledge. The data provide novel evidence that metacognition is a fundamental, domain-dependent cognitive ability in children. The findings have implications for theories of uncertainty and reveal new avenues for training metacognition in children. PMID:24973137

a Comparison of Two Strategies for Avoiding Negative Transfer in Domain Adaptation Based on Logistic Regression

NASA Astrophysics Data System (ADS)

Paul, A.; Vogt, K.; Rottensteiner, F.; Ostermann, J.; Heipke, C.

2018-05-01

In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are available, it is inherently difficult to find a robust measure of relatedness between the source and target domains. This is of crucial importance for the performance of TL, because the knowledge transfer between unrelated data may lead to negative transfer, i.e. to a decrease of classification performance after transfer. We address the problem of measuring the relatedness between source and target datasets and investigate three different strategies to predict and, consequently, to avoid negative transfer in this paper. The first strategy is based on circular validation. The second strategy relies on the Maximum Mean Discrepancy (MMD) similarity metric, whereas the third one is an extension of MMD which incorporates the knowledge about the class labels in the source domain. Our method is evaluated using two different benchmark datasets. The experiments highlight the strengths and weaknesses of the investigated methods. We also show that it is possible to reduce the amount of negative transfer using these strategies for a TL method and to generate a consistent performance improvement over the whole dataset.

Knowledge-acquisition tools for medical knowledge-based systems.

PubMed

Lanzola, G; Quaglini, S; Stefanelli, M

1995-03-01

Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

Building a knowledge base of severe adverse drug events based on AERS reporting data using semantic web technologies.

PubMed

Jiang, Guoqian; Wang, Liwei; Liu, Hongfang; Solbrig, Harold R; Chute, Christopher G

2013-01-01

A semantically coded knowledge base of adverse drug events (ADEs) with severity information is critical for clinical decision support systems and translational research applications. However it remains challenging to measure and identify the severity information of ADEs. The objective of the study is to develop and evaluate a semantic web based approach for building a knowledge base of severe ADEs based on the FDA Adverse Event Reporting System (AERS) reporting data. We utilized a normalized AERS reporting dataset and extracted putative drug-ADE pairs and their associated outcome codes in the domain of cardiac disorders. We validated the drug-ADE associations using ADE datasets from SIDe Effect Resource (SIDER) and the UMLS. We leveraged the Common Terminology Criteria for Adverse Event (CTCAE) grading system and classified the ADEs into the CTCAE in the Web Ontology Language (OWL). We identified and validated 2,444 unique Drug-ADE pairs in the domain of cardiac disorders, of which 760 pairs are in Grade 5, 775 pairs in Grade 4 and 2,196 pairs in Grade 3.

Semantics-informed geological maps: Conceptual modeling and knowledge encoding

NASA Astrophysics Data System (ADS)

Lombardo, Vincenzo; Piana, Fabrizio; Mimmo, Dario

2018-07-01

This paper introduces a novel, semantics-informed geologic mapping process, whose application domain is the production of a synthetic geologic map of a large administrative region. A number of approaches concerning the expression of geologic knowledge through UML schemata and ontologies have been around for more than a decade. These approaches have yielded resources that concern specific domains, such as, e.g., lithology. We develop a conceptual model that aims at building a digital encoding of several domains of geologic knowledge, in order to support the interoperability of the sources. We apply the devised terminological base to the classification of the elements of a geologic map of the Italian Western Alps and northern Apennines (Piemonte region). The digitally encoded knowledge base is a merged set of ontologies, called OntoGeonous. The encoding process identifies the objects of the semantic encoding, the geologic units, gathers the relevant information about such objects from authoritative resources, such as GeoSciML (giving priority to the application schemata reported in the INSPIRE Encoding Cookbook), and expresses the statements by means of axioms encoded in the Web Ontology Language (OWL). To support interoperability, OntoGeonous interlinks the general concepts by referring to the upper part level of ontology SWEET (developed by NASA), and imports knowledge that is already encoded in ontological format (e.g., ontology Simple Lithology). Machine-readable knowledge allows for consistency checking and for classification of the geological map data through algorithms of automatic reasoning.

The relationship between immediate relevant basic science knowledge and clinical knowledge: physiology knowledge and transthoracic echocardiography image interpretation.

PubMed

Nielsen, Dorte Guldbrand; Gotzsche, Ole; Sonne, Ole; Eika, Berit

2012-10-01

Two major views on the relationship between basic science knowledge and clinical knowledge stand out; the Two-world view seeing basic science and clinical science as two separate knowledge bases and the encapsulated knowledge view stating that basic science knowledge plays an overt role being encapsulated in the clinical knowledge. However, resent research has implied that a more complex relationship between the two knowledge bases exists. In this study, we explore the relationship between immediate relevant basic science (physiology) and clinical knowledge within a specific domain of medicine (echocardiography). Twenty eight medical students in their 3rd year and 45 physicians (15 interns, 15 cardiology residents and 15 cardiology consultants) took a multiple-choice test of physiology knowledge. The physicians also viewed images of a transthoracic echocardiography (TTE) examination and completed a checklist of possible pathologies found. A total score for each participant was calculated for the physiology test, and for all physicians also for the TTE checklist. Consultants scored significantly higher on the physiology test than did medical students and interns. A significant correlation between physiology test scores and TTE checklist scores was found for the cardiology residents only. Basic science knowledge of immediate relevance for daily clinical work expands with increased work experience within a specific domain. Consultants showed no relationship between physiology knowledge and TTE interpretation indicating that experts do not use basic science knowledge in routine daily practice, but knowledge of immediate relevance remains ready for use.

Indexing Guidelines: Applications in Use of Pulmonary Artery Catheters and Pressure Ulcer Prevention

PubMed Central

Jenders, Robert A.; Estey, Greg; Martin, Martha; Hamilton, Glenys; Ford-Carleton, Penny; Thompson, B. Taylor; Oliver, Diane E.; Eccles, Randy; Barnett, G. Octo; Zielstorff, Rita D.; Fitzmaurice, Joan B.

1994-01-01

In a busy clinical environment, access to knowledge must be rapid and specific to the clinical query at hand. This requires indices which support easy navigation within a knowledge source. We have developed a computer-based tool for trouble-shooting pulmonary artery waveforms using a graphical index. Preliminary results of domain knowledge tests for a group of clinicians exposed to the system (N=33) show a mean improvement on a 30-point test of 5.33 (p<0.001) compared to a control group (N=19) improvement of 0.47 (p=0.61). Survey of the experimental group (N=25) showed 84% (p=0.001) found the system easy to use. We discuss lessons learned in indexing this domain area to computer-based indexing of guidelines for pressure ulcer prevention. PMID:7950035

Factors That Influence the Difficulty of Science Words

ERIC Educational Resources Information Center

Cervetti, Gina N.; Hiebert, Elfrieda H.; Pearson, P. David; McClung, Nicola A.

2015-01-01

This study examines, within the domain of science, the characteristics of words that predict word knowledge and word learning. The authors identified a set of word characteristics--length, part of speech, polysemy, frequency, morphological frequency, domain specificity, and concreteness--that, based on earlier research, were prime candidates to…

Designing Project-Based Instruction to Foster Generative and Mechanistic Understandings in Genetics

ERIC Educational Resources Information Center

Duncan, Ravit Golan; Tseng, Katie Ann

2011-01-01

The acquisition of scientific knowledge is fraught with difficulties and challenges for the learner. The very nature of some scientific domains contributes to the learning difficulties students' experience. Phenomena in these domains are composed of multiple organization levels featuring complicated interactions within and across these levels.…

The use and generation of illustrative examples in computer-based instructional systems

NASA Technical Reports Server (NTRS)

Selig, William John; Johannes, James D.

1987-01-01

A method is proposed whereby the underlying domain knowledge is represented such that illustrative examples may be generated on demand. This method has the advantage that the generated example can follow changes in the domain in addition to allowing automatic customization of the example to the individual.

Mental Constructions and Constructions of Web Sites: Learner and Teacher Points of View

ERIC Educational Resources Information Center

Hazzan, Orit

2004-01-01

This research focuses on knowledge and ways in which knowledge may be constructed in the learner's mind. Specifically, it addresses the Web as a cognitive supporter for learning, organising and constructing a new domain of knowledge. In particular, the research analyses student reflection on constructing web sites. The analysis is based on an…

Forecasting Doctoral-Level Content in Agricultural Education: Viewpoints of Engaged Scholars in the United States

ERIC Educational Resources Information Center

Shinn, Glen C.; Briers, Gary; Baker, Matt

2008-01-01

In this study, the researchers used a classical Delphi method to re-examine the conceptual framework, definition, and knowledge base of the field. Seventeen engaged scholars, each representing the expert agricultural education community, reached consensus on defining the field of study, 10 knowledge domains, and 67 knowledge objects. The Delphi…

A Step towards a Sharable Community Knowledge Base for WRF Settings -Developing a WRF Setting Methodology based on a case study in a Torrential Rainfall Event

NASA Astrophysics Data System (ADS)

CHU, Q.; Xu, Z.; Zhuo, L.; Han, D.

2016-12-01

Increased requirements for interactions between different disciplines and readily access to the numerical weather forecasting system featured with portability and extensibility have made useful contribution to the increases of downstream model users in WRF over recent years. For these users, a knowledge base classified by the representative events would be much helpful. This is because the determination of model settings is regarded as the most important steps in WRF. However, such a process is generally time-consuming, even if with a high computational platform. As such, we propose a sharable proper lookup table on WRF domain settings and corresponding procedures based on a representative torrential rainfall event in Beijing, China. It has been found that WRF's simulations' drift away from the input lateral boundary conditions can be significantly reduced with the adjustment of the domain settings. Among all the impact factors, the placement of nested domain can not only affect the moving speed and angle of the storm-center, but also the location and amount of heavy-rain-belt which can only be detected with adjusted spatial resolutions. Spin-up time is also considered in the model settings, which is demonstrated to have the most obvious influence on the accuracy of the simulations. This conclusion is made based on the large diversity of spatial distributions of precipitation, in terms of the amount of heavy rain varied from -30% to 58% among each experiment. After following all the procedures, the variations of domain settings have minimal effect on the modeling and show the best correlation (larger than 0.65) with fusion observations. So the model settings, including domain size covering the greater Beijing area, 1:5:5 downscaling ratio, 57 vertical levels with top of 50hpa and 60h spin-up time, are found suitable for predicting the similar convective torrential rainfall event in Beijing area. We hope that the procedure for building the community WRF knowledge base in this paper would be helpful to peer-researchers and operational communities by saving them from repeating each other's work. More importantly, the results by studying different events and locations could enrich this community knowledge base to benefit WRF users around the world in the future.

Developing VISO: Vaccine Information Statement Ontology for patient education.

PubMed

Amith, Muhammad; Gong, Yang; Cunningham, Rachel; Boom, Julie; Tao, Cui

2015-01-01

To construct a comprehensive vaccine information ontology that can support personal health information applications using patient-consumer lexicon, and lead to outcomes that can improve patient education. The authors composed the Vaccine Information Statement Ontology (VISO) using the web ontology language (OWL). We started with 6 Vaccine Information Statement (VIS) documents collected from the Centers for Disease Control and Prevention (CDC) website. Important and relevant selections from the documents were recorded, and knowledge triples were derived. Based on the collection of knowledge triples, the meta-level formalization of the vaccine information domain was developed. Relevant instances and their relationships were created to represent vaccine domain knowledge. The initial iteration of the VISO was realized, based on the 6 Vaccine Information Statements and coded into OWL2 with Protégé. The ontology consisted of 132 concepts (classes and subclasses) with 33 types of relationships between the concepts. The total number of instances from classes totaled at 460, along with 429 knowledge triples in total. Semiotic-based metric scoring was applied to evaluate quality of the ontology.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

PubMed

Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

2016-06-01

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

A Method for Populating the Knowledge Base of AFIT’s Domain-Oriented Application Composition System

DTIC Science & Technology

1993-12-01

Analysis ( FODA ). The approach identifies prominent features (similarities) and distinctive features (differences) of software systems within an... analysis approaches we have summarized, the re- searchers described FODA in sufficient detail to use on large domain analysis projects (ones with...Software Technology Center, July 1991. 18. Kang, Kyo C. and others. Feature-Oriented Domain Analysis ( FODA ) Feasibility Study. Technical Report, Software

A flight expert system for on-board fault monitoring and diagnosis

NASA Technical Reports Server (NTRS)

Ali, Moonis

1990-01-01

An architecture for a flight expert system (FLES) to assist pilots in monitoring, diagnosing, and recovering from inflight faults is described. A prototype was implemented and an attempt was made to automate the knowledge acquisition process by employing a learning by being told methodology. The scope of acquired knowledge ranges from domain knowledge, including the information about objects and their relationships, to the procedural knowledge associated with the functionality of the mechanisms. AKAS (automatic knowledge acquisition system) is the constructed prototype for demonstration proof of concept, in which the expert directly interfaces with the knowledge acquisition system to ultimately construct the knowledge base for the particular application. The expert talks directly to the system using a natural language restricted only by the extent of the definitions in an analyzer dictionary, i.e., the interface understands a subset of concepts related to a given domain. In this case, the domain is the electrical system of the Boeing 737. Efforts were made to define and employ heuristics as well as algorithmic rules to conceptualize data produced by normal and faulty jet engine behavior examples. These rules were employed in developing the machine learning system (MLS). The input to MLS is examples which contain data of normal and faulty engine behavior and which are obtained from an engine simulation program. MLS first transforms the data into discrete selectors. Partial descriptions formed by those selectors are then generalized or specialized to generate concept descriptions about faults. The concepts are represented in the form of characteristic and discriminant descriptions, which are stored in the knowledge base and are employed to diagnose faults. MLS was successfully tested on jet engine examples.

Predicate Oriented Pattern Analysis for Biomedical Knowledge Discovery

PubMed Central

Shen, Feichen; Liu, Hongfang; Sohn, Sunghwan; Larson, David W.; Lee, Yugyung

2017-01-01

In the current biomedical data movement, numerous efforts have been made to convert and normalize a large number of traditional structured and unstructured data (e.g., EHRs, reports) to semi-structured data (e.g., RDF, OWL). With the increasing number of semi-structured data coming into the biomedical community, data integration and knowledge discovery from heterogeneous domains become important research problem. In the application level, detection of related concepts among medical ontologies is an important goal of life science research. It is more crucial to figure out how different concepts are related within a single ontology or across multiple ontologies by analysing predicates in different knowledge bases. However, the world today is one of information explosion, and it is extremely difficult for biomedical researchers to find existing or potential predicates to perform linking among cross domain concepts without any support from schema pattern analysis. Therefore, there is a need for a mechanism to do predicate oriented pattern analysis to partition heterogeneous ontologies into closer small topics and do query generation to discover cross domain knowledge from each topic. In this paper, we present such a model that predicates oriented pattern analysis based on their close relationship and generates a similarity matrix. Based on this similarity matrix, we apply an innovated unsupervised learning algorithm to partition large data sets into smaller and closer topics and generate meaningful queries to fully discover knowledge over a set of interlinked data sources. We have implemented a prototype system named BmQGen and evaluate the proposed model with colorectal surgical cohort from the Mayo Clinic. PMID:28983419

An architecture for intelligent task interruption

NASA Technical Reports Server (NTRS)

Sharma, D. D.; Narayan, Srini

1990-01-01

In the design of real time systems the capability for task interruption is often considered essential. The problem of task interruption in knowledge-based domains is examined. It is proposed that task interruption can be often avoided by using appropriate functional architectures and knowledge engineering principles. Situations for which task interruption is indispensable, a preliminary architecture based on priority hierarchies is described.

A Cognitive Simulator for Learning the Nature of Human Problem Solving

NASA Astrophysics Data System (ADS)

Miwa, Kazuhisa

Problem solving is understood as a process through which states of problem solving are transferred from the initial state to the goal state by applying adequate operators. Within this framework, knowledge and strategies are given as operators for the search. One of the most important points of researchers' interest in the domain of problem solving is to explain the performance of problem solving behavior based on the knowledge and strategies that the problem solver has. We call the interplay between problem solvers' knowledge/strategies and their behavior the causal relation between mental operations and behavior. It is crucially important, we believe, for novice learners in this domain to understand the causal relation between mental operations and behavior. Based on this insight, we have constructed a learning system in which learners can control mental operations of a computational agent that solves a task, such as knowledge, heuristics, and cognitive capacity, and can observe its behavior. We also introduce this system to a university class, and discuss which findings were discovered by the participants.

Evaluation of an interactive electronic health education tool in rural Afghanistan.

PubMed

Kim, Glen; Griffin, Suzanne; Nadem, Hedeyat; Aria, Jawad; Lawry, Lynn

2008-01-01

Low education levels may limit community-based health worker (CHW) efforts in rural Afghanistan. In 2004, LeapFrog Enterprises and the United States Department of Health and Human Services developed the Afghan Family Health Book (AFHB), an interactive, electronic picture book, to communicate public health messages in rural Afghanistan. Changes in health knowledge among households exposed to the AFHB vs. CHWs were compared. From January-June 2005, baseline and follow-up panel surveys were administered in Pashto-speaking Laghman and Dari-speaking Kabul provinces. Within each province, an AFHB and a CHW district were randomly sampled using a stratified, 2-staged cluster sample design (total 98 clusters and 3,372 households). Surveys tested knowledge of 17 health domains at baseline and on follow-up at three months. For each domain, multivariate logistic regression was used to assess the effect of the AFHB on followup pass rates, controlling for demographics and differences in baseline knowledge. Both AFHB and CHW resulted in statistically significant changes in pass rates on follow-up, although there were greater gains among AFHB users for five domains among Pashto-speakers (micronutrients, malaria, sexually transmitted diseases, postpartum care, and breast-feeding) and seven domains among Dari-speakers (diet, malaria, mental health, birth-spacing, and prenatal/neonatal/postpartum care). Community-based health workers effected greater knowledge gains only for the Dari breast-feeding module. Participants favored CHW over the AFHB, which they found poorly translated and difficult to use. The AFHB has potential to improve public health knowledge among rural Afghans. Future efforts may benefit from involvement of local health agencies and the integration of interactive technology with traditional CHW approaches.

A formal approach to validation and verification for knowledge-based control systems

NASA Technical Reports Server (NTRS)

Castore, Glen

1987-01-01

As control systems become more complex in response to desires for greater system flexibility, performance and reliability, the promise is held out that artificial intelligence might provide the means for building such systems. An obstacle to the use of symbolic processing constructs in this domain is the need for verification and validation (V and V) of the systems. Techniques currently in use do not seem appropriate for knowledge-based software. An outline of a formal approach to V and V for knowledge-based control systems is presented.

SWITCH user's manual

NASA Technical Reports Server (NTRS)

1987-01-01

The planning program, SWITCH, and its surrounding changed-goal-replanning program, Runaround, are described. The evolution of SWITCH and Runaround from an earlier planner, DEVISER, is recounted. SWITCH's plan representation, and its process of building a plan by backward chaining with strict chronological backtracking, are described. A guide for writing knowledge base files is provided, as are narrative guides for installing the program, running it, and interacting with it while it is running. Some utility functions are documented. For the sake of completeness, a narrative guide to the experimental discrepancy-replanning feature is provided. Appendices contain knowledge base files for a blocksworld domain, and a DRIBBLE file illustrating the output from, and user interaction with, the program in that domain.

An information theory analysis of spatial decisions in cognitive development

PubMed Central

Scott, Nicole M.; Sera, Maria D.; Georgopoulos, Apostolos P.

2015-01-01

Performance in a cognitive task can be considered as the outcome of a decision-making process operating across various knowledge domains or aspects of a single domain. Therefore, an analysis of these decisions in various tasks can shed light on the interplay and integration of these domains (or elements within a single domain) as they are associated with specific task characteristics. In this study, we applied an information theoretic approach to assess quantitatively the gain of knowledge across various elements of the cognitive domain of spatial, relational knowledge, as a function of development. Specifically, we examined changing spatial relational knowledge from ages 5 to 10 years. Our analyses consisted of a two-step process. First, we performed a hierarchical clustering analysis on the decisions made in 16 different tasks of spatial relational knowledge to determine which tasks were performed similarly at each age group as well as to discover how the tasks clustered together. We next used two measures of entropy to capture the gradual emergence of order in the development of relational knowledge. These measures of “cognitive entropy” were defined based on two independent aspects of chunking, namely (1) the number of clusters formed at each age group, and (2) the distribution of tasks across the clusters. We found that both measures of entropy decreased with age in a quadratic fashion and were positively and linearly correlated. The decrease in entropy and, therefore, gain of information during development was accompanied by improved performance. These results document, for the first time, the orderly and progressively structured “chunking” of decisions across the development of spatial relational reasoning and quantify this gain within a formal information-theoretic framework. PMID:25698915

Information Extraction Using Controlled English to Support Knowledge-Sharing and Decision-Making

DTIC Science & Technology

2012-06-01

or language variants. CE-based information extraction will greatly facilitate the processes in the cognitive and social domains that enable forces...terminology or language variants. CE-based information extraction will greatly facilitate the processes in the cognitive and social domains that...processor is run to turn the atomic CE into a more “ stylistically felicitous” CE, using techniques such as: aggregating all information about an entity

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).

Adult Age Differences in Knowledge-Driven Reading

ERIC Educational Resources Information Center

Miller, Lisa M. Soederberg; Stine-Morrow, Elizabeth A. L.; Kirkorian, Heather L.; Conroy, Michelle L.

2004-01-01

The authors investigated the effects of domain knowledge on online reading among younger and older adults. Individuals were randomly assigned to either a domain-relevant (i.e., high-knowledge) or domain-irrelevant (i.e., low-knowledge) training condition. Two days later, participants read target passages on a computer that drew on information…

Semantically-based priors and nuanced knowledge core for Big Data, Social AI, and language understanding.

PubMed

Olsher, Daniel

2014-10-01

Noise-resistant and nuanced, COGBASE makes 10 million pieces of commonsense data and a host of novel reasoning algorithms available via a family of semantically-driven prior probability distributions. Machine learning, Big Data, natural language understanding/processing, and social AI can draw on COGBASE to determine lexical semantics, infer goals and interests, simulate emotion and affect, calculate document gists and topic models, and link commonsense knowledge to domain models and social, spatial, cultural, and psychological data. COGBASE is especially ideal for social Big Data, which tends to involve highly implicit contexts, cognitive artifacts, difficult-to-parse texts, and deep domain knowledge dependencies. Copyright © 2014 Elsevier Ltd. All rights reserved.

A Proposal to Develop Interactive Classification Technology

NASA Technical Reports Server (NTRS)

deBessonet, Cary

1998-01-01

Research for the first year was oriented towards: 1) the design of an interactive classification tool (ICT); and 2) the development of an appropriate theory of inference for use in ICT technology. The general objective was to develop a theory of classification that could accommodate a diverse array of objects, including events and their constituent objects. Throughout this report, the term "object" is to be interpreted in a broad sense to cover any kind of object, including living beings, non-living physical things, events, even ideas and concepts. The idea was to produce a theory that could serve as the uniting fabric of a base technology capable of being implemented in a variety of automated systems. The decision was made to employ two technologies under development by the principal investigator, namely, SMS (Symbolic Manipulation System) and SL (Symbolic Language) [see debessonet, 1991, for detailed descriptions of SMS and SL]. The plan was to enhance and modify these technologies for use in an ICT environment. As a means of giving focus and direction to the proposed research, the investigators decided to design an interactive, classificatory tool for use in building accessible knowledge bases for selected domains. Accordingly, the proposed research was divisible into tasks that included: 1) the design of technology for classifying domain objects and for building knowledge bases from the results automatically; 2) the development of a scheme of inference capable of drawing upon previously processed classificatory schemes and knowledge bases; and 3) the design of a query/ search module for accessing the knowledge bases built by the inclusive system. The interactive tool for classifying domain objects was to be designed initially for textual corpora with a view to having the technology eventually be used in robots to build sentential knowledge bases that would be supported by inference engines specially designed for the natural or man-made environments in which the robots would be called upon to operate.

Development and validation of the Evidence Based Medicine Questionnaire (EBMQ) to assess doctors' knowledge, practice and barriers regarding the implementation of evidence-based medicine in primary care.

PubMed

Hisham, Ranita; Ng, Chirk Jenn; Liew, Su May; Lai, Pauline Siew Mei; Chia, Yook Chin; Khoo, Ee Ming; Hanafi, Nik Sherina; Othman, Sajaratulnisah; Lee, Ping Yein; Abdullah, Khatijah Lim; Chinna, Karuthan

2018-06-23

Evidence-Based Medicine (EBM) integrates best available evidence from literature and patients' values, which then informs clinical decision making. However, there is a lack of validated instruments to assess the knowledge, practice and barriers of primary care physicians in the implementation of EBM. This study aimed to develop and validate an Evidence-Based Medicine Questionnaire (EBMQ) in Malaysia. The EBMQ was developed based on a qualitative study, literature review and an expert panel. Face and content validity was verified by the expert panel and piloted among 10 participants. Primary care physicians with or without EBM training who could understand English were recruited from December 2015 to January 2016. The EBMQ was administered at baseline and two weeks later. A higher score indicates better knowledge, better practice of EBM and less barriers towards the implementation of EBM. We hypothesized that the EBMQ would have three domains: knowledge, practice and barriers. The final version of the EBMQ consists of 80 items: 62 items were measured on a nominal scale, 22 items were measured on a 5 point Likert-scale. Flesch reading ease was 61.2. A total of 343 participants were approached; of whom 320 agreed to participate (response rate = 93.2%). Factor analysis revealed that the EBMQ had eight domains after 13 items were removed: "EBM websites", "evidence-based journals", "types of studies", "terms related to EBM", "practice", "access", "patient preferences" and "support". Cronbach alpha for the overall EBMQ was 0.909, whilst the Cronbach alpha for the individual domain ranged from 0.657-0.940. The EBMQ was able to discriminate between doctors with and without EBM training for 24 out of 42 items. At test-retest, kappa values ranged from 0.155 to 0.620. The EBMQ was found to be a valid and reliable instrument to assess the knowledge, practice and barriers towards the implementation of EBM among primary care physicians in Malaysia.

Effects of Working Memory Capacity and Domain Knowledge on Recall for Grocery Prices.

PubMed

Bermingham, Douglas; Gardner, Michael K; Woltz, Dan J

2016-01-01

Hambrick and Engle (2002) proposed 3 models of how domain knowledge and working memory capacity may work together to influence episodic memory: a "rich-get-richer" model, a "building blocks" model, and a "compensatory" model. Their results supported the rich-get-richer model, although later work by Hambrick and Oswald (2005) found support for a building blocks model. We investigated the effects of domain knowledge and working memory on recall of studied grocery prices. Working memory was measured with 3 simple span tasks. A contrast of realistic versus fictitious foods in the episodic memory task served as our manipulation of domain knowledge, because participants could not have domain knowledge of fictitious food prices. There was a strong effect for domain knowledge (realistic food-price pairs were easier to remember) and a moderate effect for working memory capacity (higher working memory capacity produced better recall). Furthermore, the interaction between domain knowledge and working memory produced a small but significant interaction in 1 measure of price recall. This supported the compensatory model and stands in contrast to previous research.

Automatic acquisition of domain and procedural knowledge

NASA Technical Reports Server (NTRS)

Ferber, H. J.; Ali, M.

1988-01-01

The design concept and performance of AKAS, an automated knowledge-acquisition system for the development of expert systems, are discussed. AKAS was developed using the FLES knowledge base for the electrical system of the B-737 aircraft and employs a 'learn by being told' strategy. The system comprises four basic modules, a system administration module, a natural-language concept-comprehension module, a knowledge-classification/extraction module, and a knowledge-incorporation module; details of the module architectures are explored.

Online Knowledge-Based Model for Big Data Topic Extraction

PubMed Central

Khan, Muhammad Taimoor; Durrani, Mehr; Khalid, Shehzad; Aziz, Furqan

2016-01-01

Lifelong machine learning (LML) models learn with experience maintaining a knowledge-base, without user intervention. Unlike traditional single-domain models they can easily scale up to explore big data. The existing LML models have high data dependency, consume more resources, and do not support streaming data. This paper proposes online LML model (OAMC) to support streaming data with reduced data dependency. With engineering the knowledge-base and introducing new knowledge features the learning pattern of the model is improved for data arriving in pieces. OAMC improves accuracy as topic coherence by 7% for streaming data while reducing the processing cost to half. PMID:27195004

Teaching Thinking and Problem Solving.

ERIC Educational Resources Information Center

Bransford, John; And Others

1986-01-01

This article focuses on two approaches to teaching reasoning and problem solving. One emphasizes the role of domain-specific knowledge; the other emphasizes general strategic and metacognitive knowledge. Many instructional programs are based on the latter approach. The article concludes that these programs can be strengthened by focusing on domain…

Automatic Generation of Tests from Domain and Multimedia Ontologies

ERIC Educational Resources Information Center

Papasalouros, Andreas; Kotis, Konstantinos; Kanaris, Konstantinos

2011-01-01

The aim of this article is to present an approach for generating tests in an automatic way. Although other methods have been already reported in the literature, the proposed approach is based on ontologies, representing both domain and multimedia knowledge. The article also reports on a prototype implementation of this approach, which…

Model compilation: An approach to automated model derivation

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Baudin, Catherine; Iwasaki, Yumi; Nayak, Pandurang; Tanaka, Kazuo

1990-01-01

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration.

Development of theory-based knowledge translation interventions to facilitate the implementation of evidence-based guidelines on the early management of adults with traumatic spinal cord injury.

PubMed

Bérubé, Mélanie; Albert, Martin; Chauny, Jean-Marc; Contandriopoulos, Damien; DuSablon, Anne; Lacroix, Sébastien; Gagné, Annick; Laflamme, Élise; Boutin, Nathalie; Delisle, Stéphane; Pauzé, Anne-Marie; MacThiong, Jean-Marc

2015-12-01

Optimal, early management following a spinal cord injury (SCI) can limit individuals' disabilities and costs related to their care. Several knowledge syntheses were recently published to guide health care professionals with regard to early interventions in SCI patients. However, no knowledge translation (KT) intervention, selected according to a behaviour change theory, has been proposed to facilitate the use of SCI guidelines in an acute care setting. To develop theory-informed KT interventions to promote the application of evidence-based recommendations on the acute care management of SCI patients. The first four phases of the knowledge-to-action model were used to establish the study design. Knowledge selection was based on the Grading of Recommendations Assessment, Development and Evaluation system. Knowledge adaptation to the local context was sourced from the ADAPTE process. The theoretical domains framework oriented the selection and development of the interventions based on an assessment of barriers and enablers to knowledge application. Twenty-nine recommendations were chosen and operationalized in measurable clinical indicators. Barriers related to knowledge, skills, perceived capacities, beliefs about consequences, social influences, and the environmental context and resources theoretical domains were identified. The mapping of behaviour change techniques associated with those barriers led to the development of an online educational curriculum, interdisciplinary clinical pathways as well as policies and procedures. This research project allowed us developing KT interventions according to a thorough behavioural change methodology. Exposure to the generated interventions will support health care professionals in providing the best care to SCI patients. © 2015 John Wiley & Sons, Ltd.

Speechlinks: Robust Cross-Lingual Tactical Communication Aids

DTIC Science & Technology

2008-06-01

domain, the ontology based translation has proven to be challenging to build in this domain, however recent developments show promising results...assignments, and the effect of domain knowledge on those requirements. • Improving the front end of the speech recognizer remains one of the most challenging ...users by being very selective. 4.2.3.2 Analysis of the Normal user type inference result Figure 4.11 shows one of the most challenging users to

Issues on the use of meta-knowledge in expert systems

NASA Technical Reports Server (NTRS)

Facemire, Jon; Chen, Imao

1988-01-01

Meta knowledge is knowledge about knowledge; knowledge that is not domain specific but is concerned instead with its own internal structure. Several past systems have used meta-knowledge to improve the nature of the user interface, to maintain the knowledge base, and to control the inference engine. More extensive use of meta-knowledge is probable for the future as larger scale problems are considered. A proposed system architecture is presented and discussed in terms of meta-knowledge applications. The principle components of this system: the user support subsystem, the control structure, the knowledge base, the inference engine, and a learning facility are all outlined and discussed in light of the use of meta-knowledge. Problems with meta-constructs are also mentioned but it is concluded that the use of meta-knowledge is crucial for increasingly autonomous operations.

Realtime Knowledge Management (RKM): From an International Space Station (ISS) Point of View

NASA Technical Reports Server (NTRS)

Robinson, Peter I.; McDermott, William; Alena, Richard L.

2004-01-01

We are developing automated methods to provide realtime access to spacecraft domain knowledge relevant a spacecraft's current operational state. The method is based upon analyzing state-transition signatures in the telemetry stream. A key insight is that documentation relevant to a specific failure mode or operational state is related to the structure and function of spacecraft systems. This means that diagnostic dependency and state models can provide a roadmap for effective documentation navigation and presentation. Diagnostic models consume the telemetry and derive a high-level state description of the spacecraft. Each potential spacecraft state description is matched against the predictions of models that were developed from information found in the pages and sections in the relevant International Space Station (ISS) documentation and reference materials. By annotating each model fragment with the domain knowledge sources from which it was derived we can develop a system that automatically selects those documents representing the domain knowledge encapsulated by the models that compute the current spacecraft state. In this manner, when the spacecraft state changes, the relevant documentation context and presentation will also change.

The Design of Computerized Practice Fields for Problem Solving and Contextualized Transfer

ERIC Educational Resources Information Center

Riedel, Jens; Fitzgerald, Gail; Leven, Franz; Toenshoff, Burkhard

2003-01-01

Current theories of learning emphasize the importance of learner-centered, active, authentic, environments for meaningful knowledge construction. From this perspective, computerized case-based learning systems afford practice fields for learners to build domain knowledge and problem-solving skills and to support contextualized transfer of…

The Impact of New Learning Environments in an Engineering Design Course

ERIC Educational Resources Information Center

Dinsmore, Daniel L.; Alexander, Patricia A.; Loughlin, Sandra M.

2008-01-01

In this study, we investigated the effects of students' participation in a collaborative, project-based engineering design course on their domain knowledge, interests, and strategic processing. Participants were 70 college seniors working in teams on a design project of their choosing. Their declarative, procedural, and principled knowledge, along…

Supporting Students' Knowledge Transfer in Modeling Activities

ERIC Educational Resources Information Center

Piksööt, Jaanika; Sarapuu, Tago

2014-01-01

This study investigates ways to enhance secondary school students' knowledge transfer in complex science domains by implementing question prompts. Two samples of students applied two web-based models to study molecular genetics--the model of genetic code (n = 258) and translation (n = 245). For each model, the samples were randomly divided into…

Systematic Representation of Knowledge of Ecology: Concepts and Relationships.

ERIC Educational Resources Information Center

Garb, Yaakov; And Others

This study describes efforts to apply principles of systematic knowledge representation (concept mapping and computer-based semantic networking techniques) to the domain of ecology. A set of 24 relationships and modifiers is presented that seem sufficient for describing all ecological relationships discussed in an introductory course. Many of…

Simplifying the construction of domain-specific automatic programming systems: The NASA automated software development workstation project

NASA Technical Reports Server (NTRS)

Allen, Bradley P.; Holtzman, Peter L.

1987-01-01

An overview is presented of the Automated Software Development Workstation Project, an effort to explore knowledge-based approaches to increasing software productivity. The project focuses on applying the concept of domain specific automatic programming systems (D-SAPSs) to application domains at NASA's Johnson Space Center. A version of a D-SAPS developed in Phase 1 of the project for the domain of space station momentum management is described. How problems encountered during its implementation led researchers to concentrate on simplifying the process of building and extending such systems is discussed. Researchers propose to do this by attacking three observed bottlenecks in the D-SAPS development process through the increased automation of the acquisition of programming knowledge and the use of an object oriented development methodology at all stages of the program design. How these ideas are being implemented in the Bauhaus, a prototype workstation for D-SAPS development is discussed.

Simplifying the construction of domain-specific automatic programming systems: The NASA automated software development workstation project

NASA Technical Reports Server (NTRS)

Allen, Bradley P.; Holtzman, Peter L.

1988-01-01

An overview is presented of the Automated Software Development Workstation Project, an effort to explore knowledge-based approaches to increasing software productivity. The project focuses on applying the concept of domain specific automatic programming systems (D-SAPSs) to application domains at NASA's Johnson Space Flight Center. A version of a D-SAPS developed in Phase 1 of the project for the domain of space station momentum management is described. How problems encountered during its implementation led researchers to concentrate on simplifying the process of building and extending such systems is discussed. Researchers propose to do this by attacking three observed bottlenecks in the D-SAPS development process through the increased automation of the acquisition of programming knowledge and the use of an object oriented development methodology at all stages of the program design. How these ideas are being implemented in the Bauhaus, a prototype workstation for D-SAPS development is discussed.

Chemical name extraction based on automatic training data generation and rich feature set.

PubMed

Yan, Su; Spangler, W Scott; Chen, Ying

2013-01-01

The automation of extracting chemical names from text has significant value to biomedical and life science research. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Assuming the availability of an incomplete list of chemical names, called a dictionary, we are able to generate well-controlled, random, yet realistic chemical-like training documents. We statistically analyze the construction of chemical names based on the incomplete dictionary, and propose a series of new features, without relying on any domain knowledge. Compared to state-of-the-art models learned from manually labeled data and domain knowledge, our solution shows better or comparable results in annotating real-world data with less human effort. Moreover, we report an interesting observation about the language for chemical names. That is, both the structural and semantic components of chemical names follow a Zipfian distribution, which resembles many natural languages.

Integrating natural language processing and web GIS for interactive knowledge domain visualization

NASA Astrophysics Data System (ADS)

Du, Fangming

Recent years have seen a powerful shift towards data-rich environments throughout society. This has extended to a change in how the artifacts and products of scientific knowledge production can be analyzed and understood. Bottom-up approaches are on the rise that combine access to huge amounts of academic publications with advanced computer graphics and data processing tools, including natural language processing. Knowledge domain visualization is one of those multi-technology approaches, with its aim of turning domain-specific human knowledge into highly visual representations in order to better understand the structure and evolution of domain knowledge. For example, network visualizations built from co-author relations contained in academic publications can provide insight on how scholars collaborate with each other in one or multiple domains, and visualizations built from the text content of articles can help us understand the topical structure of knowledge domains. These knowledge domain visualizations need to support interactive viewing and exploration by users. Such spatialization efforts are increasingly looking to geography and GIS as a source of metaphors and practical technology solutions, even when non-georeferenced information is managed, analyzed, and visualized. When it comes to deploying spatialized representations online, web mapping and web GIS can provide practical technology solutions for interactive viewing of knowledge domain visualizations, from panning and zooming to the overlay of additional information. This thesis presents a novel combination of advanced natural language processing - in the form of topic modeling - with dimensionality reduction through self-organizing maps and the deployment of web mapping/GIS technology towards intuitive, GIS-like, exploration of a knowledge domain visualization. A complete workflow is proposed and implemented that processes any corpus of input text documents into a map form and leverages a web application framework to let users explore knowledge domain maps interactively. This workflow is implemented and demonstrated for a data set of more than 66,000 conference abstracts.

Multiple Domains of Parental Secure Base Support During Childhood and Adolescence Contribute to Adolescents’ Representations of Attachment as a Secure Base Script

PubMed Central

Vaughn, Brian E.; Waters, Theodore E. A.; Steele, Ryan D.; Roisman, Glenn I.; Bost, Kelly K.; Truitt, Warren; Waters, Harriet S.; Booth-LaForce, Cathryn

2016-01-01

Although attachment theory claims that early attachment representations reflecting the quality of the child’s “lived experiences” are maintained across developmental transitions, evidence that has emerged over the last decade suggests that the association between early relationship quality and adolescents’ attachment representations is fairly modest in magnitude. We used aspects of parenting beyond sensitivity over childhood and adolescence and early security to predict adolescents’ scripted attachment representations. At age 18 years, 673 participants from the NICHD Study of Early Child Care and Youth Development (SECCYD) completed the Attachment Script Assessment (ASA) from which we derived an assessment of secure base script knowledge. Measures of secure base support from childhood through age 15 years (e.g., parental monitoring of child activity, father presence in the home) were selected as predictors and accounted for an additional 8% of the variance in secure base script knowledge scores above and beyond direct observations of sensitivity and early attachment status alone, suggesting that adolescents’ scripted attachment representations reflect multiple domains of parenting. Cognitive and demographic variables also significantly increased predicted variance in secure base script knowledge by 2% each. PMID:27032953

A knowledge base for tracking the impact of genomics on population health.

PubMed

Yu, Wei; Gwinn, Marta; Dotson, W David; Green, Ridgely Fisk; Clyne, Mindy; Wulf, Anja; Bowen, Scott; Kolor, Katherine; Khoury, Muin J

2016-12-01

We created an online knowledge base (the Public Health Genomics Knowledge Base (PHGKB)) to provide systematically curated and updated information that bridges population-based research on genomics with clinical and public health applications. Weekly horizon scanning of a wide variety of online resources is used to retrieve relevant scientific publications, guidelines, and commentaries. After curation by domain experts, links are deposited into Web-based databases. PHGKB currently consists of nine component databases. Users can search the entire knowledge base or search one or more component databases directly and choose options for customizing the display of their search results. PHGKB offers researchers, policy makers, practitioners, and the general public a way to find information they need to understand the complicated landscape of genomics and population health.Genet Med 18 12, 1312-1314.

Segmentation of medical images using explicit anatomical knowledge

NASA Astrophysics Data System (ADS)

Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

1999-07-01

Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

Expert knowledge maps for knowledge management: a case study in Traditional Chinese Medicine research.

PubMed

Cui, Meng; Yang, Shuo; Yu, Tong; Yang, Ce; Gao, Yonghong; Zhu, Haiyan

2013-10-01

To design a model to capture information on the state and trends of knowledge creation, at both an individual and an organizational level, in order to enhance knowledge management. We designed a graph-theoretic knowledge model, the expert knowledge map (EKM), based on literature-based annotation. A case study in the domain of Traditional Chinese Medicine research was used to illustrate the usefulness of the model. The EKM successfully captured various aspects of knowledge and enhanced knowledge management within the case-study organization through the provision of knowledge graphs, expert graphs, and expert-knowledge biography. Our model could help to reveal the hot topics, trends, and products of the research done by an organization. It can potentially be used to facilitate knowledge learning, sharing and decision-making among researchers, academicians, students, and administrators of organizations.

Knowledge-based design of generate-and-patch problem solvers that solve global resource assignment problems

NASA Technical Reports Server (NTRS)

Voigt, Kerstin

1992-01-01

We present MENDER, a knowledge based system that implements software design techniques that are specialized to automatically compile generate-and-patch problem solvers that satisfy global resource assignments problems. We provide empirical evidence of the superior performance of generate-and-patch over generate-and-test: even with constrained generation, for a global constraint in the domain of '2D-floorplanning'. For a second constraint in '2D-floorplanning' we show that even when it is possible to incorporate the constraint into a constrained generator, a generate-and-patch problem solver may satisfy the constraint more rapidly. We also briefly summarize how an extended version of our system applies to a constraint in the domain of 'multiprocessor scheduling'.

Conceptualising forensic science and forensic reconstruction. Part II: The critical interaction between research, policy/law and practice.

PubMed

Morgan, R M

2017-11-01

This paper builds on the FoRTE conceptual model presented in part I to address the forms of knowledge that are integral to the four components of the model. Articulating the different forms of knowledge within effective forensic reconstructions is valuable. It enables a nuanced approach to the development and use of evidence bases to underpin decision-making at every stage of a forensic reconstruction by enabling transparency in the reporting of inferences. It also enables appropriate methods to be developed to ensure quality and validity. It is recognised that the domains of practice, research, and policy/law intersect to form the nexus where forensic science is situated. Each domain has a distinctive infrastructure that influences the production and application of different forms of knowledge in forensic science. The channels that can enable the interaction between these domains, enhance the impact of research in theory and practice, increase access to research findings, and support quality are presented. The particular strengths within the different domains to deliver problem solving forensic reconstructions are thereby identified and articulated. It is argued that a conceptual understanding of forensic reconstruction that draws on the full range of both explicit and tacit forms of knowledge, and incorporates the strengths of the different domains pertinent to forensic science, offers a pathway to harness the full value of trace evidence for context sensitive, problem-solving forensic applications. Copyright © 2017 The Author. Published by Elsevier B.V. All rights reserved.

Domain Generality and Specificity in Children's Causal Inference about Ambiguous Data

ERIC Educational Resources Information Center

Sobel, David M.; Munro, Sarah E.

2009-01-01

In 5 experiments the authors examined children's understanding of causal mechanisms and their reasoning about base rates across domains of knowledge. Experiment 1 showed that 3-year-olds interpret objects activating a machine differently from a novel agent liking each object; children are more likely to treat the latter as indicating the objects…

The Development of a Corpus-Based Tool for Exploring Domain-Specific Collocational Knowledge in English

ERIC Educational Resources Information Center

Huang, Ping-Yu; Chen, Chien-Ming; Tsao, Nai-Lung; Wible, David

2015-01-01

Since it was published, Coxhead's (2000) Academic Word List (AWL) has been frequently used in English for academic purposes (EAP) classrooms, included in numerous teaching materials, and re-examined in light of various domain-specific corpora. Although well-received, the AWL has been criticized for ignoring some important facts that words still…

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques. M.S. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Kavi, Srinu

1984-01-01

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report.

Articulation Management for Intelligent Integration of Information

NASA Technical Reports Server (NTRS)

Maluf, David A.; Tran, Peter B.; Clancy, Daniel (Technical Monitor)

2001-01-01

When combining data from distinct sources, there is a need to share meta-data and other knowledge about various source domains. Due to semantic inconsistencies and heterogeneity of representations, problems arise in combining multiple domains when the domains are merged. The knowledge that is irrelevant to the task of interoperation will be included, making the result unnecessarily complex. This heterogeneity problem can be eliminated by mediating the conflicts and managing the intersections of the domains. For interoperation and intelligent access to heterogeneous information, the focus is on the intersection of the knowledge, since intersection will define the required articulation rules. An algebra over domain has been proposed to use articulation rules to support disciplined manipulation of domain knowledge resources. The objective of a domain algebra is to provide the capability for interrogating many domain knowledge resources, which are largely semantically disjoint. The algebra supports formally the tasks of selecting, combining, extending, specializing, and modifying Components from a diverse set of domains. This paper presents a domain algebra and demonstrates the use of articulation rules to link declarative interfaces for Internet and enterprise applications. In particular, it discusses the articulation implementation as part of a production system capable of operating over the domain described by the IDL (interface description language) of objects registered in multiple CORBA servers.

From scenarios to domain models: processes and representations

NASA Astrophysics Data System (ADS)

Haddock, Gail; Harbison, Karan

1994-03-01

The domain specific software architectures (DSSA) community has defined a philosophy for the development of complex systems. This philosophy improves productivity and efficiency by increasing the user's role in the definition of requirements, increasing the systems engineer's role in the reuse of components, and decreasing the software engineer's role to the development of new components and component modifications only. The scenario-based engineering process (SEP), the first instantiation of the DSSA philosophy, has been adopted by the next generation controller project. It is also the chosen methodology of the trauma care information management system project, and the surrogate semi-autonomous vehicle project. SEP uses scenarios from the user to create domain models and define the system's requirements. Domain knowledge is obtained from a variety of sources including experts, documents, and videos. This knowledge is analyzed using three techniques: scenario analysis, task analysis, and object-oriented analysis. Scenario analysis results in formal representations of selected scenarios. Task analysis of the scenario representations results in descriptions of tasks necessary for object-oriented analysis and also subtasks necessary for functional system analysis. Object-oriented analysis of task descriptions produces domain models and system requirements. This paper examines the representations that support the DSSA philosophy, including reference requirements, reference architectures, and domain models. The processes used to create and use the representations are explained through use of the scenario-based engineering process. Selected examples are taken from the next generation controller project.

Exploiting domain information for Word Sense Disambiguation of medical documents.

PubMed

Stevenson, Mark; Agirre, Eneko; Soroa, Aitor

2012-01-01

Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the domain of the target documents. The authors' goal is to improve these methods by using information about the topic of the document in which the ambiguous term appears. The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. The authors report a significant improvement when combining those key terms with local context, showing that domain information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency.

Exploiting domain information for Word Sense Disambiguation of medical documents

PubMed Central

Agirre, Eneko; Soroa, Aitor

2011-01-01

Objective Current techniques for knowledge-based Word Sense Disambiguation (WSD) of ambiguous biomedical terms rely on relations in the Unified Medical Language System Metathesaurus but do not take into account the domain of the target documents. The authors' goal is to improve these methods by using information about the topic of the document in which the ambiguous term appears. Design The authors proposed and implemented several methods to extract lists of key terms associated with Medical Subject Heading terms. These key terms are used to represent the document topic in a knowledge-based WSD system. They are applied both alone and in combination with local context. Measurements A standard measure of accuracy was calculated over the set of target words in the widely used National Library of Medicine WSD dataset. Results and discussion The authors report a significant improvement when combining those key terms with local context, showing that domain information improves the results of a WSD system based on the Unified Medical Language System Metathesaurus alone. The best results were obtained using key terms obtained by relevance feedback and weighted by inverse document frequency. PMID:21900701

User Interface Technology for Formal Specification Development

NASA Technical Reports Server (NTRS)

Lowry, Michael; Philpot, Andrew; Pressburger, Thomas; Underwood, Ian; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Formal specification development and modification are an essential component of the knowledge-based software life cycle. User interface technology is needed to empower end-users to create their own formal specifications. This paper describes the advanced user interface for AMPHION1 a knowledge-based software engineering system that targets scientific subroutine libraries. AMPHION is a generic, domain-independent architecture that is specialized to an application domain through a declarative domain theory. Formal specification development and reuse is made accessible to end-users through an intuitive graphical interface that provides semantic guidance in creating diagrams denoting formal specifications in an application domain. The diagrams also serve to document the specifications. Automatic deductive program synthesis ensures that end-user specifications are correctly implemented. The tables that drive AMPHION's user interface are automatically compiled from a domain theory; portions of the interface can be customized by the end-user. The user interface facilitates formal specification development by hiding syntactic details, such as logical notation. It also turns some of the barriers for end-user specification development associated with strongly typed formal languages into active sources of guidance, without restricting advanced users. The interface is especially suited for specification modification. AMPHION has been applied to the domain of solar system kinematics through the development of a declarative domain theory. Testing over six months with planetary scientists indicates that AMPHION's interactive specification acquisition paradigm enables users to develop, modify, and reuse specifications at least an order of magnitude more rapidly than manual program development.

Secular trends on traditional ecological knowledge: An analysis of different domains of knowledge among Tsimane' men.

PubMed

Reyes-García, Victoria; Luz, Ana C; Gueze, Maximilien; Paneque-Gálvez, Jaime; Macía, Manuel J; Orta-Martínez, Martí; Pino, Joan

2013-10-01

Empirical research provides contradictory evidence of the loss of traditional ecological knowledge across societies. Researchers have argued that culture, methodological differences, and site-specific conditions are responsible for such contradictory evidences. We advance and test a third explanation: the adaptive nature of traditional ecological knowledge systems. Specifically, we test whether different domains of traditional ecological knowledge experience different secular changes and analyze trends in the context of other changes in livelihoods. We use data collected among 651 Tsimane' men (Bolivian Amazon). Our findings indicate that different domains of knowledge follow different secular trends. Among the domains of knowledge analyzed, medicinal and wild edible knowledge appear as the most vulnerable; canoe building and firewood knowledge seem to remain constant across generations; whereas house building knowledge seems to experience a slight secular increase. Our analysis reflects on the adaptive nature of traditional ecological knowledge, highlighting how changes in this knowledge system respond to the particular needs of a society in a given point of time.

A knowledge representation approach using fuzzy cognitive maps for better navigation support in an adaptive learning system.

PubMed

Chrysafiadi, Konstantina; Virvou, Maria

2013-12-01

In this paper a knowledge representation approach of an adaptive and/or personalized tutoring system is presented. The domain knowledge should be represented in a more realistic way in order to allow the adaptive and/or personalized tutoring system to deliver the learning material to each individual learner dynamically taking into account her/his learning needs and her/his different learning pace. To succeed this, the domain knowledge representation has to depict the possible increase or decrease of the learner's knowledge. Considering that the domain concepts that constitute the learning material are not independent from each other, the knowledge representation approach has to allow the system to recognize either the domain concepts that are already partly or completely known for a learner, or the domain concepts that s/he has forgotten, taking into account the learner's knowledge level of the related concepts. In other words, the system should be informed about the knowledge dependencies that exist among the domain concepts of the learning material, as well as the strength on impact of each domain concept on others. Fuzzy Cognitive Maps (FCMs) seem to be an ideal way for representing graphically this kind of information. The suggested knowledge representation approach has been implemented in an e-learning adaptive system for teaching computer programming. The particular system was used by the students of a postgraduate program in the field of Informatics in the University of Piraeus and was compared with a corresponding system, in which the domain knowledge was represented using the most common used technique of network of concepts. The results of the evaluation were very encouraging.

Sentiment Analysis Using Common-Sense and Context Information

PubMed Central

Mittal, Namita; Bansal, Pooja; Garg, Sonal

2015-01-01

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods. PMID:25866505

Sentiment analysis using common-sense and context information.

PubMed

Agarwal, Basant; Mittal, Namita; Bansal, Pooja; Garg, Sonal

2015-01-01

Sentiment analysis research has been increasing tremendously in recent times due to the wide range of business and social applications. Sentiment analysis from unstructured natural language text has recently received considerable attention from the research community. In this paper, we propose a novel sentiment analysis model based on common-sense knowledge extracted from ConceptNet based ontology and context information. ConceptNet based ontology is used to determine the domain specific concepts which in turn produced the domain specific important features. Further, the polarities of the extracted concepts are determined using the contextual polarity lexicon which we developed by considering the context information of a word. Finally, semantic orientations of domain specific features of the review document are aggregated based on the importance of a feature with respect to the domain. The importance of the feature is determined by the depth of the feature in the ontology. Experimental results show the effectiveness of the proposed methods.

Domain-independent information extraction in unstructured text

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

Irwin, N.H.

Extracting information from unstructured text has become an important research area in recent years due to the large amount of text now electronically available. This status report describes the findings and work done during the second year of a two-year Laboratory Directed Research and Development Project. Building on the first-year`s work of identifying important entities, this report details techniques used to group words into semantic categories and to output templates containing selective document content. Using word profiles and category clustering derived during a training run, the time-consuming knowledge-building task can be avoided. Though the output still lacks in completeness whenmore » compared to systems with domain-specific knowledge bases, the results do look promising. The two approaches are compatible and could complement each other within the same system. Domain-independent approaches retain appeal as a system that adapts and learns will soon outpace a system with any amount of a priori knowledge.« less

Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis

PubMed Central

Castro, Alfonso; Sedano, Andrés A.; García, Fco. Javier; Villoslada, Eduardo

2017-01-01

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica’s global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam. PMID:29283398

Application of a Multimedia Service and Resource Management Architecture for Fault Diagnosis.

PubMed

Castro, Alfonso; Sedano, Andrés A; García, Fco Javier; Villoslada, Eduardo; Villagrá, Víctor A

2017-12-28

Nowadays, the complexity of global video products has substantially increased. They are composed of several associated services whose functionalities need to adapt across heterogeneous networks with different technologies and administrative domains. Each of these domains has different operational procedures; therefore, the comprehensive management of multi-domain services presents serious challenges. This paper discusses an approach to service management linking fault diagnosis system and Business Processes for Telefónica's global video service. The main contribution of this paper is the proposal of an extended service management architecture based on Multi Agent Systems able to integrate the fault diagnosis with other different service management functionalities. This architecture includes a distributed set of agents able to coordinate their actions under the umbrella of a Shared Knowledge Plane, inferring and sharing their knowledge with semantic techniques and three types of automatic reasoning: heterogeneous, ontology-based and Bayesian reasoning. This proposal has been deployed and validated in a real scenario in the video service offered by Telefónica Latam.

Ontology-Based Multiple Choice Question Generation

PubMed Central

Al-Yahya, Maha

2014-01-01

With recent advancements in Semantic Web technologies, a new trend in MCQ item generation has emerged through the use of ontologies. Ontologies are knowledge representation structures that formally describe entities in a domain and their relationships, thus enabling automated inference and reasoning. Ontology-based MCQ item generation is still in its infancy, but substantial research efforts are being made in the field. However, the applicability of these models for use in an educational setting has not been thoroughly evaluated. In this paper, we present an experimental evaluation of an ontology-based MCQ item generation system known as OntoQue. The evaluation was conducted using two different domain ontologies. The findings of this study show that ontology-based MCQ generation systems produce satisfactory MCQ items to a certain extent. However, the evaluation also revealed a number of shortcomings with current ontology-based MCQ item generation systems with regard to the educational significance of an automatically constructed MCQ item, the knowledge level it addresses, and its language structure. Furthermore, for the task to be successful in producing high-quality MCQ items for learning assessments, this study suggests a novel, holistic view that incorporates learning content, learning objectives, lexical knowledge, and scenarios into a single cohesive framework. PMID:24982937

Theory-based Bayesian models of inductive learning and reasoning.

PubMed

Tenenbaum, Joshua B; Griffiths, Thomas L; Kemp, Charles

2006-07-01

Inductive inference allows humans to make powerful generalizations from sparse data when learning about word meanings, unobserved properties, causal relationships, and many other aspects of the world. Traditional accounts of induction emphasize either the power of statistical learning, or the importance of strong constraints from structured domain knowledge, intuitive theories or schemas. We argue that both components are necessary to explain the nature, use and acquisition of human knowledge, and we introduce a theory-based Bayesian framework for modeling inductive learning and reasoning as statistical inferences over structured knowledge representations.

Marrying Content and Process in Computer Science Education

ERIC Educational Resources Information Center

Zendler, A.; Spannagel, C.; Klaudt, D.

2011-01-01

Constructivist approaches to computer science education emphasize that as well as knowledge, thinking skills and processes are involved in active knowledge construction. K-12 computer science curricula must not be based on fashions and trends, but on contents and processes that are observable in various domains of computer science, that can be…

One-Reason Decision Making Unveiled: A Measurement Model of the Recognition Heuristic

ERIC Educational Resources Information Center

Hilbig, Benjamin E.; Erdfelder, Edgar; Pohl, Rudiger F.

2010-01-01

The fast-and-frugal recognition heuristic (RH) theory provides a precise process description of comparative judgments. It claims that, in suitable domains, judgments between pairs of objects are based on recognition alone, whereas further knowledge is ignored. However, due to the confound between recognition and further knowledge, previous…

Epistemic Metacognition in Context: Evaluating and Learning Online Information

ERIC Educational Resources Information Center

Mason, Lucia; Boldrin, Angela; Ariasi, Nicola

2010-01-01

This study examined epistemic metacognition as a reflective activity about knowledge and knowing in the context of online information searching on the Web, and whether it was related to prior knowledge on the topic, study approach, and domain-specific beliefs about science. In addition, we investigated whether Internet-based learning was…

Automatic Line Network Extraction from Aerial Imagery of Urban Areas through Knowledge-Based Image Analysis.

DTIC Science & Technology

1988-01-19

approach for the analysis of aerial images. In this approach image analysis is performed ast three levels of abstraction, namely iconic or low-level... image analysis , symbolic or medium-level image analysis , and semantic or high-level image analysis . Domain dependent knowledge about prototypical urban

Supporting the Knowledge-to-Action Process: A Systems-Thinking Approach

ERIC Educational Resources Information Center

Cherney, Adrian; Head, Brian

2011-01-01

The processes for moving research-based knowledge to the domains of action in social policy and professional practice are complex. Several disciplinary research traditions have illuminated several key aspects of these processes. A more holistic approach, drawing on systems thinking, has also been outlined and advocated by recent contributors to…

Case-Exercises, Diagnosis, and Explanations in a Knowledge Based Tutoring System for Project Planning.

ERIC Educational Resources Information Center

Pulz, Michael; Lusti, Markus

PROJECTTUTOR is an intelligent tutoring system that enhances conventional classroom instruction by teaching problem solving in project planning. The domain knowledge covered by the expert module is divided into three functions. Structural analysis, identifies the activities that make up the project, time analysis, computes the earliest and latest…

A knowledgebase system to enhance scientific discovery: Telemakus

PubMed Central

Fuller, Sherrilynne S; Revere, Debra; Bugni, Paul F; Martin, George M

2004-01-01

Background With the rapid expansion of scientific research, the ability to effectively find or integrate new domain knowledge in the sciences is proving increasingly difficult. Efforts to improve and speed up scientific discovery are being explored on a number of fronts. However, much of this work is based on traditional search and retrieval approaches and the bibliographic citation presentation format remains unchanged. Methods Case study. Results The Telemakus KnowledgeBase System provides flexible new tools for creating knowledgebases to facilitate retrieval and review of scientific research reports. In formalizing the representation of the research methods and results of scientific reports, Telemakus offers a potential strategy to enhance the scientific discovery process. While other research has demonstrated that aggregating and analyzing research findings across domains augments knowledge discovery, the Telemakus system is unique in combining document surrogates with interactive concept maps of linked relationships across groups of research reports. Conclusion Based on how scientists conduct research and read the literature, the Telemakus KnowledgeBase System brings together three innovations in analyzing, displaying and summarizing research reports across a domain: (1) research report schema, a document surrogate of extracted research methods and findings presented in a consistent and structured schema format which mimics the research process itself and provides a high-level surrogate to facilitate searching and rapid review of retrieved documents; (2) research findings, used to index the documents, allowing searchers to request, for example, research studies which have studied the relationship between neoplasms and vitamin E; and (3) visual exploration interface of linked relationships for interactive querying of research findings across the knowledgebase and graphical displays of what is known as well as, through gaps in the map, what is yet to be tested. The rationale and system architecture are described and plans for the future are discussed. PMID:15507158

Developmental Change in the Influence of Domain-General Abilities and Domain-Specific Knowledge on Mathematics Achievement: An Eight-Year Longitudinal Study

ERIC Educational Resources Information Center

Geary, David C.; Nicholas, Alan; Li, Yaoran; Sun, Jianguo

2017-01-01

The contributions of domain-general abilities and domain-specific knowledge to subsequent mathematics achievement were longitudinally assessed (n = 167) through 8th grade. First grade intelligence and working memory and prior grade reading achievement indexed domain-general effects, and domain-specific effects were indexed by prior grade…

Semantic Data Integration and Knowledge Management to Represent Biological Network Associations.

PubMed

Losko, Sascha; Heumann, Klaus

2017-01-01

The vast quantities of information generated by academic and industrial research groups are reflected in a rapidly growing body of scientific literature and exponentially expanding resources of formalized data, including experimental data, originating from a multitude of "-omics" platforms, phenotype information, and clinical data. For bioinformatics, the challenge remains to structure this information so that scientists can identify relevant information, to integrate this information as specific "knowledge bases," and to formalize this knowledge across multiple scientific domains to facilitate hypothesis generation and validation. Here we report on progress made in building a generic knowledge management environment capable of representing and mining both explicit and implicit knowledge and, thus, generating new knowledge. Risk management in drug discovery and clinical research is used as a typical example to illustrate this approach. In this chapter we introduce techniques and concepts (such as ontologies, semantic objects, typed relationships, contexts, graphs, and information layers) that are used to represent complex biomedical networks. The BioXM™ Knowledge Management Environment is used as an example to demonstrate how a domain such as oncology is represented and how this representation is utilized for research.

Development of an expert system prototype for determining software functional requirements for command management activities at NASA Goddard

NASA Technical Reports Server (NTRS)

Liebowitz, J.

1986-01-01

The development of an expert system prototype for software functional requirement determination for NASA Goddard's Command Management System, as part of its process of transforming general requests into specific near-earth satellite commands, is described. The present knowledge base was formulated through interactions with domain experts, and was then linked to the existing Knowledge Engineering Systems (KES) expert system application generator. Steps in the knowledge-base development include problem-oriented attribute hierarchy development, knowledge management approach determination, and knowledge base encoding. The KES Parser and Inspector, in addition to backcasting and analogical mapping, were used to validate the expert system-derived requirements for one of the major functions of a spacecraft, the solar Maximum Mission. Knowledge refinement, evaluation, and implementation procedures of the expert system were then accomplished.

Problem-Oriented Corporate Knowledge Base Models on the Case-Based Reasoning Approach Basis

NASA Astrophysics Data System (ADS)

Gluhih, I. N.; Akhmadulin, R. K.

2017-07-01

One of the urgent directions of efficiency enhancement of production processes and enterprises activities management is creation and use of corporate knowledge bases. The article suggests a concept of problem-oriented corporate knowledge bases (PO CKB), in which knowledge is arranged around possible problem situations and represents a tool for making and implementing decisions in such situations. For knowledge representation in PO CKB a case-based reasoning approach is encouraged to use. Under this approach, the content of a case as a knowledge base component has been defined; based on the situation tree a PO CKB knowledge model has been developed, in which the knowledge about typical situations as well as specific examples of situations and solutions have been represented. A generalized problem-oriented corporate knowledge base structural chart and possible modes of its operation have been suggested. The obtained models allow creating and using corporate knowledge bases for support of decision making and implementing, training, staff skill upgrading and analysis of the decisions taken. The universal interpretation of terms “situation” and “solution” adopted in the work allows using the suggested models to develop problem-oriented corporate knowledge bases in different subject domains. It has been suggested to use the developed models for making corporate knowledge bases of the enterprises that operate engineer systems and networks at large production facilities.

Enhancing acronym/abbreviation knowledge bases with semantic information.

PubMed

Torii, Manabu; Liu, Hongfang

2007-10-11

In the biomedical domain, a terminology knowledge base that associates acronyms/abbreviations (denoted as SFs) with the definitions (denoted as LFs) is highly needed. For the construction such terminology knowledge base, we investigate the feasibility to build a system automatically assigning semantic categories to LFs extracted from text. Given a collection of pairs (SF,LF) derived from text, we i) assess the coverage of LFs and pairs (SF,LF) in the UMLS and justify the need of a semantic category assignment system; and ii) automatically derive name phrases annotated with semantic category and construct a system using machine learning. Utilizing ADAM, an existing collection of (SF,LF) pairs extracted from MEDLINE, our system achieved an f-measure of 87% when assigning eight UMLS-based semantic groups to LFs. The system has been incorporated into a web interface which integrates SF knowledge from multiple SF knowledge bases. Web site: http://gauss.dbb.georgetown.edu/liblab/SFThesurus.

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

PubMed

Zhou, Hang; Yang, Yang; Shen, Hong-Bin

2017-03-15

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Enhancing Users' Participation in Business Process Modeling through Ontology-Based Training

NASA Astrophysics Data System (ADS)

Macris, A.; Malamateniou, F.; Vassilacopoulos, G.

Successful business process design requires active participation of users who are familiar with organizational activities and business process modelling concepts. Hence, there is a need to provide users with reusable, flexible, agile and adaptable training material in order to enable them instil their knowledge and expertise in business process design and automation activities. Knowledge reusability is of paramount importance in designing training material on process modelling since it enables users participate actively in process design/redesign activities stimulated by the changing business environment. This paper presents a prototype approach for the design and use of training material that provides significant advantages to both the designer (knowledge - content reusability and semantic web enabling) and the user (semantic search, knowledge navigation and knowledge dissemination). The approach is based on externalizing domain knowledge in the form of ontology-based knowledge networks (i.e. training scenarios serving specific training needs) so that it is made reusable.

A partitioned model order reduction approach to rationalise computational expenses in nonlinear fracture mechanics

PubMed Central

Kerfriden, P.; Goury, O.; Rabczuk, T.; Bordas, S.P.A.

2013-01-01

We propose in this paper a reduced order modelling technique based on domain partitioning for parametric problems of fracture. We show that coupling domain decomposition and projection-based model order reduction permits to focus the numerical effort where it is most needed: around the zones where damage propagates. No a priori knowledge of the damage pattern is required, the extraction of the corresponding spatial regions being based solely on algebra. The efficiency of the proposed approach is demonstrated numerically with an example relevant to engineering fracture. PMID:23750055

Finding accurate frontiers: A knowledge-intensive approach to relational learning

NASA Technical Reports Server (NTRS)

Pazzani, Michael; Brunk, Clifford

1994-01-01

An approach to analytic learning is described that searches for accurate entailments of a Horn Clause domain theory. A hill-climbing search, guided by an information based evaluation function, is performed by applying a set of operators that derive frontiers from domain theories. The analytic learning system is one component of a multi-strategy relational learning system. We compare the accuracy of concepts learned with this analytic strategy to concepts learned with an analytic strategy that operationalizes the domain theory.

Distributed, cooperating knowledge-based systems

NASA Technical Reports Server (NTRS)

Truszkowski, Walt

1991-01-01

Some current research in the development and application of distributed, cooperating knowledge-based systems technology is addressed. The focus of the current research is the spacecraft ground operations environment. The underlying hypothesis is that, because of the increasing size, complexity, and cost of planned systems, conventional procedural approaches to the architecture of automated systems will give way to a more comprehensive knowledge-based approach. A hallmark of these future systems will be the integration of multiple knowledge-based agents which understand the operational goals of the system and cooperate with each other and the humans in the loop to attain the goals. The current work includes the development of a reference model for knowledge-base management, the development of a formal model of cooperating knowledge-based agents, the use of testbed for prototyping and evaluating various knowledge-based concepts, and beginning work on the establishment of an object-oriented model of an intelligent end-to-end (spacecraft to user) system. An introductory discussion of these activities is presented, the major concepts and principles being investigated are highlighted, and their potential use in other application domains is indicated.

Learning Resources Organization Using Ontological Framework

NASA Astrophysics Data System (ADS)

Gavrilova, Tatiana; Gorovoy, Vladimir; Petrashen, Elena

The paper describes the ontological approach to the knowledge structuring for the e-learning portal design as it turns out to be efficient and relevant to current domain conditions. It is primarily based on the visual ontology-based description of the content of the learning materials and this helps to provide productive and personalized access to these materials. The experience of ontology developing for Knowledge Engineering coursetersburg State University is discussed and “OntolingeWiki” tool for creating ontology-based e-learning portals is described.

Effects of Intelligent Computer-Generated Interactive Mathematics Programs on Students' Achievement and Affective Domain

ERIC Educational Resources Information Center

Wendel, Holly Marie

2016-01-01

The purpose of this study was to determine the relationship each of the mathematics web-based programs, MyMathLab and Assessments and Learning in Knowledge Spaces (ALEKS), has with students' mathematics achievement. In addition, the study examined the relationship between students' affective domain and the type of program as well as student…

"I Prefer Success": Subject Specificity in a First Grade

ERIC Educational Resources Information Center

Levstik, Linda S.; Yessin, Ruby

Research on restructuring domain-specific knowledge suggests that inferences made by a learner are based more on what and how concepts are structured and organized in particular domains than on the age of the learner. In this view, it is possible for children to operate more expertly in a particular area than could be explained by global stage or…

Multilevel semantic analysis and problem-solving in the flight-domain

NASA Technical Reports Server (NTRS)

Chien, R. T.

1982-01-01

The use of knowledge-base architecture and planning control; mechanisms to perform an intelligent monitoring task in the flight domain is addressed. The route level, the trajectory level, and parts of the aerodynamics level are demonstrated. Hierarchical planning and monitoring conceptual levels, functional-directed mechanism rationalization, and using deep-level mechanism models for diagnoses of dependent failures are discussed.

How to help intelligent systems with different uncertainty representations cooperate with each other

NASA Technical Reports Server (NTRS)

Kreinovich, Vladik YA.; Kumar, Sundeep

1991-01-01

In order to solve a complicated problem one must use the knowledge from different domains. Therefore, if one wants to automatize the solution of these problems, one has to help the knowledge-based systems that correspond to these domains cooperate, that is, communicate facts and conclusions to each other in the process of decision making. One of the main obstacles to such cooperation is the fact that different intelligent systems use different methods of knowledge acquisition and different methods and formalisms for uncertainty representation. So an interface f is needed, 'translating' the values x, y, which represent uncertainty of the experts' knowledge in one system, into the values f(x), f(y) appropriate for another one. The problem of designing such an interface as a mathematical problem is formulated and solved. It is shown that the interface must be fractionally linear: f(x) = (ax + b)/(cx + d).

Meta-tools for software development and knowledge acquisition

NASA Technical Reports Server (NTRS)

Eriksson, Henrik; Musen, Mark A.

1992-01-01

The effectiveness of tools that provide support for software development is highly dependent on the match between the tools and their task. Knowledge-acquisition (KA) tools constitute a class of development tools targeted at knowledge-based systems. Generally, KA tools that are custom-tailored for particular application domains are more effective than are general KA tools that cover a large class of domains. The high cost of custom-tailoring KA tools manually has encouraged researchers to develop meta-tools for KA tools. Current research issues in meta-tools for knowledge acquisition are the specification styles, or meta-views, for target KA tools used, and the relationships between the specification entered in the meta-tool and other specifications for the target program under development. We examine different types of meta-views and meta-tools. Our current project is to provide meta-tools that produce KA tools from multiple specification sources--for instance, from a task analysis of the target application.

Knowing Who Knows: Laypersons' Capabilities to Judge Experts' Pertinence for Science Topics.

PubMed

Bromme, Rainer; Thomm, Eva

2016-01-01

Because modern societies are built on elaborate divisions of cognitive labor, individuals remain laypersons in most knowledge domains. Hence, they have to rely on others' expertise when deciding on many science-related issues in private and public life. Even children already locate and discern expertise in the minds of others (e.g., Danovitch & Keil, 2004). This study examines how far university students accurately judge experts' pertinence for science topics even when they lack proficient knowledge of the domain. Participants judged the pertinence of experts from diverse disciplines based on the experts' assumed contributions to texts adapted from original articles from Science and Nature. Subjective pertinence judgments were calibrated by comparing them with bibliometrics of the original articles. Furthermore, participants' general science knowledge was controlled. Results showed that participants made well-calibrated pertinence judgments regardless of their level of general science knowledge. Copyright © 2015 Cognitive Science Society, Inc.

Distributed Knowledge Base Systems for Diagnosis and Information Retrieval.

DTIC Science & Technology

1985-09-01

thinks of the idiagnostic task, while it may be generic in the sense that the task may be quite similar across domains, it is not a unitary task...solving in our approach,’, outgrowth of ou group’s experience with MDX, a meaning that a special kind of organization and Q medical diagnostic program...5.4. Determining the Findings of a Knowledge U). It is important that the meaning of the Group knowledge group’s result be clear. In this knowledge

On construction method of shipborne and airborne radar intelligence and related equipment knowledge graph

NASA Astrophysics Data System (ADS)

Hao, Ruizhe; Huang, Jian

2017-08-01

Knowledge graph construction in military intelligence domain is sprouting but technically immature. This paper presents a method to construct the heterogeneous knowledge graph in the field of shipborne and airborne radar and equipment. Based on the expert knowledge and the up-to-date Internet open source information, we construct the knowledge graph of radar characteristic information and the equipment respectively, and establish relationships between two graphs, providing the pipeline and method for the intelligence organization and management in the context of the crowding battlefields big data.

Generic screen representations for future-proof systems, is it possible? There is more to a GUI than meets the eye.

PubMed

van der Linden, Helma; Austin, Tony; Talmon, Jan

2009-09-01

Future-proof EHR systems must be capable of interpreting information structures for medical concepts that were not available at the build-time of the system. The two-model approach of CEN 13606/openEHR using archetypes achieves this by separating generic clinical knowledge from domain-related knowledge. The presentation of this information can either itself be generic, or require design time awareness of the domain knowledge being employed. To develop a Graphical User Interface (GUI) that would be capable of displaying previously unencountered clinical data structures in a meaningful way. Through "reasoning by analogy" we defined an approach for the representation and implementation of "presentational knowledge". A proof-of-concept implementation was built to validate its implementability and to test for unanticipated issues. A two-model approach to specifying and generating a screen representation for archetype-based information, inspired by the two-model approach of archetypes, was developed. There is a separation between software-related display knowledge and domain-related display knowledge and the toolkit is designed with the reuse of components in mind. The approach leads to a flexible GUI that can adapt not only to information structures that had not been predefined within the receiving system, but also to novel ways of displaying the information. We also found that, ideally, the openEHR Archetype Definition Language should receive minor adjustments to allow for generic binding.

Mindtagger: A Demonstration of Data Labeling in Knowledge Base Construction.

PubMed

Shin, Jaeho; Ré, Christopher; Cafarella, Michael

2015-08-01

End-to-end knowledge base construction systems using statistical inference are enabling more people to automatically extract high-quality domain-specific information from unstructured data. As a result of deploying DeepDive framework across several domains, we found new challenges in debugging and improving such end-to-end systems to construct high-quality knowledge bases. DeepDive has an iterative development cycle in which users improve the data. To help our users, we needed to develop principles for analyzing the system's error as well as provide tooling for inspecting and labeling various data products of the system. We created guidelines for error analysis modeled after our colleagues' best practices, in which data labeling plays a critical role in every step of the analysis. To enable more productive and systematic data labeling, we created Mindtagger, a versatile tool that can be configured to support a wide range of tasks. In this demonstration, we show in detail what data labeling tasks are modeled in our error analysis guidelines and how each of them is performed using Mindtagger.

Implementation of a frame-based representation in CLIPS

NASA Technical Reports Server (NTRS)

Assal, Hisham; Myers, Leonard

1990-01-01

Knowledge representation is one of the major concerns in expert systems. The representation of domain-specific knowledge should agree with the nature of the domain entities and their use in the real world. For example, architectural applications deal with objects and entities such as spaces, walls, and windows. A natural way of representing these architectural entities is provided by frames. This research explores the potential of using the expert system shell CLIPS, developed by NASA, to implement a frame-based representation that can accommodate architectural knowledge. These frames are similar but quite different from the 'template' construct in version 4.3 of CLIPS. Templates support only the grouping of related information and the assignment of default values to template fields. In addition to these features frames provide other capabilities including definition of classes, inheritance between classes and subclasses, relation of objects of different classes with 'has-a', association of methods (demons) of different types (standard and user-defined) to fields (slots), and creation of new fields at run-time. This frame-based representation is implemented completely in CLIPS. No change to the source code is necessary.

Fall 2014 Data-Intensive Systems

DTIC Science & Technology

2014-10-29

Oct 2014 © 2014 Carnegie Mellon University Big Data Systems NoSQL and horizontal scaling are changing architecture principles by creating...University Status LEAP4BD • Ready to pilot QuABase • Prototype is complete – covers 8 NoSQL /NewSQL implementations • Completing validation testing Big...machine learning to automate population of knowledge base • Initial focus on NoSQL /NewSQL technology domain • Extend to create knowledge bases in other

Interrelationship of Knowledge, Interest, and Recall: Assessing a Model of Domain Learning.

ERIC Educational Resources Information Center

Alexander, Patricia A.; And Others

1995-01-01

Two experiments involving 125 college and graduate students examined the interrelationship of subject-matter knowledge, interest, and recall in the field of human immunology and biology and assessed cross-domain performance in physics. Patterns of knowledge, interest, and performance fit well with the premises of the Model of Domain Learning. (SLD)

Contribution of Content Knowledge and Learning Ability to the Learning of Facts.

ERIC Educational Resources Information Center

Kuhara-Kojima, Keiko; Hatano, Giyoo

1991-01-01

In 3 experiments, 1,598 Japanese college students were examined concerning the learning of facts in 2 content domains, baseball and music. Content knowledge facilitated fact learning only in the relevant domain; learning ability facilitated fact learning in both domains. Effects of content knowledge and learning ability were additive. (SLD)

Domain Knowledge, Search Behaviour, and Search Effectiveness of Engineering and Science Students: An Exploratory Study

ERIC Educational Resources Information Center

Zhang, Xiangmin; Anghelescu, Hermina G. B.; Yuan, Xiaojun

2005-01-01

Introduction: This study sought to answer three questions: 1) Would the level of domain knowledge significantly affect the user's search behaviour? 2) Would the level of domain knowledge significantly affect search effectiveness, and 3) What would be the relationship between search behaviour and search effectiveness? Method: Participants were…

The Effects of Domain Knowledge and Instructional Manipulation on Creative Idea Generation

ERIC Educational Resources Information Center

Hao, Ning

2010-01-01

The experiment was designed to explore the effects of domain knowledge, instructional manipulation, and the interaction between them on creative idea generation. Three groups of participants who respectively possessed the domain knowledge of biology, sports, or neither were asked to finish two tasks: imagining an extraterrestrial animal and…

Domain and Specification Models for Software Engineering

NASA Technical Reports Server (NTRS)

Iscoe, Neil; Liu, Zheng-Yang; Feng, Guohui

1992-01-01

This paper discusses our approach to representing application domain knowledge for specific software engineering tasks. Application domain knowledge is embodied in a domain model. Domain models are used to assist in the creation of specification models. Although many different specification models can be created from any particular domain model, each specification model is consistent and correct with respect to the domain model. One aspect of the system-hierarchical organization is described in detail.

Concept Mapping Assessment of Media Assisted Learning in Interdisciplinary Science Education

NASA Astrophysics Data System (ADS)

Schaal, Steffen; Bogner, Franz X.; Girwidz, Raimund

2010-05-01

Acquisition of conceptual knowledge is a central aim in science education. In this study we monitored an interdisciplinary hypermedia assisted learning unit on hibernation and thermodynamics based on cooperative learning. We used concept mapping for the assessment, applying a pre-test/post-test design. In our study, 106 9th graders cooperated by working in pairs ( n = 53) for six lessons. As an interdisciplinary learning activity in such complex knowledge domains has to combine many different aspects, we focused on long-term knowledge. Learners working cooperatively in dyads constructed computer-supported concept maps which were analysed by specific software. The data analysis encompassed structural aspects of the knowledge corresponding to a target reference map. After the learning unit, the results showed the acquisition of higher-order domain-specific knowledge structures which indicates successful interdisciplinary learning through the hypermedia learning environment. The benefit of using a computer-assisted concept mapping assessment for research in science education, and in science classrooms is considered.

Does Calculation or Word-Problem Instruction Provide A Stronger Route to Pre-Algebraic Knowledge?

PubMed Central

Fuchs, Lynn S.; Powell, Sarah R.; Cirino, Paul T.; Schumacher, Robin F.; Marrin, Sarah; Hamlett, Carol L.; Fuchs, Douglas; Compton, Donald L.; Changas, Paul C.

2014-01-01

The focus of this study was connections among 3 aspects of mathematical cognition at 2nd grade: calculations, word problems, and pre-algebraic knowledge. We extended the literature, which is dominated by correlational work, by examining whether intervention conducted on calculations or word problems contributes to improved performance in the other domain and whether intervention in either or both domains contributes to pre-algebraic knowledge. Participants were 1102 children in 127 2nd-grade classrooms in 25 schools. Teachers were randomly assigned to 3 conditions: calculation intervention, word-problem intervention, and business-as-usual control. Intervention, which lasted 17 weeks, was designed to provide research-based linkages between arithmetic calculations or arithmetic word problems (depending on condition) to pre-algebraic knowledge. Multilevel modeling suggested calculation intervention improved calculation but not word-problem outcomes; word-problem intervention enhanced word-problem but not calculation outcomes; and word-problem intervention provided a stronger route than calculation intervention to pre-algebraic knowledge. PMID:25541565

Autonomously acquiring declarative and procedural knowledge for ICAT systems

NASA Technical Reports Server (NTRS)

Kovarik, Vincent J., Jr.

1993-01-01

The construction of Intelligent Computer Aided Training (ICAT) systems is critically dependent on the ability to define and encode knowledge. This knowledge engineering effort can be broadly divided into two categories: domain knowledge and expert or task knowledge. Domain knowledge refers to the physical environment or system with which the expert interacts. Expert knowledge consists of the set of procedures and heuristics employed by the expert in performing their task. Both these areas are a significant bottleneck in the acquisition of knowledge for ICAT systems. This paper presents a research project in the area of autonomous knowledge acquisition using a passive observation concept. The system observes an expert and then generalizes the observations into production rules representing the domain expert's knowledge.

An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

PubMed Central

Shen, Mingwei; Wang, Jie; Wu, Di; Zhu, Daiyin

2014-01-01

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. PMID:25222035

A knowledge based search tool for performance measures in health care systems.

PubMed

Beyan, Oya D; Baykal, Nazife

2012-02-01

Performance measurement is vital for improving the health care systems. However, we are still far from having accepted performance measurement models. Researchers and developers are seeking comparable performance indicators. We developed an intelligent search tool to identify appropriate measures for specific requirements by matching diverse care settings. We reviewed the literature and analyzed 229 performance measurement studies published after 2000. These studies are evaluated with an original theoretical framework and stored in the database. A semantic network is designed for representing domain knowledge and supporting reasoning. We have applied knowledge based decision support techniques to cope with uncertainty problems. As a result we designed a tool which simplifies the performance indicator search process and provides most relevant indicators by employing knowledge based systems.

An expert system for the design of heating, ventilating, and air-conditioning systems

NASA Astrophysics Data System (ADS)

Camejo, Pedro Jose

1989-12-01

Expert systems are computer programs that seek to mimic human reason. An expert system shelf, a software program commonly used for developing expert systems in a relatively short time, was used to develop a prototypical expert system for the design of heating, ventilating, and air-conditioning (HVAC) systems in buildings. Because HVAC design involves several related knowledge domains, developing an expert system for HVAC design requires the integration of several smaller expert systems known as knowledge bases. A menu program and several auxiliary programs for gathering data, completing calculations, printing project reports, and passing data between the knowledge bases are needed and have been developed to join the separate knowledge bases into one simple-to-use program unit.

Knowledge-Guided Docking of WW Domain Proteins and Flexible Ligands

NASA Astrophysics Data System (ADS)

Lu, Haiyun; Li, Hao; Banu Bte Sm Rashid, Shamima; Leow, Wee Kheng; Liou, Yih-Cherng

Studies of interactions between protein domains and ligands are important in many aspects such as cellular signaling. We present a knowledge-guided approach for docking protein domains and flexible ligands. The approach is applied to the WW domain, a small protein module mediating signaling complexes which have been implicated in diseases such as muscular dystrophy and Liddle’s syndrome. The first stage of the approach employs a substring search for two binding grooves of WW domains and possible binding motifs of peptide ligands based on known features. The second stage aligns the ligand’s peptide backbone to the two binding grooves using a quasi-Newton constrained optimization algorithm. The backbone-aligned ligands produced serve as good starting points to the third stage which uses any flexible docking algorithm to perform the docking. The experimental results demonstrate that the backbone alignment method in the second stage performs better than conventional rigid superposition given two binding constraints. It is also shown that using the backbone-aligned ligands as initial configurations improves the flexible docking in the third stage. The presented approach can also be applied to other protein domains that involve binding of flexible ligand to two or more binding sites.

The Effectiveness of an Online Knowledge Map Instructional Presentation

ERIC Educational Resources Information Center

Foor, Jamie L.

2011-01-01

In this study, I investigated the effectiveness of the knowledge map (k-map) instructional strategy compared to a text-based presentation in an online environment. K-maps consist of node-link representations of concepts that together form the content of a topic or domain. The benefits of using k-maps are that concepts and ideas are represented as…

How To Create Complex Measurement Models: A Case Study of Principled Assessment Design.

ERIC Educational Resources Information Center

Bauer, Malcolm; Williamson, David M.; Steinberg, Linda S.; Mislevy, Robert J.; Behrens, John T.

In computer-based simulations, students must bring a wide range of relevant knowledge, skills, and abilities to bear jointly as they solve meaningful problems in a learning domain. To function effectively as an assessment, a simulation system must additionally be able to evoke and interpret observable evidence about targeted knowledge in a manner…

Objective and Subjective Knowledge and HIV Testing among College Students

ERIC Educational Resources Information Center

Hou, Su-I

2004-01-01

Little research has been conducted on the knowledge domain specifically related to HIV testing among college students. Students (age 18-24) were recruited from a major university in the southeastern United States to participate in a Web-based survey during spring 2003 (N=440). About 21% of the students reported previous voluntary HIV tests.…

Collaborative Learning Utilizing a Domain-Based Shared Data Repository to Enhance Learning Outcomes

ERIC Educational Resources Information Center

Lubliner, David; Widmeyer, George; Deek, Fadi P.

2009-01-01

The objective of this study was to determine whether there was a quantifiable improvement in learning outcomes by integrating course materials in a 4-year baccalaureate program, utilizing a knowledge repository with a conceptual map that spans a discipline. Two new models were developed to provide the framework for this knowledge repository. A…

How Do Physicians Become Medical Experts? A Test of Three Competing Theories: Distinct Domains, Independent Influence and Encapsulation Models

ERIC Educational Resources Information Center

Violato, Claudio; Gao, Hong; O'Brien, Mary Claire; Grier, David; Shen, E.

2018-01-01

The distinction between basic sciences and clinical knowledge which has led to a theoretical debate on how medical expertise is developed has implications for medical school and lifelong medical education. This longitudinal, population based observational study was conducted to test the fit of three theories--knowledge encapsulation, independent…

Exploring creative activity: a software environment for multimedia systems

NASA Astrophysics Data System (ADS)

Farrett, Peter W.; Jardine, David A.

1992-03-01

This paper examines various issues related to the theory, design, and implementation of a system that supports creative activity for a multimedia environment. The system incorporates artificial intelligence notions to acquire concepts of the problem domain. This paper investigates this environment by considering a model that is a basis for a system, which supports a history of user interaction. A multimedia system that supports creative activity is problematic. It must function as a tool allowing users to experiment dynamically with their own creative reasoning process--a very nebulous task environment. It should also support the acquisition of domain knowledge so that empirical observation can be further evaluated. This paper aims to illustrate that via the reuse of domain-specific knowledge, closely related ideas can be quickly developed. This approach is useful in the following sense: Multimedia navigational systems hardcode referential links with respect to a web or network. Although users can access or control navigation in a nonlinear (static) manner, these referential links are 'frozen' and can not capture their creative actions, which are essential in tutoring or learning applications. This paper describes a multimedia assistant based on the notion of knowledge- links, which allows users to navigate through creative information in a nonlinear (dynamic) fashion. A selection of prototype code based on object-oriented techniques and logic programming partially demonstrates this.

Automating the design of scientific computing software

NASA Technical Reports Server (NTRS)

Kant, Elaine

1992-01-01

SINAPSE is a domain-specific software design system that generates code from specifications of equations and algorithm methods. This paper describes the system's design techniques (planning in a space of knowledge-based refinement and optimization rules), user interaction style (user has option to control decision making), and representation of knowledge (rules and objects). It also summarizes how the system knowledge has evolved over time and suggests some issues in building software design systems to facilitate reuse.

Transforming schools into communities of thinking and learning about serious matters.

PubMed

Brown, A L

1997-04-01

In this article, a program of research known as Fostering Communities of Learners is described. This program is in place in several schools and classrooms serving inner-city students from 6 to 12 years of age. Based on theoretical advances in cognitive and developmental psychology, the program is successful at improving both literacy skills and domain-area subject matter knowledge (e.g., environmental science and biology). Building on young children's emergent strategic and metacognitive knowledge, together with their skeletal biological theories, the program leads children to discover the deep principles of the domain and to develop flexible learning and inquiry strategies of wide applicability.

Building Bridges for Dance through Arts-Based Research

ERIC Educational Resources Information Center

Wilson, Lisa; Moffett, Ann-Thomas

2017-01-01

This paper considers arts-based research (ABR) as a useful resource for creating fluid and dialogic spaces between multiple domains of dance knowledge and practices. Through the lens of a multi-disciplinary, arts-based research project "Same Story, Different Countries" explored the socio-political phenomena of racism in the United States…

Summarizing an Ontology: A "Big Knowledge" Coverage Approach.

PubMed

Zheng, Ling; Perl, Yehoshua; Elhanan, Gai; Ochs, Christopher; Geller, James; Halper, Michael

2017-01-01

Maintenance and use of a large ontology, consisting of thousands of knowledge assertions, are hampered by its scope and complexity. It is important to provide tools for summarization of ontology content in order to facilitate user "big picture" comprehension. We present a parameterized methodology for the semi-automatic summarization of major topics in an ontology, based on a compact summary of the ontology, called an "aggregate partial-area taxonomy", followed by manual enhancement. An experiment is presented to test the effectiveness of such summarization measured by coverage of a given list of major topics of the corresponding application domain. SNOMED CT's Specimen hierarchy is the test-bed. A domain-expert provided a list of topics that serves as a gold standard. The enhanced results show that the aggregate taxonomy covers most of the domain's main topics.

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis

PubMed Central

Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon

2018-01-01

Background With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. Objective This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. Methods We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. Results The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. Conclusions In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. PMID:29305341

Community/public health nursing faculty's knowledge, skills and attitudes of the Quad Council Competencies for Public Health Nurses.

PubMed

Joyce, Barbara L; Harmon, Monica; Johnson, Regina Gina H; Hicks, Vicki; Brown-Schott, Nancy; Pilling, Lucille; Brownrigg, Vicki

2018-05-02

A multisite collaborative team of community/public health nursing (C/PHN) faculty surveyed baccalaureate nursing faculty to explore their knowledge, skills, attitudes, and application of the Quad Council Competencies for Public Health Nurses (QCC-PHN). (1) Evaluate the knowledge, skills, and attitudes of the 2011 QCC-PHN by academic C/PHN faculty; (2) Evaluate the application of 2011 QCC-PHN by C/PHN faculty in the clinical practicum for undergraduate baccalaureate C/PHN students; and (3) Determine if a significant difference existed in the knowledge for each domain. A mixed methods descriptive research design was used to answer three specific hypotheses related to the study objectives. A convenience sample of 143 faculty teaching C/PHN in baccalaureate schools of nursing completed an online survey. ANOVA was used to determine the difference between knowledge, skills, attitudes, and application of nursing faculty regarding the QCC-PHN based on years of nursing experience, C/PHN experience, and nursing specialty preparation. Participants' qualitative comments for each domain were analyzed for themes. C/PHN nursing faculty are described and differences in knowledge, skills, and attitudes delineated. A statistically significant difference was found in skills based on years of experience in C/PHN and in the application of the competencies based on nursing specialty preparation. Variations in knowledge of the QCC-PHN are identified. Ten recommendations are proposed for key skill sets and necessary preparation for faculty to effectively teach C/PHN in baccalaureate schools of nursing. © 2018 Wiley Periodicals, Inc.

Does Domain Knowledge Moderate Involvement of Working Memory Capacity in Higher-Level Cognition? A Test of Three Models

ERIC Educational Resources Information Center

Hambrick, D.Z.; Oswald, F.L.

2005-01-01

Research suggests that both working memory capacity and domain knowledge contribute to individual differences in higher-level cognition. This study evaluated three hypotheses concerning the interplay between these factors. The compensation hypothesis predicts that domain knowledge attenuates the influence of working memory capacity on higher-level…

A UML-based meta-framework for system design in public health informatics.

PubMed

Orlova, Anna O; Lehmann, Harold

2002-01-01

The National Agenda for Public Health Informatics calls for standards in data and knowledge representation within public health, which requires a multi-level framework that links all aspects of public health. The literature of public health informatics and public health informatics application were reviewed. A UML-based systems analysis was performed. Face validity of results was evaluated in analyzing the public health domain of lead poisoning. The core class of the UML-based system of public health is the Public Health Domain, which is associated with multiple Problems, for which Actors provide Perspectives. Actors take Actions that define, generate, utilize and/or evaluate Data Sources. The life cycle of the domain is a sequence of activities attributed to its problems that spirals through multiple iterations and realizations within a domain. The proposed Public Health Informatics Meta-Framework broadens efforts in applying informatics principles to the field of public health

An ontological case base engineering methodology for diabetes management.

PubMed

El-Sappagh, Shaker H; El-Masri, Samir; Elmogy, Mohammed; Riad, A M; Saddik, Basema

2014-08-01

Ontology engineering covers issues related to ontology development and use. In Case Based Reasoning (CBR) system, ontology plays two main roles; the first as case base and the second as domain ontology. However, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. This paper proposes an ontology engineering methodology to generate case bases in the medical domain. It mainly focuses on the research of case representation in the form of ontology to support the case semantic retrieval and enhance all knowledge intensive CBR processes. A case study on diabetes diagnosis case base will be provided to evaluate the proposed methodology.

(Mis)perceptions of continuing education: insights from knowledge translation, quality improvement, and patient safety leaders.

PubMed

Kitto, Simon C; Bell, Mary; Goldman, Joanne; Peller, Jennifer; Silver, Ivan; Sargeant, Joan; Reeves, Scott

2013-01-01

Minimal attention has been given to the intersection and potential collaboration among the domains of continuing education (CE), knowledge translation (KT), quality improvement (QI), and patient safety (PS), despite their overlapping objectives. A study was undertaken to examine leaders' perspectives of these 4 domains and their relationships to each other. In this article, we report on a subset of the data that focuses on how leaders in KT, PS, and QI define and view the domain of CE and opportunities for collaboration. This study is based on a qualitative interpretivist framework to guide the collection and analysis of data in semistructured interviews. Criterion-based, maximum variation, and snowball sampling were used to identify key opinion leaders in each domain. The sample consisted of 15 individuals from the domains KT, QI, and PS. The transcripts were coded using a directed content analysis approach. The findings are organized into 3 thematic subsections: (1) definition and interpretation of CE, (2) concerns about relevance and effectiveness of CE, and (3) opportunities for collaboration among CE and the other domains. While there were slight differences among the data from the leaders of each domain, common themes were generally reported. The findings provide CE leaders with information about KT, QI, and PS leaders' (mis)perceptions about CE that can inform future strategic planning and activities. CE leaders can play an important role in building upon initial collaborations among the domains to enable their strengths to complement each other. Copyright © 2013 The Alliance for Continuing Education in the Health Professions, the Society for Academic Continuing Medical Education, and the Council on CME, Association for Hospital Medical Education.

Evidence-based selection of theories for designing behaviour change interventions: using methods based on theoretical construct domains to understand clinicians' blood transfusion behaviour.

PubMed

Francis, Jill J; Stockton, Charlotte; Eccles, Martin P; Johnston, Marie; Cuthbertson, Brian H; Grimshaw, Jeremy M; Hyde, Chris; Tinmouth, Alan; Stanworth, Simon J

2009-11-01

Many theories of behaviour are potentially relevant to predictive and intervention studies but most studies investigate a narrow range of theories. Michie et al. (2005) agreed 12 'theoretical domains' from 33 theories that explain behaviour change. They developed a 'Theoretical Domains Interview' (TDI) for identifying relevant domains for specific clinical behaviours, but the framework has not been used for selecting theories for predictive studies. It was used here to investigate clinicians' transfusion behaviour in intensive care units (ICU). Evidence suggests that red blood cells transfusion could be reduced for some patients without reducing quality of care. (1) To identify the domains relevant to transfusion practice in ICUs and neonatal intensive care units (NICUs), using the TDI. (2) To use the identified domains to select appropriate theories for a study predicting transfusion behaviour. An adapted TDI about managing a patient with borderline haemoglobin by watching and waiting instead of transfusing red blood cells was used to conduct semi-structured, one-to-one interviews with 18 intensive care consultants and neonatologists across the UK. Relevant theoretical domains were: knowledge, beliefs about capabilities, beliefs about consequences, social influences, behavioural regulation. Further analysis at the construct level resulted in selection of seven theoretical approaches relevant to this context: Knowledge-Attitude-Behaviour Model, Theory of Planned Behaviour, Social Cognitive Theory, Operant Learning Theory, Control Theory, Normative Model of Work Team Effectiveness and Action Planning Approaches. This study illustrated, the use of the TDI to identify relevant domains in a complex area of inpatient care. This approach is potentially valuable for selecting theories relevant to predictive studies and resulted in greater breadth of potential explanations than would be achieved if a single theoretical model had been adopted.

College students' memory for vocabulary in their majors: evidence for a nonlinear relation between knowledge and memory.

PubMed

DeMarie, Darlene; Aloise-Young, Patricia A; Prideaux, Cheri L; Muransky-Doran, Jean; Gerda, Julie Hart

2004-09-01

The effect of domain knowledge on students' memory for vocabulary terms was investigated. Participants were 142 college students (94 education majors and 48 business majors). The measure of domain knowledge was the number of courses completed in the major. Students recalled three different lists (control, education, and business) of 20 words. Knowledge effects were estimated controlling for academic aptitude, academic achievement, and general memory ability. Domain-specific knowledge consistently predicted recall, above and beyond the effect of these control variables. Moreover, nonlinear models better represented the relation between knowledge and memory, with very similar functions predicting recall in both knowledge domains. Specifically, early in the majors more classes corresponded with increased memory performance, but a plateau period, when more classes did not result in higher recall, was evident for both majors. Longitudinal research is needed to explore at what point in learning novices' performance begins to resemble experts' performance.

Development of an audit method to assess the prevalence of the ACGME's general competencies in an undergraduate medical education curriculum.

PubMed

Mooney, Christopher J; Lurie, Stephen J; Lyness, Jeffrey M; Lambert, David R; Guzick, David S

2010-10-01

Despite the use of competency-based frameworks to evaluate physicians, the role of competency-based objectives in undergraduate medical education remains uncertain. By use of an audit methodology, we sought to determine how the six Accreditation Council for Graduate Medical Education (ACGME) competencies, conceptualized as educational domains, would map onto an undergraduate medical curriculum. Standardized audit forms listing required activities were provided to course directors, who were then asked to indicate which of the domains were represented in each activity. Descriptive statistics were calculated. Of 1,500 activities, there was a mean of 2.13 domains per activity. Medical Knowledge was the most prevalent (44%), followed by Patient Care (20%), Interpersonal and Communication Skills (12%), Professionalism (9%), Systems-Based Practice (8%), and Practice-Based Learning and Improvement (7%). There was considerable variation by year and course. The domains provide a useful framework for organizing didactic components. Faculty can also consider activities in light of the domains, providing a vocabulary for instituting curricular change and innovation.

Generating target system specifications from a domain model using CLIPS

NASA Technical Reports Server (NTRS)

Sugumaran, Vijayan; Gomaa, Hassan; Kerschberg, Larry

1991-01-01

The quest for reuse in software engineering is still being pursued and researchers are actively investigating the domain modeling approach to software construction. There are several domain modeling efforts reported in the literature and they all agree that the components that are generated from domain modeling are more conducive to reuse. Once a domain model is created, several target systems can be generated by tailoring the domain model or by evolving the domain model and then tailoring it according to the specified requirements. This paper presents the Evolutionary Domain Life Cycle (EDLC) paradigm in which a domain model is created using multiple views, namely, aggregation hierarchy, generalization/specialization hierarchies, object communication diagrams and state transition diagrams. The architecture of the Knowledge Based Requirements Elicitation Tool (KBRET) which is used to generate target system specifications is also presented. The preliminary version of KBRET is implemented in the C Language Integrated Production System (CLIPS).

New knowledge-based genetic algorithm for excavator boom structural optimization

NASA Astrophysics Data System (ADS)

Hua, Haiyan; Lin, Shuwen

2014-03-01

Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem.

Excellence in clinical teaching: knowledge transformation and development required.

PubMed

Irby, David M

2014-08-01

Clinical teachers in medicine face the daunting task of mastering the many domains of knowledge needed for practice and teaching. The breadth and complexity of this knowledge continue to increase, as does the difficulty of transforming the knowledge into concepts that are understandable to learners. Properly targeted faculty development has the potential to expedite the knowledge transformation process for clinical teachers. Based on my own research in clinical teaching and faculty development, as well as the work of others, I describe the unique forms of clinical teacher knowledge, the transformation of that knowledge for teaching purposes and implications for faculty development. The following forms of knowledge for clinical teaching in medicine need to be mastered and transformed: (i) knowledge of medicine and patients; (ii) knowledge of context; (iii) knowledge of pedagogy and learners, and (iv) knowledge integrated into teaching scripts. This knowledge is employed and conveyed through the parallel processes of clinical reasoning and clinical instructional reasoning. Faculty development can facilitate this knowledge transformation process by: (i) examining, deconstructing and practising new teaching scripts; (ii) focusing on foundational concepts; (iii) demonstrating knowledge-in-use, and (iv) creating a supportive organisational climate for clinical teaching. To become an excellent clinical teacher in medicine requires the transformation of multiple forms of knowledge for teaching purposes. These domains of knowledge allow clinical teachers to provide tailored instruction to learners at varying levels in the context of fast-paced and demanding clinical practice. Faculty development can facilitate this knowledge transformation process. © 2014 John Wiley & Sons Ltd.

Evidence-based approach to the maintenance of laboratory and medical equipment in resource-poor settings.

PubMed

Malkin, Robert; Keane, Allison

2010-07-01

Much of the laboratory and medical equipment in resource-poor settings is out-of-service. The most commonly cited reasons are (1) a lack of spare parts and (2) a lack of highly trained technicians. However, there is little data to support these hypotheses, or to generate evidence-based solutions to the problem. We studied 2,849 equipment-repair requests (of which 2,529 were out-of-service medical equipment) from 60 resource-poor hospitals located in 11 nations in Africa, Europe, Asia, and Central America. Each piece of equipment was analyzed by an engineer or an engineering student and a repair was attempted using only locally available materials. If the piece was placed back into service, we assumed that the engineer's problem analysis was correct. A total of 1,821 pieces of medical equipment were placed back into service, or 72%, without requiring the use of imported spare parts. Of those pieces repaired, 1,704 were sufficiently documented to determine what knowledge was required to place the equipment back into service. We found that six domains of knowledge were required to accomplish 99% of the repairs: electrical (18%), mechanical (18%), power supply (14%), plumbing (19%), motors (5%), and installation or user training (25%). A further analysis of the domains shows that 66% of the out-of-service equipment was placed back into service using only 107 skills covering basic knowledge in each domain; far less knowledge than that required of a biomedical engineer or biomedical engineering technician. We conclude that a great majority of laboratory and medical equipment can be put back into service without importing spare parts and using only basic knowledge. Capacity building in resource-poor settings should first focus on a limited set of knowledge; a body of knowledge that we call the biomedical technician's assistant (BTA). This data set suggests that a supported BTA could place 66% of the out-of-service laboratory and medical equipment in their hospital back into service.

Automated generation of patient-tailored electronic care pathways by translating computer-interpretable guidelines into hierarchical task networks.

PubMed

González-Ferrer, Arturo; ten Teije, Annette; Fdez-Olivares, Juan; Milian, Krystyna

2013-02-01

This paper describes a methodology which enables computer-aided support for the planning, visualization and execution of personalized patient treatments in a specific healthcare process, taking into account complex temporal constraints and the allocation of institutional resources. To this end, a translation from a time-annotated computer-interpretable guideline (CIG) model of a clinical protocol into a temporal hierarchical task network (HTN) planning domain is presented. The proposed method uses a knowledge-driven reasoning process to translate knowledge previously described in a CIG into a corresponding HTN Planning and Scheduling domain, taking advantage of HTNs known ability to (i) dynamically cope with temporal and resource constraints, and (ii) automatically generate customized plans. The proposed method, focusing on the representation of temporal knowledge and based on the identification of workflow and temporal patterns in a CIG, makes it possible to automatically generate time-annotated and resource-based care pathways tailored to the needs of any possible patient profile. The proposed translation is illustrated through a case study based on a 70 pages long clinical protocol to manage Hodgkin's disease, developed by the Spanish Society of Pediatric Oncology. We show that an HTN planning domain can be generated from the corresponding specification of the protocol in the Asbru language, providing a running example of this translation. Furthermore, the correctness of the translation is checked and also the management of ten different types of temporal patterns represented in the protocol. By interpreting the automatically generated domain with a state-of-art HTN planner, a time-annotated care pathway is automatically obtained, customized for the patient's and institutional needs. The generated care pathway can then be used by clinicians to plan and manage the patients long-term care. The described methodology makes it possible to automatically generate patient-tailored care pathways, leveraging an incremental knowledge-driven engineering process that starts from the expert knowledge of medical professionals. The presented approach makes the most of the strengths inherent in both CIG languages and HTN planning and scheduling techniques: for the former, knowledge acquisition and representation of the original clinical protocol, and for the latter, knowledge reasoning capabilities and an ability to deal with complex temporal and resource constraints. Moreover, the proposed approach provides immediate access to technologies such as business process management (BPM) tools, which are increasingly being used to support healthcare processes. Copyright © 2012 Elsevier B.V. All rights reserved.

Building capacity for knowledge translation in occupational therapy: learning through participatory action research.

PubMed

Bennett, Sally; Whitehead, Mary; Eames, Sally; Fleming, Jennifer; Low, Shanling; Caldwell, Elizabeth

2016-10-01

There has been widespread acknowledgement of the need to build capacity in knowledge translation however much of the existing work focuses on building capacity amongst researchers rather than with clinicians directly. This paper's aim is to describe a research project for developing a knowledge translation capacity building program for occupational therapy clinicians. Participatory action research methods were used to both develop and evaluate the knowledge translation capacity-building program. Participants were occupational therapists from a large metropolitan hospital in Australia. Researchers and clinicians worked together to use the action cycle of the Knowledge to Action Framework to increase use of knowledge translation itself within the department in general, within their clinical teams, and to facilitate knowledge translation becoming part of the department's culture. Barriers and enablers to using knowledge translation were identified through a survey based on the Theoretical Domains Framework and through focus groups. Multiple interventions were used to develop a knowledge translation capacity-building program. Fifty-two occupational therapists participated initially, but only 20 across the first 18 months of the project. Barriers and enablers were identified across all domains of the Theoretical Domains Framework. Interventions selected to address these barriers or facilitate enablers were categorised into ten different categories: educational outreach; teams working on clinical knowledge translation case studies; identifying time blocks for knowledge translation; mentoring; leadership strategies; communication strategies; documentation and resources to support knowledge translation; funding a knowledge translation champion one day per week; setting goals for knowledge translation; and knowledge translation reporting strategies. Use of these strategies was, and continues to be monitored. Participants continue to be actively involved in learning and shaping the knowledge translation program across the department and within their specific clinical areas. To build capacity for knowledge translation, it is important to involve clinicians. The action cycle of the Knowledge to Action framework is a useful guide to introduce the knowledge translation process to clinicians. It may be used to engage the department as a whole, and facilitate the learning and application of knowledge translation within specific clinical areas. Research evaluating this knowledge translation program is being conducted.

Competent Systems: Effective, Efficient, Deliverable.

ERIC Educational Resources Information Center

Abramson, Bruce

Recent developments in artificial intelligence and decision analysis suggest reassessing the approaches commonly taken to the design of knowledge-based systems. Competent systems are based on models known as influence diagrams, which graphically capture a domain's basic objects and their interrelationships. Among the benefits offered by influence…

Learning Outcomes in Affective Domain within Contemporary Architectural Curricula

ERIC Educational Resources Information Center

Savic, Marko; Kashef, Mohamad

2013-01-01

Contemporary architectural education has shifted from the traditional focus on providing students with specific knowledge and skill sets or "inputs" to outcome based, student-centred educational approach. Within the outcome based model, students' performance is assessed against measureable objectives that relate acquired knowledge…

ATHENA: A knowledge-based hybrid backpropagation-grammatical evolution neural network algorithm for discovering epistasis among quantitative trait Loci

PubMed Central

2010-01-01

Background Growing interest and burgeoning technology for discovering genetic mechanisms that influence disease processes have ushered in a flood of genetic association studies over the last decade, yet little heritability in highly studied complex traits has been explained by genetic variation. Non-additive gene-gene interactions, which are not often explored, are thought to be one source of this "missing" heritability. Methods Stochastic methods employing evolutionary algorithms have demonstrated promise in being able to detect and model gene-gene and gene-environment interactions that influence human traits. Here we demonstrate modifications to a neural network algorithm in ATHENA (the Analysis Tool for Heritable and Environmental Network Associations) resulting in clear performance improvements for discovering gene-gene interactions that influence human traits. We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions. We tested these modifications in silico using simulated datasets. Results We show that the alternative tree-based crossover modification resulted in a modest increase in the sensitivity of the ATHENA algorithm for discovering gene-gene interactions. The performance increase was highly statistically significant when backpropagation was used to locally fit NN weights. We also demonstrate that using domain knowledge to initialize the search for gene-gene interactions results in a large performance increase, especially when the search space is larger than the search coverage. Conclusions We show that a hybrid optimization procedure, alternative crossover strategies, and incorporation of domain knowledge from publicly available biological databases can result in marked increases in sensitivity and performance of the ATHENA algorithm for detecting and modelling gene-gene interactions that influence a complex human trait. PMID:20875103

Bidirectional Transfer between Metaphorical Related Domains in Implicit Learning of Form-Meaning Connections

PubMed Central

Yang, Zhiliang; Dienes, Zoltan

2013-01-01

People can implicitly learn a connection between linguistic forms and meanings, for example between specific determiners (e.g. this, that…) and the type of nouns to which they apply. Li et al (2013) recently found that transfer of form-meaning connections from a concrete domain (height) to an abstract domain (power) was achieved in a metaphor-consistent way without awareness, showing that unconscious knowledge can be abstract and flexibly deployed. The current study aims to determine whether people transfer knowledge of form-meaning connections not only from a concrete domain to an abstract one, but also vice versa, consistent with metaphor representation being bi-directional. With a similar paradigm as used by Li et al, participants learnt form- meaning connections of different domains (concrete vs. abstract) and then were tested on two kinds of generalizations (same and different domain generalization). As predicted, transfer of form-meaning connections occurred bidirectionally when structural knowledge was unconscious. Moreover, the present study also revealed that more transfer occurred between metaphorically related domains when judgment knowledge was conscious (intuition) rather than unconscious (guess). Conscious and unconscious judgment knowledge may have different functional properties. PMID:23844159

Knowledge Discovery from Posts in Online Health Communities Using Unified Medical Language System.

PubMed

Chen, Donghua; Zhang, Runtong; Liu, Kecheng; Hou, Lei

2018-06-19

Patient-reported posts in Online Health Communities (OHCs) contain various valuable information that can help establish knowledge-based online support for online patients. However, utilizing these reports to improve online patient services in the absence of appropriate medical and healthcare expert knowledge is difficult. Thus, we propose a comprehensive knowledge discovery method that is based on the Unified Medical Language System for the analysis of narrative posts in OHCs. First, we propose a domain-knowledge support framework for OHCs to provide a basis for post analysis. Second, we develop a Knowledge-Involved Topic Modeling (KI-TM) method to extract and expand explicit knowledge within the text. We propose four metrics, namely, explicit knowledge rate, latent knowledge rate, knowledge correlation rate, and perplexity, for the evaluation of the KI-TM method. Our experimental results indicate that our proposed method outperforms existing methods in terms of providing knowledge support. Our method enhances knowledge support for online patients and can help develop intelligent OHCs in the future.

Interactive knowledge discovery with the doctor-in-the-loop: a practical example of cerebral aneurysms research.

PubMed

Girardi, Dominic; Küng, Josef; Kleiser, Raimund; Sonnberger, Michael; Csillag, Doris; Trenkler, Johannes; Holzinger, Andreas

2016-09-01

Established process models for knowledge discovery find the domain-expert in a customer-like and supervising role. In the field of biomedical research, it is necessary to move the domain-experts into the center of this process with far-reaching consequences for both their research output and the process itself. In this paper, we revise the established process models for knowledge discovery and propose a new process model for domain-expert-driven interactive knowledge discovery. Furthermore, we present a research infrastructure which is adapted to this new process model and demonstrate how the domain-expert can be deeply integrated even into the highly complex data-mining process and data-exploration tasks. We evaluated this approach in the medical domain for the case of cerebral aneurysms research.

Conceptual Structure within and between Modalities

PubMed Central

Dilkina, Katia; Lambon Ralph, Matthew A.

2012-01-01

Current views of semantic memory share the assumption that conceptual representations are based on multimodal experience, which activates distinct modality-specific brain regions. This proposition is widely accepted, yet little is known about how each modality contributes to conceptual knowledge and how the structure of this contribution varies across these multiple information sources. We used verbal feature lists, features from drawings, and verbal co-occurrence statistics from latent semantic analysis to examine the informational structure in four domains of knowledge: perceptual, functional, encyclopedic, and verbal. The goals of the analysis were three-fold: (1) to assess the structure within individual modalities; (2) to compare structures between modalities; and (3) to assess the degree to which concepts organize categorically or randomly. Our results indicated significant and unique structure in all four modalities: perceptually, concepts organize based on prominent features such as shape, size, color, and parts; functionally, they group based on use and interaction; encyclopedically, they arrange based on commonality in location or behavior; and verbally, they group associatively or relationally. Visual/perceptual knowledge gives rise to the strongest hierarchical organization and is closest to classic taxonomic structure. Information is organized somewhat similarly in the perceptual and encyclopedic domains, which differs significantly from the structure in the functional and verbal domains. Notably, the verbal modality has the most unique organization, which is not at all categorical but also not random. The idiosyncrasy and complexity of conceptual structure across modalities raise the question of how all of these modality-specific experiences are fused together into coherent, multifaceted yet unified concepts. Accordingly, both methodological and theoretical implications of the present findings are discussed. PMID:23293593

Person-Centeredness in Home- and Community-Based Services and Supports: Domains, Attributes, and Assisted Living Indicators.

PubMed

Zimmerman, Sheryl; Love, Karen; Cohen, Lauren W; Pinkowitz, Jackie; Nyrop, Kirsten A

2014-01-01

As a result of the Centers for Medicare & Medicaid Services (CMS) interest in creating a unifying definition of "community living" for its Medicaid Home and Community Based Services and Support (HCBS) programs, it needed clarifying descriptors of person-centered (PC) practices in assisted living to distinguish them from institutional ones. Additionally, CMS's proposed language defining "community living" had the unintended potential to exclude many assisted living communities and disadvantage residents who receive Medicaid. This manuscript describes the consensus process through which clarifying language for "community living" and a framework for HCBS PC domains, attributes, and indicators specific to assisted living were developed. It examines the validity of those domains based on literature review, surveys, and stakeholder focus groups, and identifies nine domains and 43 indicators that provide a foundation for defining and measuring PC practice in assisted living. Ongoing efforts using community-based participatory research methods are further refining and testing PC indicators for assisted living to advance knowledge, operational policies, practices, and quality outcomes.

Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions.

PubMed

Yan, Yuguang; Wu, Qingyao; Tan, Mingkui; Ng, Michael K; Min, Huaqing; Tsang, Ivor W

2017-10-10

In this paper, we study the online heterogeneous transfer (OHT) learning problem, where the target data of interest arrive in an online manner, while the source data and auxiliary co-occurrence data are from offline sources and can be easily annotated. OHT is very challenging, since the feature spaces of the source and target domains are different. To address this, we propose a novel technique called OHT by hedge ensemble by exploiting both offline knowledge and online knowledge of different domains. To this end, we build an offline decision function based on a heterogeneous similarity that is constructed using labeled source data and unlabeled auxiliary co-occurrence data. After that, an online decision function is learned from the target data. Last, we employ a hedge weighting strategy to combine the offline and online decision functions to exploit knowledge from the source and target domains of different feature spaces. We also provide a theoretical analysis regarding the mistake bounds of the proposed approach. Comprehensive experiments on three real-world data sets demonstrate the effectiveness of the proposed technique.

Preservation of person-specific knowledge in semantic memory disorder: a longitudinal investigation in two cases of dementia.

PubMed

Haslam, Catherine; Sabah, Mazen

2013-03-01

The double dissociation involving person-specific and general semantic knowledge is supported by numerous patient studies, though cases with preservation of the former are few. In this paper, we report longitudinal data from two cases. Their knowledge in both domains was preserved at the start of the investigation, but progressive deterioration was primarily observed on tests of general semantics. These data strengthen the evidence-base for preservation of person-specific knowledge in semantic memory disorder, and support its separate representation from object knowledge. © 2012 The British Psychological Society.

Federal Public Health Workforce Development: An Evidence-Based Approach for Defining Competencies.

PubMed

Mumford, Karen; Young, Andrea C; Nawaz, Saira

2016-01-01

This study reports the use of exploratory factor analysis to describe essential skills and knowledge for an important segment of the domestic public health workforce-Centers for Disease Control and Prevention (CDC) project officers-using an evidence-based approach to competency development and validation. A multicomponent survey was conducted. Exploratory factor analysis was used to examine the underlying domains and relationships between competency domains and key behaviors. The Cronbach α coefficient determined the reliability of the overall scale and identified factors. All domestic (US state, tribe, local, and territorial) grantees who received funding from the CDC during fiscal year 2011 to implement nonresearch prevention or intervention programs were invited to participate in a Web-based questionnaire. A total of 34 key behaviors representing knowledge, skills, and abilities, grouped in 7 domains-communication, grant administration and management, public health applied science and knowledge, program planning and development, program management, program monitoring and improvement, and organizational consultation-were examined. There were 795 responses (58% response rate). A total of 6 factors were identified with loadings of 0.40 or more for all 34 behavioral items. The Cronbach α coefficient was 0.95 overall and ranged between 0.73 and 0.91 for the factors. This study provides empirical evidence for the construct validity of 6 competencies and 34 key behaviors important for CDC project officers and serves as an important first step to evidence-driven workforce development efforts in public health.

The Domains of Human Nutrition: The Importance of Nutrition Education in Academia and Medical Schools

PubMed Central

Donini, Lorenzo M.; Leonardi, Francesco; Rondanelli, Mariangela; Banderali, Giuseppe; Battino, Maurizio; Bertoli, Enrico; Bordoni, Alessandra; Brighenti, Furio; Caccialanza, Riccardo; Cairella, Giulia; Caretto, Antonio; Cena, Hellas; Gambarara, Manuela; Gentile, Maria Gabriella; Giovannini, Marcello; Lucchin, Lucio; Migliaccio, Pietro; Nicastro, Francesco; Pasanisi, Fabrizio; Piretta, Luca; Radrizzani, Danilo; Roggi, Carla; Rotilio, Giuseppe; Scalfi, Luca; Vettor, Roberto; Vignati, Federico; Battistini, Nino C.; Muscaritoli, Maurizio

2017-01-01

Human nutrition encompasses an extremely broad range of medical, social, commercial, and ethical domains and thus represents a wide, interdisciplinary scientific and cultural discipline. The high prevalence of both disease-related malnutrition and overweight/obesity represents an important risk factor for disease burden and mortality worldwide. It is the opinion of Federation of the Italian Nutrition Societies (FeSIN) that these two sides of the same coin, with their sociocultural background, are related to a low “nutritional culture” secondary, at least in part, to an insufficient academic training for health-care professionals (HCPs). Therefore, FeSIN created a study group, composed of delegates of all the federated societies and representing the different HCPs involved in human nutrition, with the aim of identifying and defining the domains of human nutrition in the attempt to more clearly define the cultural identity of human nutrition in an academically and professionally oriented perspective and to report the conclusions in a position paper. Three main domains of human nutrition, namely, basic nutrition, applied nutrition, and clinical nutrition, were identified. FeSIN has examined the areas of knowledge pertinent to human nutrition. Thirty-two items were identified, attributed to one or more of the three domains and ranked considering their diverse importance for academic training in the different domains of human nutrition. Finally, the study group proposed the attribution of the different areas of knowledge to the degree courses where training in human nutrition is deemed necessary (e.g., schools of medicine, biology, nursing, etc.). It is conceivable that, in the near future, a better integration of the professionals involved in the field of human nutrition will eventually occur based on the progressive consolidation of knowledge, competence, and skills in the different areas and domains of this discipline. PMID:28275609

What Happens to the Food We Eat? Children's Conceptions of the Structure and Function of the Digestive System.

ERIC Educational Resources Information Center

Teixeira, Francimar Martins

2000-01-01

Describes children's conceptions of the structure and function of the human digestive system based on an investigation carried out with children aged 4-10 (n=45). Finds that children possess biological knowledge as an independent knowledge domain from the age of four. Discusses acquisition of and barriers to scientific concepts related to human…

Semi-Automated Methods for Refining a Domain-Specific Terminology Base

DTIC Science & Technology

2011-02-01

only as a resource for written and oral translation, but also for Natural Language Processing ( NLP ) applications, text retrieval, document indexing...Natural Language Processing ( NLP ) applications, text retrieval, document indexing, and other knowledge management tasks. The objective of this...also for Natural Language Processing ( NLP ) applications, text retrieval (1), document indexing, and other knowledge management tasks. The National

Developing an Advanced Environment for Collaborative Computing

NASA Technical Reports Server (NTRS)

Becerra-Fernandez, Irma; Stewart, Helen; DelAlto, Martha; DelAlto, Martha; Knight, Chris

1999-01-01

Knowledge management in general tries to organize and make available important know-how, whenever and where ever is needed. Today, organizations rely on decision-makers to produce "mission critical" decisions that am based on inputs from multiple domains. The ideal decision-maker has a profound understanding of specific domains that influence the decision-making process coupled with the experience that allows them to act quickly and decisively on the information. In addition, learning companies benefit by not repeating costly mistakes, and by reducing time-to-market in Research & Development projects. Group-decision making tools can help companies make better decisions by capturing the knowledge from groups of experts. Furthermore, companies that capture their customers preferences can improve their customer service, which translates to larger profits. Therefore collaborative computing provides a common communication space, improves sharing of knowledge, provides a mechanism for real-time feedback on the tasks being performed, helps to optimize processes, and results in a centralized knowledge warehouse. This paper presents the research directions. of a project which seeks to augment an advanced collaborative web-based environment called Postdoc, with workflow capabilities. Postdoc is a "government-off-the-shelf" document management software developed at NASA-Ames Research Center (ARC).

How can knowledge discovery methods uncover spatio-temporal patterns in environmental data?

NASA Astrophysics Data System (ADS)

Wachowicz, Monica

2000-04-01

This paper proposes the integration of KDD, GVis and STDB as a long-term strategy, which will allow users to apply knowledge discovery methods for uncovering spatio-temporal patterns in environmental data. The main goal is to combine innovative techniques and associated tools for exploring very large environmental data sets in order to arrive at valid, novel, potentially useful, and ultimately understandable spatio-temporal patterns. The GeoInsight approach is described using the principles and key developments in the research domains of KDD, GVis, and STDB. The GeoInsight approach aims at the integration of these research domains in order to provide tools for performing information retrieval, exploration, analysis, and visualization. The result is a knowledge-based design, which involves visual thinking (perceptual-cognitive process) and automated information processing (computer-analytical process).

Anchoring in Numeric Judgments of Visual Stimuli

PubMed Central

Langeborg, Linda; Eriksson, Mårten

2016-01-01

This article investigates effects of anchoring in age estimation and estimation of quantities, two tasks which to different extents are based on visual stimuli. The results are compared to anchoring in answers to classic general knowledge questions that rely on semantic knowledge. Cognitive load was manipulated to explore possible differences between domains. Effects of source credibility, manipulated by differing instructions regarding the selection of anchor values (no information regarding anchor selection, information that the anchors are randomly generated or information that the anchors are answers from an expert) on anchoring were also investigated. Effects of anchoring were large for all types of judgments but were not affected by cognitive load or by source credibility in either one of the researched domains. A main effect of cognitive load on quantity estimations and main effects of source credibility in the two visually based domains indicate that the manipulations were efficient. Implications for theoretical explanations of anchoring are discussed. In particular, because anchoring did not interact with cognitive load, the results imply that the process behind anchoring in visual tasks is predominantly automatic and unconscious. PMID:26941684

Knowledge-based public health situation awareness

NASA Astrophysics Data System (ADS)

Mirhaji, Parsa; Zhang, Jiajie; Srinivasan, Arunkumar; Richesson, Rachel L.; Smith, Jack W.

2004-09-01

There have been numerous efforts to create comprehensive databases from multiple sources to monitor the dynamics of public health and most specifically to detect the potential threats of bioterrorism before widespread dissemination. But there are not many evidences for the assertion that these systems are timely and dependable, or can reliably identify man made from natural incident. One must evaluate the value of so called 'syndromic surveillance systems' along with the costs involved in design, development, implementation and maintenance of such systems and the costs involved in investigation of the inevitable false alarms1. In this article we will introduce a new perspective to the problem domain with a shift in paradigm from 'surveillance' toward 'awareness'. As we conceptualize a rather different approach to tackle the problem, we will introduce a different methodology in application of information science, computer science, cognitive science and human-computer interaction concepts in design and development of so called 'public health situation awareness systems'. We will share some of our design and implementation concepts for the prototype system that is under development in the Center for Biosecurity and Public Health Informatics Research, in the University of Texas Health Science Center at Houston. The system is based on a knowledgebase containing ontologies with different layers of abstraction, from multiple domains, that provide the context for information integration, knowledge discovery, interactive data mining, information visualization, information sharing and communications. The modular design of the knowledgebase and its knowledge representation formalism enables incremental evolution of the system from a partial system to a comprehensive knowledgebase of 'public health situation awareness' as it acquires new knowledge through interactions with domain experts or automatic discovery of new knowledge.

An application of object-oriented knowledge representation to engineering expert systems

NASA Technical Reports Server (NTRS)

Logie, D. S.; Kamil, H.; Umaretiya, J. R.

1990-01-01

The paper describes an object-oriented knowledge representation and its application to engineering expert systems. The object-oriented approach promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects and organized by defining relationships between the objects. An Object Representation Language (ORL) was implemented as a tool for building and manipulating the object base. Rule-based knowledge representation is then used to simulate engineering design reasoning. Using a common object base, very large expert systems can be developed, comprised of small, individually processed, rule sets. The integration of these two schemes makes it easier to develop practical engineering expert systems. The general approach to applying this technology to the domain of the finite element analysis, design, and optimization of aerospace structures is discussed.

Expert system for the design of heating, ventilating, and air-conditioning systems. Master's thesis

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

Camejo, P.J.

1989-12-01

Expert systems are computer programs that seek to mimic human reason. An expert system shelf, a software program commonly used for developing expert systems in a relatively short time, was used to develop a prototypical expert system for the design of heating, ventilating, and air-conditioning (HVAC) systems in buildings. Because HVAC design involves several related knowledge domains, developing an expert system for HVAC design requires the integration of several smaller expert systems known as knowledge bases. A menu program and several auxiliary programs for gathering data, completing calculations, printing project reports, and passing data between the knowledge bases are neededmore » and have been developed to join the separate knowledge bases into one simple-to-use program unit.« less

Buildings classification from airborne LiDAR point clouds through OBIA and ontology driven approach

NASA Astrophysics Data System (ADS)

Tomljenovic, Ivan; Belgiu, Mariana; Lampoltshammer, Thomas J.

2013-04-01

In the last years, airborne Light Detection and Ranging (LiDAR) data proved to be a valuable information resource for a vast number of applications ranging from land cover mapping to individual surface feature extraction from complex urban environments. To extract information from LiDAR data, users apply prior knowledge. Unfortunately, there is no consistent initiative for structuring this knowledge into data models that can be shared and reused across different applications and domains. The absence of such models poses great challenges to data interpretation, data fusion and integration as well as information transferability. The intention of this work is to describe the design, development and deployment of an ontology-based system to classify buildings from airborne LiDAR data. The novelty of this approach consists of the development of a domain ontology that specifies explicitly the knowledge used to extract features from airborne LiDAR data. The overall goal of this approach is to investigate the possibility for classification of features of interest from LiDAR data by means of domain ontology. The proposed workflow is applied to the building extraction process for the region of "Biberach an der Riss" in South Germany. Strip-adjusted and georeferenced airborne LiDAR data is processed based on geometrical and radiometric signatures stored within the point cloud. Region-growing segmentation algorithms are applied and segmented regions are exported to the GeoJSON format. Subsequently, the data is imported into the ontology-based reasoning process used to automatically classify exported features of interest. Based on the ontology it becomes possible to define domain concepts, associated properties and relations. As a consequence, the resulting specific body of knowledge restricts possible interpretation variants. Moreover, ontologies are machinable and thus it is possible to run reasoning on top of them. Available reasoners (FACT++, JESS, Pellet) are used to check the consistency of the developed ontologies, and logical reasoning is performed to infer implicit relations between defined concepts. The ontology for the definition of building is specified using the Ontology Web Language (OWL). It is the most widely used ontology language that is based on Description Logics (DL). DL allows the description of internal properties of modelled concepts (roof typology, shape, area, height etc.) and relationships between objects (IS_A, MEMBER_OF/INSTANCE_OF). It captures terminological knowledge (TBox) as well as assertional knowledge (ABox) - that represents facts about concept instances, i.e. the buildings in airborne LiDAR data. To assess the classification accuracy, ground truth data generated by visual interpretation and calculated classification results in terms of precision and recall are used. The advantages of this approach are: (i) flexibility, (ii) transferability, and (iii) extendibility - i.e. ontology can be extended with further concepts, data properties and object properties.

NED-IIS: An Intelligent Information System for Forest Ecosystem Management

Treesearch

W.D. Potter; S. Somasekar; R. Kommineni; H.M. Rauscher

1999-01-01

We view Intelligent Information System (IIS) as composed of a unified knowledge base, database, and model base. The model base includes decision support models, forecasting models, and cvsualization models for example. In addition, we feel that the model base should include domain specific porblems solving modules as well as decision support models. This, then,...

Children's Comprehension of Object Relative Sentences: It's Extant Language Knowledge That Matters, Not Domain-General Working Memory

ERIC Educational Resources Information Center

Rusli, Yazmin Ahmad; Montgomery, James W.

2017-01-01

Purpose: The aim of this study was to determine whether extant language (lexical) knowledge or domain-general working memory is the better predictor of comprehension of object relative sentences for children with typical development. We hypothesized that extant language knowledge, not domain-general working memory, is the better predictor. Method:…

Incomplete Multisource Transfer Learning.

PubMed

Ding, Zhengming; Shao, Ming; Fu, Yun

2018-02-01

Transfer learning is generally exploited to adapt well-established source knowledge for learning tasks in weakly labeled or unlabeled target domain. Nowadays, it is common to see multiple sources available for knowledge transfer, each of which, however, may not include complete classes information of the target domain. Naively merging multiple sources together would lead to inferior results due to the large divergence among multiple sources. In this paper, we attempt to utilize incomplete multiple sources for effective knowledge transfer to facilitate the learning task in target domain. To this end, we propose an incomplete multisource transfer learning through two directional knowledge transfer, i.e., cross-domain transfer from each source to target, and cross-source transfer. In particular, in cross-domain direction, we deploy latent low-rank transfer learning guided by iterative structure learning to transfer knowledge from each single source to target domain. This practice reinforces to compensate for any missing data in each source by the complete target data. While in cross-source direction, unsupervised manifold regularizer and effective multisource alignment are explored to jointly compensate for missing data from one portion of source to another. In this way, both marginal and conditional distribution discrepancy in two directions would be mitigated. Experimental results on standard cross-domain benchmarks and synthetic data sets demonstrate the effectiveness of our proposed model in knowledge transfer from incomplete multiple sources.

Generation of Natural-Language Textual Summaries from Longitudinal Clinical Records.

PubMed

Goldstein, Ayelet; Shahar, Yuval

2015-01-01

Physicians are required to interpret, abstract and present in free-text large amounts of clinical data in their daily tasks. This is especially true for chronic-disease domains, but holds also in other clinical domains. We have recently developed a prototype system, CliniText, which, given a time-oriented clinical database, and appropriate formal abstraction and summarization knowledge, combines the computational mechanisms of knowledge-based temporal data abstraction, textual summarization, abduction, and natural-language generation techniques, to generate an intelligent textual summary of longitudinal clinical data. We demonstrate our methodology, and the feasibility of providing a free-text summary of longitudinal electronic patient records, by generating summaries in two very different domains - Diabetes Management and Cardiothoracic surgery. In particular, we explain the process of generating a discharge summary of a patient who had undergone a Coronary Artery Bypass Graft operation, and a brief summary of the treatment of a diabetes patient for five years.

Development of Veteran-Centric Competency Domains for Psychiatric-Mental Health Nurse Practitioner Residents.

PubMed

York, Janet; Sternke, Lisa Marie; Myrick, Donald Hugh; Lauerer, Joy; Hair, Carole

2016-11-01

The mental health needs of military service members, Veterans, and their families are a designated national priority; however, there has been little emphasis on the inclusion of Veteran-centric domains in competency-based nursing education for psychiatric-mental health nurse practitioners (PMHNPs). The current article describes the identification and application of Veteran-centric domains in an innovative pilot residency program for PMHNPs, funded by the Veterans Health Administration Office of Academic Affiliations. Fourteen Veteran-centric competency domains were developed from literature review, including knowledge, attitudes, and skill behaviors. Adoption and application of these domains in curricular components included the resident competency evaluation, baseline assessment of military experience, and evidence-based practice seminars and training. Methods of competency domain evaluation are presented, along with gaps related to the evaluation of competency skills. The delivery of mental health services reflecting these domains is consistent with the VA core values and goal of developing a positive service culture. [Journal of Psychosocial Nursing and Mental Health Services, 54(11), 31-36.]. Copyright 2016, SLACK Incorporated.

Specification, Design, and Analysis of Advanced HUMS Architectures

NASA Technical Reports Server (NTRS)

Mukkamala, Ravi

2004-01-01

During the two-year project period, we have worked on several aspects of domain-specific architectures for HUMS. In particular, we looked at using scenario-based approach for the design and designed a language for describing such architectures. The language is now being used in all aspects of our HUMS design. In particular, we have made contributions in the following areas. 1) We have employed scenarios in the development of HUMS in three main areas. They are: (a) To improve reusability by using scenarios as a library indexing tool and as a domain analysis tool; (b) To improve maintainability by recording design rationales from two perspectives - problem domain and solution domain; (c) To evaluate the software architecture. 2) We have defined a new architectural language called HADL or HUMS Architectural Definition Language. It is a customized version of xArch/xADL. It is based on XML and, hence, is easily portable from domain to domain, application to application, and machine to machine. Specifications written in HADL can be easily read and parsed using the currently available XML parsers. Thus, there is no need to develop a plethora of software to support HADL. 3) We have developed an automated design process that involves two main techniques: (a) Selection of solutions from a large space of designs; (b) Synthesis of designs. However, the automation process is not an absolute Artificial Intelligence (AI) approach though it uses a knowledge-based system that epitomizes a specific HUMS domain. The process uses a database of solutions as an aid to solve the problems rather than creating a new design in the literal sense. Since searching is adopted as the main technique, the challenges involved are: (a) To minimize the effort in searching the database where a very large number of possibilities exist; (b) To develop representations that could conveniently allow us to depict design knowledge evolved over many years; (c) To capture the required information that aid the automation process.

Using domain knowledge and domain-inspired discourse model for coreference resolution for clinical narratives

PubMed Central

Roth, Dan

2013-01-01

Objective This paper presents a coreference resolution system for clinical narratives. Coreference resolution aims at clustering all mentions in a single document to coherent entities. Materials and methods A knowledge-intensive approach for coreference resolution is employed. The domain knowledge used includes several domain-specific lists, a knowledge intensive mention parsing, and task informed discourse model. Mention parsing allows us to abstract over the surface form of the mention and represent each mention using a higher-level representation, which we call the mention's semantic representation (SR). SR reduces the mention to a standard form and hence provides better support for comparing and matching. Existing coreference resolution systems tend to ignore discourse aspects and rely heavily on lexical and structural cues in the text. The authors break from this tradition and present a discourse model for “person” type mentions in clinical narratives, which greatly simplifies the coreference resolution. Results This system was evaluated on four different datasets which were made available in the 2011 i2b2/VA coreference challenge. The unweighted average of F1 scores (over B-cubed, MUC and CEAF) varied from 84.2% to 88.1%. These experiments show that domain knowledge is effective for different mention types for all the datasets. Discussion Error analysis shows that most of the recall errors made by the system can be handled by further addition of domain knowledge. The precision errors, on the other hand, are more subtle and indicate the need to understand the relations in which mentions participate for building a robust coreference system. Conclusion This paper presents an approach that makes an extensive use of domain knowledge to significantly improve coreference resolution. The authors state that their system and the knowledge sources developed will be made publicly available. PMID:22781192

ISYMOD: a knowledge warehouse for the identification, assembly and analysis of bacterial integrated systems.

PubMed

Chabalier, Julie; Capponi, Cécile; Quentin, Yves; Fichant, Gwennaele

2005-04-01

Complex biological functions emerge from interactions between proteins in stable supra-molecular assemblies and/or through transitory contacts. Most of the time protein partners of the assemblies are composed of one or several domains which exhibit different biochemical functions. Thus the study of cellular process requires the identification of different functional units and their integration in an interaction network; such complexes are referred to as integrated systems. In order to exploit with optimum efficiency the increased release of data, automated bioinformatics strategies are needed to identify, reconstruct and model such systems. For that purpose, we have developed a knowledge warehouse dedicated to the representation and acquisition of bacterial integrated systems involved in the exchange of the bacterial cell with its environment. ISYMOD is a knowledge warehouse that consistently integrates in the same environment the data and the methods used for their acquisition. This is achieved through the construction of (1) a domain knowledge base (DKB) devoted to the storage of the knowledge about the systems, their functional specificities, their partners and how they are related and (2) a methodological knowledge base (MKB) which depicts the task layout used to identify and reconstruct functional integrated systems. Instantiation of the DKB is obtained by solving the tasks of the MKB, whereas some tasks need instances of the DKB to be solved. AROM, an object-based knowledge representation system, has been used to design the DKB, and its task manager, AROMTasks, for developing the MKB. In this study two integrated systems, ABC transporters and two component systems, both involved in adaptation processes of a bacterial cell to its biotope, have been used to evaluate the feasibility of the approach.

Knowledge Distance, Cognitive-Search Processes, and Creativity

PubMed Central

Acar, Oguz Ali; van den Ende, Jan

2016-01-01

Prior research has provided conflicting arguments and evidence about whether people who are outsiders or insiders relative to a knowledge domain are more likely to demonstrate scientific creativity in that particular domain. We propose that the nature of the relationship between creativity and the distance of an individual’s expertise from a knowledge domain depends on his or her cognitive processes of problem solving (i.e., cognitive-search effort and cognitive-search variation). In an analysis of 230 solutions generated in a science contest platform, we found that distance was positively associated with creativity when problem solvers engaged in a focused search (i.e., low cognitive-search variation) and exerted a high level of cognitive effort. People whose expertise was close to a knowledge domain, however, were more likely to demonstrate creativity in that domain when they drew on a wide variety of different knowledge elements for recombination (i.e., high cognitive-search variation) and exerted substantial cognitive effort. PMID:27016241

Knowledge Distance, Cognitive-Search Processes, and Creativity: The Making of Winning Solutions in Science Contests.

PubMed

Acar, Oguz Ali; van den Ende, Jan

2016-05-01

Prior research has provided conflicting arguments and evidence about whether people who are outsiders or insiders relative to a knowledge domain are more likely to demonstrate scientific creativity in that particular domain. We propose that the nature of the relationship between creativity and the distance of an individual's expertise from a knowledge domain depends on his or her cognitive processes of problem solving (i.e., cognitive-search effort and cognitive-search variation). In an analysis of 230 solutions generated in a science contest platform, we found that distance was positively associated with creativity when problem solvers engaged in a focused search (i.e., low cognitive-search variation) and exerted a high level of cognitive effort. People whose expertise was close to a knowledge domain, however, were more likely to demonstrate creativity in that domain when they drew on a wide variety of different knowledge elements for recombination (i.e., high cognitive-search variation) and exerted substantial cognitive effort. © The Author(s) 2016.

HCV knowledge among a sample of HCV positive Aboriginal Australians residing in New South Wales.

PubMed

Wilson, Hannah; Brener, Loren; Jackson, L Clair; Saunders, Veronica; Johnson, Priscilla; Treloar, Carla

2017-06-01

Australian Aboriginal and Torres Strait Islanders are overrepresented in both the prevalence and incidence of the hepatitis C (HCV). HCV knowledge has been associated with a range of positive health behaviours. HCV knowledge has previously been investigated as a single construct; however examining different knowledge domains (i.e. transmission, risk of complications, testing and treatment) separately may be beneficial. This study investigated whether having greater HCV knowledge in different domains is associated with self-reported positive health behaviours. 203 Aboriginal people living with HCV completed a survey assessing HCV knowledge, testing and care, lifestyle changes since diagnosis and treatment intent. Respondents' knowledge was relatively high. Greater knowledge of risk of health complications was associated with undertaking more positive lifestyle changes since diagnosis. Respondents testing and treatment knowledge was significantly associated with incarceration, lifestyle changes since diagnosis and future treatment intentions. This study illustrates the importance of ensuring that knowledge is high across different HCV domains to optimise a range of positive health behaviours of Aboriginal people living with HCV. Future health promotion campaigns targeted at Aboriginal people living with HCV could benefit from broadening their focus from prevention to other domains such as testing and treatment.

Towards Ontology as Knowledge Representation for Intellectual Capital Measurement

NASA Astrophysics Data System (ADS)

Zadjabbari, B.; Wongthongtham, P.; Dillon, T. S.

For many years, physical asset indicators were the main evidence of an organization’s successful performance. However, the situation has changed after information technology revolution in the knowledge-based economy. Since 1980’s business performance has not been limited only to physical assets instead intellectual capital are increasingly playing a major role in business performance. In this paper, we utilize ontology as a tool for knowledge representation in the domain of intellectual capital measurement. The ontology classifies ways of intangible capital measurement.

Generic domain models in software engineering

NASA Technical Reports Server (NTRS)

Maiden, Neil

1992-01-01

This paper outlines three research directions related to domain-specific software development: (1) reuse of generic models for domain-specific software development; (2) empirical evidence to determine these generic models, namely elicitation of mental knowledge schema possessed by expert software developers; and (3) exploitation of generic domain models to assist modelling of specific applications. It focuses on knowledge acquisition for domain-specific software development, with emphasis on tool support for the most important phases of software development.

An Examination of Learning Profiles in Physical Education

ERIC Educational Resources Information Center

Shen, Bo; Chen, Ang

2007-01-01

Using the model of domain learning as a theoretical framework, the study was designed to examine the extent to which learners' initial learning profiles based on previously acquired knowledge, learning strategy application, and interest-based motivation were distinctive in learning softball. Participants were 177 sixth-graders from three middle…

XML-based data model and architecture for a knowledge-based grid-enabled problem-solving environment for high-throughput biological imaging.

PubMed

Ahmed, Wamiq M; Lenz, Dominik; Liu, Jia; Paul Robinson, J; Ghafoor, Arif

2008-03-01

High-throughput biological imaging uses automated imaging devices to collect a large number of microscopic images for analysis of biological systems and validation of scientific hypotheses. Efficient manipulation of these datasets for knowledge discovery requires high-performance computational resources, efficient storage, and automated tools for extracting and sharing such knowledge among different research sites. Newly emerging grid technologies provide powerful means for exploiting the full potential of these imaging techniques. Efficient utilization of grid resources requires the development of knowledge-based tools and services that combine domain knowledge with analysis algorithms. In this paper, we first investigate how grid infrastructure can facilitate high-throughput biological imaging research, and present an architecture for providing knowledge-based grid services for this field. We identify two levels of knowledge-based services. The first level provides tools for extracting spatiotemporal knowledge from image sets and the second level provides high-level knowledge management and reasoning services. We then present cellular imaging markup language, an extensible markup language-based language for modeling of biological images and representation of spatiotemporal knowledge. This scheme can be used for spatiotemporal event composition, matching, and automated knowledge extraction and representation for large biological imaging datasets. We demonstrate the expressive power of this formalism by means of different examples and extensive experimental results.

Healing and morality: a Javanese example.

PubMed

Woodward, M R

1985-01-01

Javanese traditional medicine is based on Sufi Muslim notions of personhood, knowledge and magical power. This world view motivates two conflicting modalities of medical practice: one based on the magic powers of curers (dukun), the others on the religiously validated powers of Sufi saints. The association of magical and bio-medical knowledge allows Javanese to interpret traditional and bio-medical cures as components of a unified health care system. Comparison of Javanese medical, religious and political systems suggests that the structural uniformity of cultural domains derives from the hierarchical organization of cultural knowledge and that the study of traditional medicine and medical pluralism can not be undertaken apart from that of world view.

Knowledge acquisition from natural language for expert systems based on classification problem-solving methods

NASA Technical Reports Server (NTRS)

Gomez, Fernando

1989-01-01

It is shown how certain kinds of domain independent expert systems based on classification problem-solving methods can be constructed directly from natural language descriptions by a human expert. The expert knowledge is not translated into production rules. Rather, it is mapped into conceptual structures which are integrated into long-term memory (LTM). The resulting system is one in which problem-solving, retrieval and memory organization are integrated processes. In other words, the same algorithm and knowledge representation structures are shared by these processes. As a result of this, the system can answer questions, solve problems or reorganize LTM.

Modelling robot's behaviour using finite automata

NASA Astrophysics Data System (ADS)

Janošek, Michal; Žáček, Jaroslav

2017-07-01

This paper proposes a model of a robot's behaviour described by finite automata. We split robot's knowledge into several knowledge bases which are used by the inference mechanism of the robot's expert system to make a logic deduction. Each knowledgebase is dedicated to the particular behaviour domain and the finite automaton helps us switching among these knowledge bases with the respect of actual situation. Our goal is to simplify and reduce complexity of one big knowledgebase splitting it into several pieces. The advantage of this model is that we can easily add new behaviour by adding new knowledgebase and add this behaviour into the finite automaton and define necessary states and transitions.

Realizing Relevance: The Influence of Domain-Specific Information on Generation of New Knowledge through Integration in 4- to 8-Year-Old Children

ERIC Educational Resources Information Center

Bauer, Patricia J.; Larkina, Marina

2017-01-01

In accumulating knowledge, direct modes of learning are complemented by productive processes, including self-generation based on integration of separate episodes. Effects of the number of potentially relevant episodes on integration were examined in 4- to 8-year-olds (N = 121; racially/ethnically heterogeneous sample, English speakers, from large…

Knowledge and Skills for the Beginning German Teacher. The Praxis Series: Professional Assessments for Beginning Teachers.

ERIC Educational Resources Information Center

Reynolds, Anne

The job analysis study described in this report was conducted to serve as one of the bases for documenting the content validity of the Praxis II Subject Assessment in German. The purpose was to describe the most important knowledge and skills domains needed by newly licensed (certified) German teachers in order to perform their jobs in a competent…

Linguistic Model for Engine Power Loss

DTIC Science & Technology

2011-11-27

Intelligent Vehicle Health Management System (IVHMS) for light trucks. In particular, this paper is focused on the system architecture for monitoring...developed for the cooling system of a diesel engine, integrating a priori, ‘expert’ knowledge , sensor data, and the adaptive network-based fuzzy...domain knowledge . However, in a nonlinear system in which not all possible causes to engine power loss are considered and measured, merely relying

Knowledge-based modularization and global optimization of artificial neural network models in hydrological forecasting.

PubMed

Corzo, Gerald; Solomatine, Dimitri

2007-05-01

Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.

Survey of Australian practitioners' provision of healthy lifestyle advice to clients who are obese.

PubMed

Ashby, Samantha; James, Carole; Plotnikoff, Ronald; Collins, Clare; Guest, Maya; Kable, Ashley; Snodgrass, Suzanne

2012-06-01

Obesity is a global issue, with healthcare practitioners increasingly involved in clinical interactions with people who are overweight or obese. These interactions are opportunities to provide evidence-based healthy lifestyle advice, and impact on public health. This study used a cross-sectional survey of Australian healthcare practitioners to investigate what influenced the provision of healthy lifestyle advice to obese and overweight clients. A modified theory of planned behavior was used to explore knowledge translation processes. Knowledge translation was linked to three factors: (i) a healthcare practitioner's education and confidence in the currency of their knowledge; (ii) personal characteristics - whether they accepted that providing this advice was within their domain of practice; and (iii) the existence of organizational support structures, such as access to education, and best practice guidelines. To fulfill the potential role healthcare practitioners can play in the provision of evidence-based health promotion advice requires organizations to provide access to practice guidelines and to instill a belief in their workforce that this is a shared professional domain. © 2012 Blackwell Publishing Asia Pty Ltd.

The relation between prior knowledge and students' collaborative discovery learning processes

NASA Astrophysics Data System (ADS)

Gijlers, Hannie; de Jong, Ton

2005-03-01

In this study we investigate how prior knowledge influences knowledge development during collaborative discovery learning. Fifteen dyads of students (pre-university education, 15-16 years old) worked on a discovery learning task in the physics field of kinematics. The (face-to-face) communication between students was recorded and the interaction with the environment was logged. Based on students' individual judgments of the truth-value and testability of a series of domain-specific propositions, a detailed description of the knowledge configuration for each dyad was created before they entered the learning environment. Qualitative analyses of two dialogues illustrated that prior knowledge influences the discovery learning processes, and knowledge development in a pair of students. Assessments of student and dyad definitional (domain-specific) knowledge, generic (mathematical and graph) knowledge, and generic (discovery) skills were related to the students' dialogue in different discovery learning processes. Results show that a high level of definitional prior knowledge is positively related to the proportion of communication regarding the interpretation of results. Heterogeneity with respect to generic prior knowledge was positively related to the number of utterances made in the discovery process categories hypotheses generation and experimentation. Results of the qualitative analyses indicated that collaboration between extremely heterogeneous dyads is difficult when the high achiever is not willing to scaffold information and work in the low achiever's zone of proximal development.

Evidence-Based Practice Knowledge, Attitude, Access and Confidence: A comparison of dental hygiene and dental students.

PubMed

Santiago, Victoria; Cardenas, Melissa; Charles, Anne Laure; Hernandez, Estefany; Oyoyo, Udochukwu; Kwon, So Ran

2018-04-01

Purpose: The purpose of this study was to evaluate whether current educational strategies at a dental institution in the United States made a difference in dental hygiene (DNHY) and dental students' (D3) learning outcomes in the four domains of evidence-based practice (EBP), knowledge, attitude, accessing evidence, and confidence (KACE), following a 12-week research design course. Methods: All participants DNHY (n=19) and D3 (n=96) enrolled in the research design course at Loma Linda University completed a paper KACE survey distributed on the first day of class. Students completed the KACE survey once more at the end of the 12-week course. Pre- and post-survey results were compared both within and between the DNHY and D3 student groups to identify the learning outcomes in the four domains of EBP; knowledge, attitude, accessing evidence, and confidence in EBP. Descriptive statistics were conducted to profile all variables in the study; the level of significance was set at α=0.05. Results: All DNHY students (n=19) completed the pre and post KACE surveys; of the D3 (n=96) students enrolled in the course 82% (n=79) competed the post-survey. Comparison of the survey results showed that both DNHY and D3 students demonstrated statistically significant increases in their level of knowledge and attitude (p < 0.05) towards EBP. In the attitude domain, DNHY students indicated more positive attitudes towards EBP (p < 0.001) than their D3 student cohorts. Neither group demonstrated significant changes in confidence in applying EBP (p > 0.05). Conclusion: DNHY and D3 students increased their knowledge and developed more positive attitudes towards EBP following a 12-week research design course. Study results identify improvement areas for EBP knowledge acquisition including determining levels of evidence, analysis of study results, and evaluating the appropriateness of research study designs through the use of validated EBP survey instrument. Copyright © 2018 The American Dental Hygienists’ Association.

Sentence Similarity Analysis with Applications in Automatic Short Answer Grading

ERIC Educational Resources Information Center

Mohler, Michael A. G.

2012-01-01

In this dissertation, I explore unsupervised techniques for the task of automatic short answer grading. I compare a number of knowledge-based and corpus-based measures of text similarity, evaluate the effect of domain and size on the corpus-based measures, and also introduce a novel technique to improve the performance of the system by integrating…

The process-knowledge model of health literacy: evidence from a componential analysis of two commonly used measures.

PubMed

Chin, Jessie; Morrow, Daniel G; Stine-Morrow, Elizabeth A L; Conner-Garcia, Thembi; Graumlich, James F; Murray, Michael D

2011-01-01

We investigated the effects of domain-general processing capacity (fluid ability such as working memory), domain-general knowledge (crystallized ability such as vocabulary), and domain-specific health knowledge for two of the most commonly used measures of health literacy (S-TOFHLA and REALM). One hundred forty six community-dwelling older adults participated; 103 had been diagnosed with hypertension. The results showed that older adults who had higher levels of processing capacity or knowledge (domain-general or health) performed better on both of the health literacy measures. Processing capacity interacted with knowledge: Processing capacity had a lower level of association with health literacy for participants with more knowledge than for those with lower levels of knowledge, suggesting that knowledge may offset the effects of processing capacity limitations on health literacy. Furthermore, performance on the two health literacy measures appeared to reflect a different weighting for the three types of abilities. S-TOFHLA performance reflected processing capacity as well as general knowledge, whereas performance on the REALM depended more on general and health knowledge than on processing capacity. The findings support a process-knowledge model of health literacy among older adults, and have implications for selecting health literacy measures in various health care contexts.

Emerging medical informatics with case-based reasoning for aiding clinical decision in multi-agent system.

PubMed

Shen, Ying; Colloc, Joël; Jacquet-Andrieu, Armelle; Lei, Kai

2015-08-01

This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts. Copyright © 2015 Elsevier Inc. All rights reserved.

Online human training of a myoelectric prosthesis controller via actor-critic reinforcement learning.

PubMed

Pilarski, Patrick M; Dawson, Michael R; Degris, Thomas; Fahimi, Farbod; Carey, Jason P; Sutton, Richard S

2011-01-01

As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis. © 2011 IEEE

Maintaining consistency between planning hierarchies: Techniques and applications

NASA Technical Reports Server (NTRS)

Zoch, David R.

1987-01-01

In many planning and scheduling environments, it is desirable to be able to view and manipulate plans at different levels of abstraction, allowing the users the option of viewing and manipulating either a very detailed representation of the plan or a high-level more abstract version of the plan. Generating a detailed plan from a more abstract plan requires domain-specific planning/scheduling knowledge; the reverse process of generating a high-level plan from a detailed plan Reverse Plan Maintenance, or RPM) requires having the system remember the actions it took based on its domain-specific knowledge and its reasons for taking those actions. This reverse plan maintenance process is described as implemented in a specific planning and scheduling tool, The Mission Operations Planning Assistant (MOPA), as well as the applications of RPM to other planning and scheduling problems; emphasizing the knowledge that is needed to maintain the correspondence between the different hierarchical planning levels.

Deep neural network-based domain adaptation for classification of remote sensing images

NASA Astrophysics Data System (ADS)

Ma, Li; Song, Jiazhen

2017-10-01

We investigate the effectiveness of deep neural network for cross-domain classification of remote sensing images in this paper. In the network, class centroid alignment is utilized as a domain adaptation strategy, making the network able to transfer knowledge from the source domain to target domain on a per-class basis. Since predicted labels of target data should be used to estimate the centroid of each class, we use overall centroid alignment as a coarse domain adaptation method to improve the estimation accuracy. In addition, rectified linear unit is used as the activation function to produce sparse features, which may improve the separation capability. The proposed network can provide both aligned features and an adaptive classifier, as well as obtain label-free classification of target domain data. The experimental results using Hyperion, NCALM, and WorldView-2 remote sensing images demonstrated the effectiveness of the proposed approach.

Knowledge-Driven Event Extraction in Russian: Corpus-Based Linguistic Resources

PubMed Central

Solovyev, Valery; Ivanov, Vladimir

2016-01-01

Automatic event extraction form text is an important step in knowledge acquisition and knowledge base population. Manual work in development of extraction system is indispensable either in corpus annotation or in vocabularies and pattern creation for a knowledge-based system. Recent works have been focused on adaptation of existing system (for extraction from English texts) to new domains. Event extraction in other languages was not studied due to the lack of resources and algorithms necessary for natural language processing. In this paper we define a set of linguistic resources that are necessary in development of a knowledge-based event extraction system in Russian: a vocabulary of subordination models, a vocabulary of event triggers, and a vocabulary of Frame Elements that are basic building blocks for semantic patterns. We propose a set of methods for creation of such vocabularies in Russian and other languages using Google Books NGram Corpus. The methods are evaluated in development of event extraction system for Russian. PMID:26955386

Calculus domains modelled using an original bool algebra based on polygons

NASA Astrophysics Data System (ADS)

Oanta, E.; Panait, C.; Raicu, A.; Barhalescu, M.; Axinte, T.

2016-08-01

Analytical and numerical computer based models require analytical definitions of the calculus domains. The paper presents a method to model a calculus domain based on a bool algebra which uses solid and hollow polygons. The general calculus relations of the geometrical characteristics that are widely used in mechanical engineering are tested using several shapes of the calculus domain in order to draw conclusions regarding the most effective methods to discretize the domain. The paper also tests the results of several CAD commercial software applications which are able to compute the geometrical characteristics, being drawn interesting conclusions. The tests were also targeting the accuracy of the results vs. the number of nodes on the curved boundary of the cross section. The study required the development of an original software consisting of more than 1700 computer code lines. In comparison with other calculus methods, the discretization using convex polygons is a simpler approach. Moreover, this method doesn't lead to large numbers as the spline approximation did, in that case being required special software packages in order to offer multiple, arbitrary precision. The knowledge resulted from this study may be used to develop complex computer based models in engineering.

Generation of surgical pathology report using a 5,000-word speech recognizer.

PubMed

Tischler, A S; Martin, M R

1989-10-01

Pressures to decrease both turnaround time and operating costs simultaneously have placed conflicting demands on traditional forms of medical transcription. The new technology of voice recognition extends the promise of enabling the pathologist or other medical professional to dictate a correct report and have it printed and/or transmitted to a database immediately. The usefulness of voice recognition systems depends on several factors, including ease of use, reliability, speed, and accuracy. These in turn depend on the general underlying design of the systems and inclusion in the systems of a specific knowledge base appropriate for each application. Development of a good knowledge base requires close collaboration between a domain expert and a knowledge engineer with expertise in voice recognition. The authors have recently completed a knowledge base for surgical pathology using the Kurzweil VoiceReport 5,000-word system.

Enhanced situational awareness in the maritime domain: an agent-based approach for situation management

NASA Astrophysics Data System (ADS)

Brax, Christoffer; Niklasson, Lars

2009-05-01

Maritime Domain Awareness is important for both civilian and military applications. An important part of MDA is detection of unusual vessel activities such as piracy, smuggling, poaching, collisions, etc. Today's interconnected sensorsystems provide us with huge amounts of information over large geographical areas which can make the operators reach their cognitive capacity and start to miss important events. We propose and agent-based situation management system that automatically analyse sensor information to detect unusual activity and anomalies. The system combines knowledge-based detection with data-driven anomaly detection. The system is evaluated using information from both radar and AIS sensors.

Personal Reflection: Pedagogical Content Knowledge and the Affective Domain of Scholarship of Teaching and Learning

ERIC Educational Resources Information Center

Garritz, Andoni

2010-01-01

The question of these reflections is if among those content-dependent instructional conditions necessary to attain conceptual understanding, those belonging to the affective domain of teaching and learning must be included in Pedagogical Content Knowledge (PCK), the special amalgam of content knowledge and knowledge of general pedagogy that a…

A Pilot Study of Biomedical Text Comprehension using an Attention-Based Deep Neural Reader: Design and Experimental Analysis.

PubMed

Kim, Seongsoon; Park, Donghyeon; Choi, Yonghwa; Lee, Kyubum; Kim, Byounggun; Jeon, Minji; Kim, Jihye; Tan, Aik Choon; Kang, Jaewoo

2018-01-05

With the development of artificial intelligence (AI) technology centered on deep-learning, the computer has evolved to a point where it can read a given text and answer a question based on the context of the text. Such a specific task is known as the task of machine comprehension. Existing machine comprehension tasks mostly use datasets of general texts, such as news articles or elementary school-level storybooks. However, no attempt has been made to determine whether an up-to-date deep learning-based machine comprehension model can also process scientific literature containing expert-level knowledge, especially in the biomedical domain. This study aims to investigate whether a machine comprehension model can process biomedical articles as well as general texts. Since there is no dataset for the biomedical literature comprehension task, our work includes generating a large-scale question answering dataset using PubMed and manually evaluating the generated dataset. We present an attention-based deep neural model tailored to the biomedical domain. To further enhance the performance of our model, we used a pretrained word vector and biomedical entity type embedding. We also developed an ensemble method of combining the results of several independent models to reduce the variance of the answers from the models. The experimental results showed that our proposed deep neural network model outperformed the baseline model by more than 7% on the new dataset. We also evaluated human performance on the new dataset. The human evaluation result showed that our deep neural model outperformed humans in comprehension by 22% on average. In this work, we introduced a new task of machine comprehension in the biomedical domain using a deep neural model. Since there was no large-scale dataset for training deep neural models in the biomedical domain, we created the new cloze-style datasets Biomedical Knowledge Comprehension Title (BMKC_T) and Biomedical Knowledge Comprehension Last Sentence (BMKC_LS) (together referred to as BioMedical Knowledge Comprehension) using the PubMed corpus. The experimental results showed that the performance of our model is much higher than that of humans. We observed that our model performed consistently better regardless of the degree of difficulty of a text, whereas humans have difficulty when performing biomedical literature comprehension tasks that require expert level knowledge. ©Seongsoon Kim, Donghyeon Park, Yonghwa Choi, Kyubum Lee, Byounggun Kim, Minji Jeon, Jihye Kim, Aik Choon Tan, Jaewoo Kang. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 05.01.2018.

Prostate segmentation by feature enhancement using domain knowledge and adaptive region based operations

NASA Astrophysics Data System (ADS)

Nanayakkara, Nuwan D.; Samarabandu, Jagath; Fenster, Aaron

2006-04-01

Estimation of prostate location and volume is essential in determining a dose plan for ultrasound-guided brachytherapy, a common prostate cancer treatment. However, manual segmentation is difficult, time consuming and prone to variability. In this paper, we present a semi-automatic discrete dynamic contour (DDC) model based image segmentation algorithm, which effectively combines a multi-resolution model refinement procedure together with the domain knowledge of the image class. The segmentation begins on a low-resolution image by defining a closed DDC model by the user. This contour model is then deformed progressively towards higher resolution images. We use a combination of a domain knowledge based fuzzy inference system (FIS) and a set of adaptive region based operators to enhance the edges of interest and to govern the model refinement using a DDC model. The automatic vertex relocation process, embedded into the algorithm, relocates deviated contour points back onto the actual prostate boundary, eliminating the need of user interaction after initialization. The accuracy of the prostate boundary produced by the proposed algorithm was evaluated by comparing it with a manually outlined contour by an expert observer. We used this algorithm to segment the prostate boundary in 114 2D transrectal ultrasound (TRUS) images of six patients scheduled for brachytherapy. The mean distance between the contours produced by the proposed algorithm and the manual outlines was 2.70 ± 0.51 pixels (0.54 ± 0.10 mm). We also showed that the algorithm is insensitive to variations of the initial model and parameter values, thus increasing the accuracy and reproducibility of the resulting boundaries in the presence of noise and artefacts.

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach.

PubMed

Nasr Azadani, Mozhgan; Ghadiri, Nasser; Davoodijam, Ensieh

2018-06-12

Automatic text summarization offers an efficient solution to access the ever-growing amounts of both scientific and clinical literature in the biomedical domain by summarizing the source documents while maintaining their most informative contents. In this paper, we propose a novel graph-based summarization method that takes advantage of the domain-specific knowledge and a well-established data mining technique called frequent itemset mining. Our summarizer exploits the Unified Medical Language System (UMLS) to construct a concept-based model of the source document and mapping the document to the concepts. Then, it discovers frequent itemsets to take the correlations among multiple concepts into account. The method uses these correlations to propose a similarity function based on which a represented graph is constructed. The summarizer then employs a minimum spanning tree based clustering algorithm to discover various subthemes of the document. Eventually, it generates the final summary by selecting the most informative and relative sentences from all subthemes within the text. We perform an automatic evaluation over a large number of summaries using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. The results demonstrate that the proposed summarization system outperforms various baselines and benchmark approaches. The carried out research suggests that the incorporation of domain-specific knowledge and frequent itemset mining equips the summarization system in a better way to address the informativeness measurement of the sentences. Moreover, clustering the graph nodes (sentences) can enable the summarizer to target different main subthemes of a source document efficiently. The evaluation results show that the proposed approach can significantly improve the performance of the summarization systems in the biomedical domain. Copyright © 2018. Published by Elsevier Inc.

Studies in knowledge-based diagnosis of failures in robotic assembly

NASA Technical Reports Server (NTRS)

Lam, Raymond K.; Pollard, Nancy S.; Desai, Rajiv S.

1990-01-01

The telerobot diagnostic system (TDS) is a knowledge-based system that is being developed for identification and diagnosis of failures in the space robotic domain. The system is able to isolate the symptoms of the failure, generate failure hypotheses based on these symptoms, and test their validity at various levels by interpreting or simulating the effects of the hypotheses on results of plan execution. The implementation of the TDS is outlined. The classification of failures and the types of system models used by the TDS are discussed. A detailed example of the TDS approach to failure diagnosis is provided.

Gendered knowledge and adaptive practices: Differentiation and change in Mwanga District, Tanzania.

PubMed

Smucker, Thomas A; Wangui, Elizabeth Edna

2016-12-01

We examine the wider social knowledge domain that complements technical and environmental knowledge in enabling adaptive practices through two case studies in Tanzania. We are concerned with knowledge production that is shaped by gendered exclusion from the main thrusts of planned adaptation, in the practice of irrigation in a dryland village and the adoption of fast-maturing seed varieties in a highland village. The findings draw on data from a household survey, community workshops, and key informant interviews. The largest challenge to effective adaptation is a lack of access to the social networks and institutions that allocate resources needed for adaptation. Results demonstrate the social differentiation of local knowledge, and how it is entwined with adaptive practices that emerge in relation to gendered mechanisms of access. We conclude that community-based adaptation can learn from engaging the broader social knowledge base in evaluating priorities for coping with greater climate variability.

A unified approach to the design of clinical reporting systems.

PubMed

Gouveia-Oliveira, A; Salgado, N C; Azevedo, A P; Lopes, L; Raposo, V D; Almeida, I; de Melo, F G

1994-12-01

Computer-based Clinical Reporting Systems (CRS) for diagnostic departments that use structured data entry have a number of functional and structural affinities suggesting that a common software architecture for CRS may be defined. Such an architecture should allow easy expandability and reusability of a CRS. We report the development methodology and the architecture of SISCOPE, a CRS originally designed for gastrointestinal endoscopy that is expandable and reusable. Its main components are a patient database, a knowledge base, a reports base, and screen and reporting engines. The knowledge base contains the description of the controlled vocabulary and all the information necessary to control the menu system, and is easily accessed and modified with a conventional text editor. The structure of the controlled vocabulary is formally presented as an entity-relationship diagram. The screen engine drives a dynamic user interface and the reporting engine automatically creates a medical report; both engines operate by following a set of rules and the information contained in the knowledge base. Clinical experience has shown this architecture to be highly flexible and to allow frequent modifications of both the vocabulary and the menu system. This structure provided increased collaboration among development teams, insulating the domain expert from the details of the database, and enabling him to modify the system as necessary and to test the changes immediately. The system has also been reused in several different domains.

The core content of the undergraduate curriculum in Manchester.

PubMed

O'Neill, P A; Metcalfe, D; David, T J

1999-02-01

To identify the core content for the new undergraduate medical curriculum in Manchester. The initial step was to produce a list of 'index clinical situations' (ICSs), for which a newly graduated doctor must have a required level of competence. Using repeated consultation with consultants and general practitioners involved in medical education in the North-West of England, a list of 215 ICSs was agreed. Specialists and generalists were then asked to identify the components of the knowledge base and the performance (skills) base for each ICS. The knowledge base was divided into technical (biomedical facts/concepts) and contextual (effect/management of disease within the individual, family and society) domains. The performance base was divided into intellectual (problem solving and decision making) and interpersonal (history, examination, communication and procedural skills) domains. Forty specialties were consulted and 11,021 items (defined as a piece of knowledge, a concept or a skill) were identified. There was considerable overlap in the items listed, such that when the returns for each ICS were amalgamated, the 215 ICSs contained 6434 items with a mean of 34 +/- 14.2 per situation (range 6-85). UTILISATION: We have used the defined ICSs in the design of the trigger material used in the weekly problem-based learning sessions. Over 4 years almost all (207/215, 96%) of the ICS are covered, with many being revisited at several points in the curriculum.

OpenBiodiv-O: ontology of the OpenBiodiv knowledge management system.

PubMed

Senderov, Viktor; Simov, Kiril; Franz, Nico; Stoev, Pavel; Catapano, Terry; Agosti, Donat; Sautter, Guido; Morris, Robert A; Penev, Lyubomir

2018-01-18

The biodiversity domain, and in particular biological taxonomy, is moving in the direction of semantization of its research outputs. The present work introduces OpenBiodiv-O, the ontology that serves as the basis of the OpenBiodiv Knowledge Management System. Our intent is to provide an ontology that fills the gaps between ontologies for biodiversity resources, such as DarwinCore-based ontologies, and semantic publishing ontologies, such as the SPAR Ontologies. We bridge this gap by providing an ontology focusing on biological taxonomy. OpenBiodiv-O introduces classes, properties, and axioms in the domains of scholarly biodiversity publishing and biological taxonomy and aligns them with several important domain ontologies (FaBiO, DoCO, DwC, Darwin-SW, NOMEN, ENVO). By doing so, it bridges the ontological gap across scholarly biodiversity publishing and biological taxonomy and allows for the creation of a Linked Open Dataset (LOD) of biodiversity information (a biodiversity knowledge graph) and enables the creation of the OpenBiodiv Knowledge Management System. A key feature of the ontology is that it is an ontology of the scientific process of biological taxonomy and not of any particular state of knowledge. This feature allows it to express a multiplicity of scientific opinions. The resulting OpenBiodiv knowledge system may gain a high level of trust in the scientific community as it does not force a scientific opinion on its users (e.g. practicing taxonomists, library researchers, etc.), but rather provides the tools for experts to encode different views as science progresses. OpenBiodiv-O provides a conceptual model of the structure of a biodiversity publication and the development of related taxonomic concepts. It also serves as the basis for the OpenBiodiv Knowledge Management System.

Inferring protein domains associated with drug side effects based on drug-target interaction network.

PubMed

Iwata, Hiroaki; Mizutani, Sayaka; Tabei, Yasuo; Kotera, Masaaki; Goto, Susumu; Yamanishi, Yoshihiro

2013-01-01

Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains.

Behaviors and Knowledge of HealthCorps New York City High School Students: Nutrition, Mental Health and Physical Activity

PubMed Central

Moonseong, Heo; Erica, Irvin; Natania, Ostrovsky; Carmen, Isasi; Shawn, Hayes; Judith, Wylie-Rosett

2015-01-01

BACKGROUND HealthCorps provides school wellness programming using curricula to promote changes in nutrition, mental health and physical activity behaviors. The research objective was to evaluate effects of implementing its curricula on nutrition, mental health and physical activity knowledge and behavior. METHODS Pre- and post-survey data were collected (N = 2255) during the 2012-13 academic year from 14 New York City public high schools. An 18-item knowledge questionnaire addressed 3 domains; 26 behavioral items were analyzed by factor analysis to identify 6 behavior domains, breakfast being a seventh one-item domain. We examined the effects stratified by sex, applying mixed-effects models to take into account clustering effects of schools and participants adjusted for age. RESULTS The HealthCorps program significantly increased all 3 knowledge domains (p < .05), and significantly changed several key behavioral domains. Boys significantly increased fruits/vegetables intake (p = .03). Girls increased acceptance of new fruits/vegetables (p = .03) and breakfast consumption (p = .04), and decreased sugar-sweetened beverages and energy dense food intake (p = .03). The associations between knowledge and behavior were stronger in boys than girls. CONCLUSION The HealthCorps program significantly increased participants’ knowledge on nutrition, mental health and physical activity. It also improved several key behavioral domains, which are targets of the 2010 Dietary Guidelines to address obesity in youth. PMID:26762819

Behaviors and Knowledge of HealthCorps New York City High School Students: Nutrition, Mental Health, and Physical Activity.

PubMed

Heo, Moonseong; Irvin, Erica; Ostrovsky, Natania; Isasi, Carmen; Blank, Arthur E; Lounsbury, David W; Fredericks, Lynn; Yom, Tiana; Ginsberg, Mindy; Hayes, Shawn; Wylie-Rosett, Judith

2016-02-01

HealthCorps provides school wellness programming using curricula to promote changes in nutrition, mental health, and physical activity behaviors. The research objective was to evaluate effects of implementing its curricula on nutrition, mental health, and physical activity knowledge and behavior. Pre- and postsurvey data were collected (N = 2255) during the 2012-2013 academic year from 14 New York City public high schools. An 18-item knowledge questionnaire addressed 3 domains; 26 behavioral items were analyzed by factor analysis to identify 6 behavior domains, breakfast being a seventh 1-item domain. We examined the effects stratified by sex, applying mixed-effects models to take into account clustering effects of schools and participants adjusted for age. The HealthCorps program significantly increased all 3 knowledge domains (p < .05), and significantly changed several key behavioral domains. Boys significantly increased fruits/vegetables intake (p = .03). Girls increased acceptance of new fruits/vegetables (p = .03) and breakfast consumption (p = .04), and decreased sugar-sweetened beverages and energy dense food intake (p = .03). The associations between knowledge and behavior were stronger in boys than girls. The HealthCorps program significantly increased participants' knowledge on nutrition, mental health, and physical activity. It also improved several key behavioral domains, which are targets of the 2010 Dietary Guidelines to address obesity in youth. © 2016, American School Health Association.

Children's Comprehension of Object Relative Sentences: It's Extant Language Knowledge That Matters, Not Domain-General Working Memory.

PubMed

Rusli, Yazmin Ahmad; Montgomery, James W

2017-10-17

The aim of this study was to determine whether extant language (lexical) knowledge or domain-general working memory is the better predictor of comprehension of object relative sentences for children with typical development. We hypothesized that extant language knowledge, not domain-general working memory, is the better predictor. Fifty-three children (ages 9-11 years) completed a word-level verbal working-memory task, indexing extant language (lexical) knowledge; an analog nonverbal working-memory task, representing domain-general working memory; and a hybrid sentence comprehension task incorporating elements of both agent selection and cross-modal picture-priming paradigms. Images of the agent and patient were displayed at the syntactic gap in the object relative sentences, and the children were asked to select the agent of the sentence. Results of general linear modeling revealed that extant language knowledge accounted for a unique 21.3% of variance in the children's object relative sentence comprehension over and above age (8.3%). Domain-general working memory accounted for a nonsignificant 1.6% of variance. We interpret the results to suggest that extant language knowledge and not domain-general working memory is a critically important contributor to children's object relative sentence comprehension. Results support a connectionist view of the association between working memory and object relative sentence comprehension. https://doi.org/10.23641/asha.5404573.

A case-based assistant for clinical psychiatry expertise.

PubMed

Bichindaritz, I

1994-01-01

Case-based reasoning is an artificial intelligence methodology for the processing of empirical knowledge. Recent case-based reasoning systems also use theoretic knowledge about the domain to constrain the case-based reasoning. The organization of the memory is the key issue in case-based reasoning. The case-based assistant presented here has two structures in memory: cases and concepts. These memory structures permit it to be as skilled in problem-solving tasks, such as diagnosis and treatment planning, as in interpretive tasks, such as clinical research. A prototype applied to clinical work about eating disorders in psychiatry, reasoning from the alimentary questionnaires of these patients, is presented as an example of the system abilities.

ADO: a disease ontology representing the domain knowledge specific to Alzheimer's disease.

PubMed

Malhotra, Ashutosh; Younesi, Erfan; Gündel, Michaela; Müller, Bernd; Heneka, Michael T; Hofmann-Apitius, Martin

2014-03-01

Biomedical ontologies offer the capability to structure and represent domain-specific knowledge semantically. Disease-specific ontologies can facilitate knowledge exchange across multiple disciplines, and ontology-driven mining approaches can generate great value for modeling disease mechanisms. However, in the case of neurodegenerative diseases such as Alzheimer's disease, there is a lack of formal representation of the relevant knowledge domain. Alzheimer's disease ontology (ADO) is constructed in accordance to the ontology building life cycle. The Protégé OWL editor was used as a tool for building ADO in Ontology Web Language format. ADO was developed with the purpose of containing information relevant to four main biological views-preclinical, clinical, etiological, and molecular/cellular mechanisms-and was enriched by adding synonyms and references. Validation of the lexicalized ontology by means of named entity recognition-based methods showed a satisfactory performance (F score = 72%). In addition to structural and functional evaluation, a clinical expert in the field performed a manual evaluation and curation of ADO. Through integration of ADO into an information retrieval environment, we show that the ontology supports semantic search in scientific text. The usefulness of ADO is authenticated by dedicated use case scenarios. Development of ADO as an open ADO is a first attempt to organize information related to Alzheimer's disease in a formalized, structured manner. We demonstrate that ADO is able to capture both established and scattered knowledge existing in scientific text. Copyright © 2014 The Alzheimer's Association. Published by Elsevier Inc. All rights reserved.

Knowledge discovery about quality of life changes of spinal cord injury patients: clustering based on rules by states.

PubMed

Gibert, Karina; García-Rudolph, Alejandro; Curcoll, Lluïsa; Soler, Dolors; Pla, Laura; Tormos, José María

2009-01-01

In this paper, an integral Knowledge Discovery Methodology, named Clustering based on rules by States, which incorporates artificial intelligence (AI) and statistical methods as well as interpretation-oriented tools, is used for extracting knowledge patterns about the evolution over time of the Quality of Life (QoL) of patients with Spinal Cord Injury. The methodology incorporates the interaction with experts as a crucial element with the clustering methodology to guarantee usefulness of the results. Four typical patterns are discovered by taking into account prior expert knowledge. Several hypotheses are elaborated about the reasons for psychological distress or decreases in QoL of patients over time. The knowledge discovery from data (KDD) approach turns out, once again, to be a suitable formal framework for handling multidimensional complexity of the health domains.

The knowledge translation status in selected Eastern-Mediterranean universities and research institutes.

PubMed

Maleki, Katayoun; Hamadeh, Randah R; Gholami, Jaleh; Mandil, Ahmed; Hamid, Saima; Butt, Zahid Ahmad; Bin Saeed, Abdulaziz; El Kheir, Dalia Y M; Saleem, Mohammed; Maqsoud, Sahar; Safi, Najibullah; Abdul-Majeed, Ban A; Majdzadeh, Reza

2014-01-01

A serious worldwide effort to strengthen research based knowledge translation (KT) has begun in recent years and some countries, particularly developed ones, are trying to incorporate KT in their health and health research systems. Keeping in mind the recent economic depression and the need to perform more efficient research, we aimed to assess and compare the KT status of selected health research institutes in the Eastern Mediterranean Regions' countries, and to identify their strengths and weaknesses in the field. After finding the focal points that would steer the focus group discussions (FGDs) and help complete the 'Self Assessment Tool for Research Institutes' (SATORI) tool, each focal point held two FGDs in which researchers, research authorities and other individuals specified in detail further in the study were held. The scores obtained by each institute were evaluated quantitatively, and the transcriptions were analyzed qualitatively with OpenCode software. For ease of analysis the 50 items of the SATORI were classified into 7 main domains: 'priority setting', 'research quality and timeliness', 'researchers' KT capacities', 'facilities and pre-requisites of KT', 'processes and regulations supporting KT', 'interaction with research users', and 'promoting and evaluating the use of knowledge'. Based on the scoring system, the strongest domain was 'research quality and timeliness'. 'Priority setting' was the weakest domain of all. The remaining domains were more or less equal in strength and were not in a favorable state. The qualitative findings confirmed the quantitative findings. The main problem, it seems, is that a KT climate does not exist in the region. And despite the difference in the contexts, there are many similarities in the region's institutes included in this study. Collaborative efforts can play a role in creating this climate by steering countries towards KT and suggesting regional strategic directions according to their needs.

Impact of Computer Based Online Entrepreneurship Distance Education in India

ERIC Educational Resources Information Center

Shree Ram, Bhagwan; Selvaraj, M.

2012-01-01

The success of Indian enterprises and professionals in the computer and information technology (CIT) domain during the twenty year has been spectacular. Entrepreneurs, bureaucrats and technocrats are now advancing views about how India can ride CIT bandwagon and leapfrog into a knowledge-based economy in the area of entrepreneurship distance…

Making Digital Game-Based Learning Work: Domain Knowledge Transparency

ERIC Educational Resources Information Center

Wang, Feihong; Burton, John K.

2010-01-01

During the past two decades, the popularity of computer and video games has prompted games to be a source of study for educational applications (Dickey, 2007). The most distinguishing characteristic of games is their capability to engage and motivate their players (Kiili, 2005). Educators started to explore game-based learning by testing…

Multilevel semantic analysis and problem-solving in the flight domain

NASA Technical Reports Server (NTRS)

Chien, R. T.; Chen, D. C.; Ho, W. P. C.; Pan, Y. C.

1982-01-01

A computer based cockpit system which is capable of assisting the pilot in such important tasks as monitoring, diagnosis, and trend analysis was developed. The system is properly organized and is endowed with a knowledge base so that it enhances the pilot's control over the aircraft while simultaneously reducing his workload.

Clinical nurse specialist practice domains and evidence-based practice competencies: a matrix of influence.

PubMed

Kring, Daria L

2008-01-01

The purpose of this article is to describe master's-level evidence-based practice (EBP) competencies as determined by a national consensus panel and present an EBP matrix that illustrates the influence that the clinical nurse specialist (CNS) practice can have on driving EBP change. Evidence-based practice is a growing and necessary paradigm for nursing care. The ACE Star Model conceptualizes the knowledge transformation that must occur in an EBP environment as 5 distinct points: discovery, summary, translation, integration, and evaluation. Master's-level EBP competencies based on these 5 steps were established by a national consensus panel. The CNS's practice can be organized around 5 domains: expert practitioner, researcher, consultant, educator, and leader. The master's-level EBP competencies can be transposed on a crosswalk of the ACE Star Model and the 5 CNS practice domains to form a matrix representing the influence that CNSs can have over the EBP process. Each competency falls well within the practice domains of the CNS, making the CNS an ideal person to lead the EBP movement forward, providing tangible outcomes to further demonstrate the need for the CNS role.

Knowledge Base Refinement as Improving an Incorrect and Incomplete Domain Theory

DTIC Science & Technology

1990-04-01

Ginsberg et al., 1985), and RL (Fu and Buchanan, 1985), which perform empirical induction over a library of test cases. This chapter describes a new...state knowledge. Examples of high-level goals are: to test a hypothesis, to differentiate between several plausible hypotheses, to ask a clarifying...one tuple when we Group Hypotheses Test Hypothesis Applyrule Findout Strategy Metarule Strategy Metarule Strategy Metarule Strategy Metarule goal(group

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

NASA Astrophysics Data System (ADS)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

ATK Launch Systems Engineering NASA Programs Engineering Examples

NASA Technical Reports Server (NTRS)

Richardson, David

2007-01-01

This presentation provides an overview of the work done at ATK Launch Systems with and indication of how engineering knowledge can be applied to several real world problems. All material in the presentation has been screened to meet ITAR restrictions. The information provided is a compilation of general engineering knowledge and material available in the public domain. The presentation provides an overview of ATK Launch Systems and NASA programs. Some discussion is provided about the types of engineering conducted at the Promontory plant with added detail about RSRM nozzle engineering. Some brief examples of examples of nozzle technical issues with regard to adhesives and phenolics are shared. These technical issue discussions are based on material available in the public domain.

Two Theories Are Better Than One

NASA Astrophysics Data System (ADS)

Jones, Robert

2008-03-01

All knowledge is of an approximate character (B. Russell, Human Knowledge, 1948, pg 497 and 507). Our formalisms abstract, idealize, and simplify (R. L. Epstein, Propositional Logics, 2001, Ch XI and E. Bender, An Intro. to Math. Modeling, 1978, pg v and 2). Each formalism is an idealization, often times approximating in its own DIFFERENT ways, each offering somewhat different coverage of the domain. Having MULTIPLE overlaping theories of a knowledge domain is then better than having just one theory (R. Jones, APS general meeting, April 2004). Theories are not unique (T. M. Mitchell, Machine Learning, 1997, pg 65-66 and Cooper, Machine Learning, vol. 9, 1992, pg 319). In the future every field will possess multiple theories of its domain and scientific work and engineering will be performed based on the ensemble predictions of ALL of these. In some cases the theories may be quite divergent, differing greatly one from the other. This idea can be considered an extension of Bohr's notion of complementarity, ``...different experimental arrangements...described by different physical concepts...together and only together exhaust the definable information we can obtain about the object.'' (H. J. Folse, The Philosophy of Neils Bohr, 1985, pg 238)

FUTURE APPLICATIONS OF EXPERT SYSTEMS FOR THE EVALUATION OF ENERGY RESOURCES.

USGS Publications Warehouse

Miller, Betty M.

1988-01-01

The loss of professional experience and expertise in the domain of the earth sciences may prove to be one of the most serious outcomes of the boom-and-bust cyclic nature of the volatile energy and mining industries. Promising new applications of powerful computer systems, known as 'expert systems' or 'knowledge-based systems', are predicted for use in the earth science. These systems have the potential capability to capture and preserve the invaluable knowledge bases essential to the evaluation of US energy and mineral resources.

FUTURE APPLICATIONS OF EXPERT SYSTEMS FOR THE EVALUATION OF ENERGY RESOURCES.

USGS Publications Warehouse

Miller, B.M.

1987-01-01

The loss of professional experience and expertise in the domain of the earth sciences may prove to be one of the most serious outcomes of the boom-and-bust cyclic nature of the volatile energy and mining industries. Promising new applications of powerful computer systems, known as 'expert systems' or 'knowledge-based systems', are predicted for use in the earth sciences. These systems have the potential capability to capture and preserve the invaluable knowledge bases essential to the evaluation of the Nation's energy and mineral resources.

Predicting Children's Reading and Mathematics Achievement from Early Quantitative Knowledge and Domain-General Cognitive Abilities

PubMed Central

Chu, Felicia W.; vanMarle, Kristy; Geary, David C.

2016-01-01

One hundred children (44 boys) participated in a 3-year longitudinal study of the development of basic quantitative competencies and the relation between these competencies and later mathematics and reading achievement. The children's preliteracy knowledge, intelligence, executive functions, and parental educational background were also assessed. The quantitative tasks assessed a broad range of symbolic and nonsymbolic knowledge and were administered four times across 2 years of preschool. Mathematics achievement was assessed at the end of each of 2 years of preschool, and mathematics and word reading achievement were assessed at the end of kindergarten. Our goals were to determine how domain-general abilities contribute to growth in children's quantitative knowledge and to determine how domain-general and domain-specific abilities contribute to children's preschool mathematics achievement and kindergarten mathematics and reading achievement. We first identified four core quantitative competencies (e.g., knowledge of the cardinal value of number words) that predict later mathematics achievement. The domain-general abilities were then used to predict growth in these competencies across 2 years of preschool, and the combination of domain-general abilities, preliteracy skills, and core quantitative competencies were used to predict mathematics achievement across preschool and mathematics and word reading achievement at the end of kindergarten. Both intelligence and executive functions predicted growth in the four quantitative competencies, especially across the first year of preschool. A combination of domain-general and domain-specific competencies predicted preschoolers' mathematics achievement, with a trend for domain-specific skills to be more strongly related to achievement at the beginning of preschool than at the end of preschool. Preschool preliteracy skills, sensitivity to the relative quantities of collections of objects, and cardinal knowledge predicted reading and mathematics achievement at the end of kindergarten. Preliteracy skills were more strongly related to word reading, whereas sensitivity to relative quantity was more strongly related to mathematics achievement. The overall results indicate that a combination of domain-general and domain-specific abilities contribute to development of children's early mathematics and reading achievement. PMID:27252675

Predicting Children's Reading and Mathematics Achievement from Early Quantitative Knowledge and Domain-General Cognitive Abilities.

PubMed

Chu, Felicia W; vanMarle, Kristy; Geary, David C

2016-01-01

One hundred children (44 boys) participated in a 3-year longitudinal study of the development of basic quantitative competencies and the relation between these competencies and later mathematics and reading achievement. The children's preliteracy knowledge, intelligence, executive functions, and parental educational background were also assessed. The quantitative tasks assessed a broad range of symbolic and nonsymbolic knowledge and were administered four times across 2 years of preschool. Mathematics achievement was assessed at the end of each of 2 years of preschool, and mathematics and word reading achievement were assessed at the end of kindergarten. Our goals were to determine how domain-general abilities contribute to growth in children's quantitative knowledge and to determine how domain-general and domain-specific abilities contribute to children's preschool mathematics achievement and kindergarten mathematics and reading achievement. We first identified four core quantitative competencies (e.g., knowledge of the cardinal value of number words) that predict later mathematics achievement. The domain-general abilities were then used to predict growth in these competencies across 2 years of preschool, and the combination of domain-general abilities, preliteracy skills, and core quantitative competencies were used to predict mathematics achievement across preschool and mathematics and word reading achievement at the end of kindergarten. Both intelligence and executive functions predicted growth in the four quantitative competencies, especially across the first year of preschool. A combination of domain-general and domain-specific competencies predicted preschoolers' mathematics achievement, with a trend for domain-specific skills to be more strongly related to achievement at the beginning of preschool than at the end of preschool. Preschool preliteracy skills, sensitivity to the relative quantities of collections of objects, and cardinal knowledge predicted reading and mathematics achievement at the end of kindergarten. Preliteracy skills were more strongly related to word reading, whereas sensitivity to relative quantity was more strongly related to mathematics achievement. The overall results indicate that a combination of domain-general and domain-specific abilities contribute to development of children's early mathematics and reading achievement.

A Short Note on Rules and Higher Order Rules.

ERIC Educational Resources Information Center

Scandura, Joseph M.

This brief paper argues that structural analysis--an extended form of cognitive task analysis--demonstrates that both domain dependent and domain independent knowledge can be derived from specific content domains. It is noted that the major difference between the two is that lower order rules (specific knowledge) are derived directly from specific…

Examining the Domain-Specificity of Metacognition Using Academic Domains and Task-Specific Individual Differences

ERIC Educational Resources Information Center

Scott, Brianna M.; Berman, Ashleigh F.

2013-01-01

Metacognition refers to students' knowledge and regulation of cognition, as well as their accuracy in predicting their academic performance. This study addressed two major questions: 1) how do metacognitive knowledge, regulation and accuracy differ across domains?, and 2) how do students' individual differences relate to their reported…

The Influence of Domain Knowledge on the Functional Capacity of Working Memory

ERIC Educational Resources Information Center

Ricks, Travis Rex; Wiley, Jennifer

2009-01-01

Theories of expertise have proposed that superior cognitive performance is in part due to increases in the functional capacity of working memory during domain-related tasks. Consistent with this approach Fincher-Kiefer et al. (1988), found that domain knowledge increased scores on baseball-related reading span tasks. The present studies extended…

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

Exploring dangerous neighborhoods: Latent Semantic Analysis and computing beyond the bounds of the familiar

PubMed Central

Cohen, Trevor; Blatter, Brett; Patel, Vimla

2005-01-01

Certain applications require computer systems to approximate intended human meaning. This is achievable in constrained domains with a finite number of concepts. Areas such as psychiatry, however, draw on concepts from the world-at-large. A knowledge structure with broad scope is required to comprehend such domains. Latent Semantic Analysis (LSA) is an unsupervised corpus-based statistical method that derives quantitative estimates of the similarity between words and documents from their contextual usage statistics. The aim of this research was to evaluate the ability of LSA to derive meaningful associations between concepts relevant to the assessment of dangerousness in psychiatry. An expert reference model of dangerousness was used to guide the construction of a relevant corpus. Derived associations between words in the corpus were evaluated qualitatively. A similarity-based scoring function was used to assign dangerousness categories to discharge summaries. LSA was shown to derive intuitive relationships between concepts and correlated significantly better than random with human categorization of psychiatric discharge summaries according to dangerousness. The use of LSA to derive a simulated knowledge structure can extend the scope of computer systems beyond the boundaries of constrained conceptual domains. PMID:16779020

Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach.

PubMed

Park, Hyunseok; Magee, Christopher L

2017-01-01

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents.

Tracing Technological Development Trajectories: A Genetic Knowledge Persistence-Based Main Path Approach

PubMed Central

2017-01-01

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method can dramatically reduce network complexity without missing any dominantly important patents. The main paths identified by our approach for two test cases are almost 10x less complex than the main paths identified by the existing approach. The proposed approach identifies all dominantly important patents on the main paths, but the main paths identified by the existing approach miss about 20% of dominantly important patents. PMID:28135304

Case-Based Learning with Worked Examples in Complex Domains: Two Experimental Studies in Undergraduate Medical Education

ERIC Educational Resources Information Center

Stark, Robin; Kopp, Veronika; Fischer, Martin R.

2011-01-01

To investigate the effects of example format (erroneous examples vs. correct examples) and feedback format (elaborated feedback vs. knowledge of results feedback) on medical students' diagnostic competence in the context of a web-based learning environment containing case-based worked examples, two studies with a 2 x 2 design were conducted in the…

Domain-Specific Knowledge and General Skills in Reading Comprehension.

ERIC Educational Resources Information Center

Kuhara-Kojima, Keiko; Hatano, Giyoo

A study examined whether the reading comprehension of students with rich domain-specific knowledge will be better than that of students without it and whether assessed general skills will be correlated significantly with reading comprehension performance for students without specific knowledge, but negligible for the students with much specific…

Learning the facts in medical school is not enough: which factors predict successful application of procedural knowledge in a laboratory setting?

PubMed

Schmidmaier, Ralf; Eiber, Stephan; Ebersbach, Rene; Schiller, Miriam; Hege, Inga; Holzer, Matthias; Fischer, Martin R

2013-02-22

Medical knowledge encompasses both conceptual (facts or "what" information) and procedural knowledge ("how" and "why" information). Conceptual knowledge is known to be an essential prerequisite for clinical problem solving. Primarily, medical students learn from textbooks and often struggle with the process of applying their conceptual knowledge to clinical problems. Recent studies address the question of how to foster the acquisition of procedural knowledge and its application in medical education. However, little is known about the factors which predict performance in procedural knowledge tasks. Which additional factors of the learner predict performance in procedural knowledge? Domain specific conceptual knowledge (facts) in clinical nephrology was provided to 80 medical students (3rd to 5th year) using electronic flashcards in a laboratory setting. Learner characteristics were obtained by questionnaires. Procedural knowledge in clinical nephrology was assessed by key feature problems (KFP) and problem solving tasks (PST) reflecting strategic and conditional knowledge, respectively. Results in procedural knowledge tests (KFP and PST) correlated significantly with each other. In univariate analysis, performance in procedural knowledge (sum of KFP+PST) was significantly correlated with the results in (1) the conceptual knowledge test (CKT), (2) the intended future career as hospital based doctor, (3) the duration of clinical clerkships, and (4) the results in the written German National Medical Examination Part I on preclinical subjects (NME-I). After multiple regression analysis only clinical clerkship experience and NME-I performance remained independent influencing factors. Performance in procedural knowledge tests seems independent from the degree of domain specific conceptual knowledge above a certain level. Procedural knowledge may be fostered by clinical experience. More attention should be paid to the interplay of individual clinical clerkship experiences and structured teaching of procedural knowledge and its assessment in medical education curricula.

Memory and Comprehension for Health Information among Older Adults: Distinguishing the Effects of Domain-General and Domain-Specific Knowledge

PubMed Central

Chin, Jessie; Payne, Brennan; Gao, Xuefei; Conner-Garcia, Thembi; Graumlich, James F.; Murray, Michael D.; Morrow, Daniel G.; Stine-Morrow, Elizabeth A.L.

2014-01-01

While there is evidence that knowledge influences understanding of health information, less is known about the processing mechanisms underlying this effect and its impact on memory. We used the moving window paradigm to examine how older adults varying in domain-general crystallized ability (verbal ability) and health knowledge allocate attention to understand health and domain-general texts. Participants (n=107, aged 60 to 88 yrs) read and recalled single sentences about hypertension and about non-health topics. Mixed-effects modeling of word-by-word reading times suggested that domain-general crystallized ability increased conceptual integration regardless of text domain, while health knowledge selectively increased resource allocation to conceptual integration at clause boundaries in health texts. These patterns of attentional allocation were related to subsequent recall performance. Although older adults with lower levels of crystallized ability were less likely to engage in integrative processing, when they did, this strategy had a compensatory effect in improving recall. These findings suggest that semantic integration during reading is an important comprehension process that supports the construction of the memory representation and is engendered by knowledge. Implications of the findings for theories of text processing and memory as well as for designing patient education materials are discussed. PMID:24787361

Memory and comprehension for health information among older adults: distinguishing the effects of domain-general and domain-specific knowledge.

PubMed

Chin, Jessie; Payne, Brennan; Gao, Xuefei; Conner-Garcia, Thembi; Graumlich, James F; Murray, Michael D; Morrow, Daniel G; Stine-Morrow, Elizabeth A L

2015-01-01

While there is evidence that knowledge influences understanding of health information, less is known about the processing mechanisms underlying this effect and its impact on memory. We used the moving window paradigm to examine how older adults varying in domain-general crystallised ability (verbal ability) and health knowledge allocate attention to understand health and domain-general texts. Participants (n = 107, age: 60-88 years) read and recalled single sentences about hypertension and about non-health topics. Mixed-effects modelling of word-by-word reading times suggested that domain-general crystallised ability increased conceptual integration regardless of text domain, while health knowledge selectively increased resource allocation to conceptual integration at clause boundaries in health texts. These patterns of attentional allocation were related to subsequent recall performance. Although older adults with lower levels of crystallised ability were less likely to engage in integrative processing, when they did, this strategy had a compensatory effect in improving recall. These findings suggest that semantic integration during reading is an important comprehension process that supports the construction of the memory representation and is engendered by knowledge. Implications of the findings for theories of text processing and memory as well as for designing patient education materials are discussed.

A protein relational database and protein family knowledge bases to facilitate structure-based design analyses.

PubMed

Mobilio, Dominick; Walker, Gary; Brooijmans, Natasja; Nilakantan, Ramaswamy; Denny, R Aldrin; Dejoannis, Jason; Feyfant, Eric; Kowticwar, Rupesh K; Mankala, Jyoti; Palli, Satish; Punyamantula, Sairam; Tatipally, Maneesh; John, Reji K; Humblet, Christine

2010-08-01

The Protein Data Bank is the most comprehensive source of experimental macromolecular structures. It can, however, be difficult at times to locate relevant structures with the Protein Data Bank search interface. This is particularly true when searching for complexes containing specific interactions between protein and ligand atoms. Moreover, searching within a family of proteins can be tedious. For example, one cannot search for some conserved residue as residue numbers vary across structures. We describe herein three databases, Protein Relational Database, Kinase Knowledge Base, and Matrix Metalloproteinase Knowledge Base, containing protein structures from the Protein Data Bank. In Protein Relational Database, atom-atom distances between protein and ligand have been precalculated allowing for millisecond retrieval based on atom identity and distance constraints. Ring centroids, centroid-centroid and centroid-atom distances and angles have also been included permitting queries for pi-stacking interactions and other structural motifs involving rings. Other geometric features can be searched through the inclusion of residue pair and triplet distances. In Kinase Knowledge Base and Matrix Metalloproteinase Knowledge Base, the catalytic domains have been aligned into common residue numbering schemes. Thus, by searching across Protein Relational Database and Kinase Knowledge Base, one can easily retrieve structures wherein, for example, a ligand of interest is making contact with the gatekeeper residue.

Integration of Basic Knowledge Models for the Simulation of Cereal Foods Processing and Properties.

PubMed

Kristiawan, Magdalena; Kansou, Kamal; Valle, Guy Della

Cereal processing (breadmaking, extrusion, pasting, etc.) covers a range of mechanisms that, despite their diversity, can be often reduced to a succession of two core phenomena: (1) the transition from a divided solid medium (the flour) to a continuous one through hydration, mechanical, biochemical, and thermal actions and (2) the expansion of a continuous matrix toward a porous structure as a result of the growth of bubble nuclei either by yeast fermentation or by water vaporization after a sudden pressure drop. Modeling them is critical for the domain, but can be quite challenging to address with mechanistic approaches relying on partial differential equations. In this chapter we present alternative approaches through basic knowledge models (BKM) that integrate scientific and expert knowledge, and possess operational interest for domain specialists. Using these BKMs, simulations of two cereal foods processes, extrusion and breadmaking, are provided by focusing on the two core phenomena. To support the use by non-specialists, these BKMs are implemented as computer tools, a Knowledge-Based System developed for the modeling of the flour mixing operation or Ludovic ® , a simulation software for twin screw extrusion. They can be applied to a wide domain of compositions, provided that the data on product rheological properties are available. Finally, it is stated that the use of such systems can help food engineers to design cereal food products and predict their texture properties.

A path-oriented matrix-based knowledge representation system

NASA Technical Reports Server (NTRS)

Feyock, Stefan; Karamouzis, Stamos T.

1993-01-01

Experience has shown that designing a good representation is often the key to turning hard problems into simple ones. Most AI (Artificial Intelligence) search/representation techniques are oriented toward an infinite domain of objects and arbitrary relations among them. In reality much of what needs to be represented in AI can be expressed using a finite domain and unary or binary predicates. Well-known vector- and matrix-based representations can efficiently represent finite domains and unary/binary predicates, and allow effective extraction of path information by generalized transitive closure/path matrix computations. In order to avoid space limitations a set of abstract sparse matrix data types was developed along with a set of operations on them. This representation forms the basis of an intelligent information system for representing and manipulating relational data.

Uncertainty management by relaxation of conflicting constraints in production process scheduling

NASA Technical Reports Server (NTRS)

Dorn, Juergen; Slany, Wolfgang; Stary, Christian

1992-01-01

Mathematical-analytical methods as used in Operations Research approaches are often insufficient for scheduling problems. This is due to three reasons: the combinatorial complexity of the search space, conflicting objectives for production optimization, and the uncertainty in the production process. Knowledge-based techniques, especially approximate reasoning and constraint relaxation, are promising ways to overcome these problems. A case study from an industrial CIM environment, namely high-grade steel production, is presented to demonstrate how knowledge-based scheduling with the desired capabilities could work. By using fuzzy set theory, the applied knowledge representation technique covers the uncertainty inherent in the problem domain. Based on this knowledge representation, a classification of jobs according to their importance is defined which is then used for the straightforward generation of a schedule. A control strategy which comprises organizational, spatial, temporal, and chemical constraints is introduced. The strategy supports the dynamic relaxation of conflicting constraints in order to improve tentative schedules.

Cooperative knowledge evolution: a construction-integration approach to knowledge discovery in medicine.

PubMed

Schmalhofer, F J; Tschaitschian, B

1998-11-01

In this paper, we perform a cognitive analysis of knowledge discovery processes. As a result of this analysis, the construction-integration theory is proposed as a general framework for developing cooperative knowledge evolution systems. We thus suggest that for the acquisition of new domain knowledge in medicine, one should first construct pluralistic views on a given topic which may contain inconsistencies as well as redundancies. Only thereafter does this knowledge become consolidated into a situation-specific circumscription and the early inconsistencies become eliminated. As a proof for the viability of such knowledge acquisition processes in medicine, we present the IDEAS system, which can be used for the intelligent documentation of adverse events in clinical studies. This system provides a better documentation of the side-effects of medical drugs. Thereby, knowledge evolution occurs by achieving consistent explanations in increasingly larger contexts (i.e., more cases and more pharmaceutical substrates). Finally, it is shown how prototypes, model-based approaches and cooperative knowledge evolution systems can be distinguished as different classes of knowledge-based systems.

Exploring the knowledge behind predictions in everyday cognition: an iterated learning study.

PubMed

Stephens, Rachel G; Dunn, John C; Rao, Li-Lin; Li, Shu

2015-10-01

Making accurate predictions about events is an important but difficult task. Recent work suggests that people are adept at this task, making predictions that reflect surprisingly accurate knowledge of the distributions of real quantities. Across three experiments, we used an iterated learning procedure to explore the basis of this knowledge: to what extent is domain experience critical to accurate predictions and how accurate are people when faced with unfamiliar domains? In Experiment 1, two groups of participants, one resident in Australia, the other in China, predicted the values of quantities familiar to both (movie run-times), unfamiliar to both (the lengths of Pharaoh reigns), and familiar to one but unfamiliar to the other (cake baking durations and the lengths of Beijing bus routes). While predictions from both groups were reasonably accurate overall, predictions were inaccurate in the selectively unfamiliar domains and, surprisingly, predictions by the China-resident group were also inaccurate for a highly familiar domain: local bus route lengths. Focusing on bus routes, two follow-up experiments with Australia-resident groups clarified the knowledge and strategies that people draw upon, plus important determinants of accurate predictions. For unfamiliar domains, people appear to rely on extrapolating from (not simply directly applying) related knowledge. However, we show that people's predictions are subject to two sources of error: in the estimation of quantities in a familiar domain and extension to plausible values in an unfamiliar domain. We propose that the key to successful predictions is not simply domain experience itself, but explicit experience of relevant quantities.

SIRE: A Simple Interactive Rule Editor for NICBES

NASA Technical Reports Server (NTRS)

Bykat, Alex

1988-01-01

To support evolution of domain expertise, and its representation in an expert system knowledge base, a user-friendly rule base editor is mandatory. The Nickel Cadmium Battery Expert System (NICBES), a prototype of an expert system for the Hubble Space Telescope power storage management system, does not provide such an editor. In the following, a description of a Simple Interactive Rule Base Editor (SIRE) for NICBES is described. The SIRE provides a consistent internal representation of the NICBES knowledge base. It supports knowledge presentation and provides a user-friendly and code language independent medium for rule addition and modification. The SIRE is integrated with NICBES via an interface module. This module provides translation of the internal representation to Prolog-type rules (Horn clauses), latter rule assertion, and a simple mechanism for rule selection for its Prolog inference engine.

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.

Crowdsourcing Knowledge Discovery and Innovations in Medicine

PubMed Central

2014-01-01

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. “Health hackathons” and “data marathons”, in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled. PMID:25239002

Crowdsourcing knowledge discovery and innovations in medicine.

PubMed

Celi, Leo Anthony; Ippolito, Andrea; Montgomery, Robert A; Moses, Christopher; Stone, David J

2014-09-19

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. "Health hackathons" and "data marathons", in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled.

The CompHP core competencies framework for health promotion in Europe.

PubMed

Barry, Margaret M; Battel-Kirk, Barbara; Dempsey, Colette

2012-12-01

The CompHP Project on Developing Competencies and Professional Standards for Health Promotion in Europe was developed in response to the need for new and changing health promotion competencies to address health challenges. This article presents the process of developing the CompHP Core Competencies Framework for Health Promotion across the European Union Member States and Candidate Countries. A phased, multiple-method approach was employed to facilitate a consensus-building process on the development of the core competencies. Key stakeholders in European health promotion were engaged in a layered consultation process using the Delphi technique, online consultations, workshops, and focus groups. Based on an extensive literature review, a mapping process was used to identify the core domains, which informed the first draft of the Framework. A consultation process involving two rounds of a Delphi survey with national experts in health promotion from 30 countries was carried out. In addition, feedback was received from 25 health promotion leaders who participated in two focus groups at a pan-European level and 116 health promotion practitioners who engaged in four country-specific consultations. A further 54 respondents replied to online consultations, and there were a number of followers on various social media platforms. Based on four rounds of redrafting, the final Framework document was produced, consisting of 11 core domains and 68 core competency statements. The CompHP Core Competencies Framework for Health Promotion provides a resource for workforce development in Europe, by articulating the necessary knowledge, skills, and abilities that are required for effective practice. The core domains are based on the multidisciplinary concepts, theories, and research that make health promotion distinctive. It is the combined application of all the domains, the knowledge base, and the ethical values that constitute the CompHP Core Competencies Framework for Health Promotion.

Structure-based multiscale approach for identification of interaction partners of PDZ domains.

PubMed

Tiwari, Garima; Mohanty, Debasisa

2014-04-28

PDZ domains are peptide recognition modules which mediate specific protein-protein interactions and are known to have a complex specificity landscape. We have developed a novel structure-based multiscale approach which identifies crucial specificity determining residues (SDRs) of PDZ domains from explicit solvent molecular dynamics (MD) simulations on PDZ-peptide complexes and uses these SDRs in combination with knowledge-based scoring functions for proteomewide identification of their interaction partners. Multiple explicit solvent simulations ranging from 5 to 50 ns duration have been carried out on 28 PDZ-peptide complexes with known binding affinities. MM/PBSA binding energy values calculated from these simulations show a correlation coefficient of 0.755 with the experimental binding affinities. On the basis of the SDRs of PDZ domains identified by MD simulations, we have developed a simple scoring scheme for evaluating binding energies for PDZ-peptide complexes using residue based statistical pair potentials. This multiscale approach has been benchmarked on a mouse PDZ proteome array data set by calculating the binding energies for 217 different substrate peptides in binding pockets of 64 different mouse PDZ domains. Receiver operating characteristic (ROC) curve analysis indicates that, the area under curve (AUC) values for binder vs nonbinder classification by our structure based method is 0.780. Our structure based method does not require experimental PDZ-peptide binding data for training.

eClims: An Extensible and Dynamic Integration Framework for Biomedical Information Systems.

PubMed

Savonnet, Marinette; Leclercq, Eric; Naubourg, Pierre

2016-11-01

Biomedical information systems (BIS) require consideration of three types of variability: data variability induced by new high throughput technologies, schema or model variability induced by large scale studies or new fields of research, and knowledge variability resulting from new discoveries. Beyond data heterogeneity, managing variabilities in the context of BIS requires extensible and dynamic integration process. In this paper, we focus on data and schema variabilities and we propose an integration framework based on ontologies, master data, and semantic annotations. The framework addresses issues related to: 1) collaborative work through a dynamic integration process; 2) variability among studies using an annotation mechanism; and 3) quality control over data and semantic annotations. Our approach relies on two levels of knowledge: BIS-related knowledge is modeled using an application ontology coupled with UML models that allow controlling data completeness and consistency, and domain knowledge is described by a domain ontology, which ensures data coherence. A system build with the eClims framework has been implemented and evaluated in the context of a proteomic platform.

The identification of knowledge content and function in manual labour.

PubMed

Shalin, Valerie; Verdile, Charles

2003-06-10

Calls for an alternative conceptualization of cognition for applied concerns retain the core commitment of the basic research community to abstract cognition detached from a physical environment. The present paper attempts to break out of the dominant, narrow view of cognition and cognitive domains, with a cognitive analysis of digging ditches for the utility industry. To illustrate knowledge-based cognition in manual labour excerpts are presented from the journal entries of a moderately experienced student working a summer job, organized with a representation that distinguishes between the goals and methods of work. The journal entries illustrate the functions of knowledge for interacting with a physical environment; knowledge enables the selection, execution and monitoring of work methods, the interpretation of perceptual information, the application of task completion criteria and the ability for explanation and generalization. To emphasize the generality of the functions of cognition in ditch digging, comparable functions are indicated in a domain rarely regarded as a form of manual labour: the practice of internal medicine. Discussion of the results includes the implications for cognitive theory as well as practical implications for productivity, training and task analysis.

Modeling software systems by domains

NASA Technical Reports Server (NTRS)

Dippolito, Richard; Lee, Kenneth

1992-01-01

The Software Architectures Engineering (SAE) Project at the Software Engineering Institute (SEI) has developed engineering modeling techniques that both reduce the complexity of software for domain-specific computer systems and result in systems that are easier to build and maintain. These techniques allow maximum freedom for system developers to apply their domain expertise to software. We have applied these techniques to several types of applications, including training simulators operating in real time, engineering simulators operating in non-real time, and real-time embedded computer systems. Our modeling techniques result in software that mirrors both the complexity of the application and the domain knowledge requirements. We submit that the proper measure of software complexity reflects neither the number of software component units nor the code count, but the locus of and amount of domain knowledge. As a result of using these techniques, domain knowledge is isolated by fields of engineering expertise and removed from the concern of the software engineer. In this paper, we will describe kinds of domain expertise, describe engineering by domains, and provide relevant examples of software developed for simulator applications using the techniques.

Barriers and facilitators to preventing pressure ulcers in nursing home residents: A qualitative analysis informed by the Theoretical Domains Framework.

PubMed

Lavallée, Jacqueline F; Gray, Trish A; Dumville, Jo; Cullum, Nicky

2018-06-01

Pressure ulcers are areas of localised damage to the skin and underlying tissue; and can cause pain, immobility, and delay recovery, impacting on health-related quality of life. The individuals who are most at risk of developing a pressure ulcer are those who are seriously ill, elderly, have impaired mobility and/or poor nutrition; thus, many nursing home residents are at risk. To understand the context of pressure ulcer prevention in nursing homes and to explore the potential barriers and facilitators to evidence-informed practices. Semi-structured interviews were conducted with nursing home nurses, healthcare assistants and managers, National Health Service community-based wound specialist nurses (known in the UK as tissue viability nurses) and a nurse manager in the North West of England. The interview guide was developed using the Theoretical Domains Framework to explore the barriers and facilitators to pressure ulcer prevention in nursing home residents. Data were analysed using a framework analysis and domains were identified as salient based on their frequency and the potential strength of their impact. 25 participants (nursing home: 2 managers, 7 healthcare assistants, 11 qualified nurses; National Health Service community services: 4 tissue viability nurses, 1 manager) were interviewed. Depending upon the behaviours reported and the context, the same domain could be classified as both a barrier and a facilitator. We identified seven domains as relevant in the prevention of pressure ulcers in nursing home residents mapping to four "barrier" domains and six "facilitator" domains. The four "barrier" domains were knowledge, physical skills, social influences and environmental context and resources and the six "facilitator" domains were interpersonal skills, environmental context and resources, social influences, beliefs about capabilities, beliefs about consequences and social/professional role and identity). Knowledge and insight into these barriers and facilitators provide a theoretical understanding of the complexities in preventing pressure ulcers with reference to the staff capabilities, opportunities and motivation related to pressure ulcer prevention. Pressure ulcer prevention in nursing home residents is complex and is influenced by several factors. The findings will inform a theory and evidence-based intervention to aid the prevention of pressure ulcers in nursing home settings. Copyright © 2018 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Knowledge representation for commonality

NASA Technical Reports Server (NTRS)

Yeager, Dorian P.

1990-01-01

Domain-specific knowledge necessary for commonality analysis falls into two general classes: commonality constraints and costing information. Notations for encoding such knowledge should be powerful and flexible and should appeal to the domain expert. The notations employed by the Commonality Analysis Problem Solver (CAPS) analysis tool are described. Examples are given to illustrate the main concepts.

Memetic Algorithms, Domain Knowledge, and Financial Investing

ERIC Educational Resources Information Center

Du, Jie

2012-01-01

While the question of how to use human knowledge to guide evolutionary search is long-recognized, much remains to be done to answer this question adequately. This dissertation aims to further answer this question by exploring the role of domain knowledge in evolutionary computation as applied to real-world, complex problems, such as financial…

Representation and matching of knowledge to design digital systems

NASA Technical Reports Server (NTRS)

Jones, J. U.; Shiva, S. G.

1988-01-01

A knowledge-based expert system is described that provides an approach to solve a problem requiring an expert with considerable domain expertise and facts about available digital hardware building blocks. To design digital hardware systems from their high level VHDL (Very High Speed Integrated Circuit Hardware Description Language) representation to their finished form, a special data representation is required. This data representation as well as the functioning of the overall system is described.

Framing a Knowledge Base for a Legal Expert System Dealing with Indeterminate Concepts.

PubMed

Araszkiewicz, Michał; Łopatkiewicz, Agata; Zienkiewicz, Adam; Zurek, Tomasz

2015-01-01

Despite decades of development of formal tools for modelling legal knowledge and reasoning, the creation of a fully fledged legal decision support system remains challenging. Among those challenges, such system requires an enormous amount of commonsense knowledge to derive legal expertise. This paper describes the development of a negotiation decision support system (the Parenting Plan Support System or PPSS) to support parents in drafting an agreement (the parenting plan) for the exercise of parental custody of minor children after a divorce is granted. The main objective here is to discuss problems of framing an intuitively appealing and computationally efficient knowledge base that can adequately represent the indeterminate legal concept of the well-being of the child in the context of continental legal culture and of Polish law in particular. In addition to commonsense reasoning, interpretation of such a concept demands both legal expertise and significant professional knowledge from other domains.

Framing a Knowledge Base for a Legal Expert System Dealing with Indeterminate Concepts

PubMed Central

Araszkiewicz, Michał; Łopatkiewicz, Agata; Zienkiewicz, Adam

2015-01-01

Despite decades of development of formal tools for modelling legal knowledge and reasoning, the creation of a fully fledged legal decision support system remains challenging. Among those challenges, such system requires an enormous amount of commonsense knowledge to derive legal expertise. This paper describes the development of a negotiation decision support system (the Parenting Plan Support System or PPSS) to support parents in drafting an agreement (the parenting plan) for the exercise of parental custody of minor children after a divorce is granted. The main objective here is to discuss problems of framing an intuitively appealing and computationally efficient knowledge base that can adequately represent the indeterminate legal concept of the well-being of the child in the context of continental legal culture and of Polish law in particular. In addition to commonsense reasoning, interpretation of such a concept demands both legal expertise and significant professional knowledge from other domains. PMID:26495435

Educational Application of Dialogue System To Support e-Learning.

ERIC Educational Resources Information Center

Kim, Youn-Gi; Lee, Chul-Hwan; Han, Sun-Gwan

This study is on the design and implementation of an educational dialogue system to support e-learning. The learning domain to apply the dialogue system used the subject of geometry. The knowledge in the dialogue-based system for learning geometry was created and represented by XML-based AIML. The implemented system in this study can understand…

Teaching with Game-Based Learning Management Systems: Exploring a Pedagogical Dungeon

ERIC Educational Resources Information Center

Carron, Thibault; Marty, Jean-Charles; Heraud, Jean-Mathias

2008-01-01

The work reported here takes place in the educational domain. The authors propose a learning environment based on a graphical representation of a course. The emergence of online multiplayer games led the authors to apply the following metaphor to the digital work environments: The method of acquiring knowledge during a learning session is similar…

The Role of Domain Knowledge in Creative Generation

ERIC Educational Resources Information Center

Ward, Thomas B.

2008-01-01

Previous studies have shown that a predominant tendency in creative generation tasks is to base new ideas on well-known, specific instances of previous ideas (e.g., basing ideas for imaginary aliens on dogs, cats or bears). However, a substantial minority of individuals has been shown to adopt more abstract approaches to the task and to develop…

Accelerating Preschoolers' Early Literacy Development through Classroom-Based Teacher-Child Storybook Reading and Explicit Print Referencing

ERIC Educational Resources Information Center

Justice, Laura M.; Kaderavek, Joan N.; Fan, Xitao; Sofka, Amy; Hunt, Aileen

2009-01-01

Purpose: This study examined the impact of teacher use of a print referencing style during classroom-based storybook reading sessions conducted over an academic year. Impacts on preschoolers' early literacy development were examined, focusing specifically on the domain of print knowledge. Method: This randomized, controlled trial examined the…

Organizing knowledge for tutoring fire loss prevention

NASA Astrophysics Data System (ADS)

Schmoldt, Daniel L.

1989-09-01

The San Bernardino National Forest in southern California has recently developed a systematic approach to wildfire prevention planning. However, a comprehensive document or other mechanism for teaching this process to other prevention personnel does not exist. An intelligent tutorial expert system is being constructed to provide a means for learning the process and to assist in the creation of specific prevention plans. An intelligent tutoring system (ITS) contains two types of knowledge—domain and tutoring. The domain knowledge for wildfire prevention is structured around several foci: (1) individual concepts used in prevention planning; (2) explicitly specified interrelationships between concepts; (3) deductive methods that contain subjective judgment normally unavailable to less-experienced users; (4) analytical models of fire behavior used for identification of hazard areas; (5) how-to guidance needed for performance of planning tasks; and (6) expository information that provides a rationale for planning steps and ideas. Combining analytical, procedure, inferential, conceptual, and expositional knowledge into a tutoring environment provides the student and/or user with a multiple perspective of the subject matter. A concept network provides a unifying framework for structuring and utilizing these diverse forms of prevention planning knowledge. This network structure borrows from and combines semantic networks and frame-based knowledge representations. The flexibility of this organization facilitates an effective synthesis and organization of multiple knowledge forms.

The Case for Case-Based Transfer Learning

DTIC Science & Technology

2011-01-01

Thorndike and Woodworth 1901; Perkins and Salomon 1994; Bransford, Brown, and Cocking 2000), among other disciplines. Transfer learning uses knowledge...Transfer Learning for Rein- forcement Learning Domains: A Survey. Journal of Machine Learning Research 10(1): 1633–1685. Thorndike , E. L., and

Goal-Based Domain Modeling as a Basis for Cross-Disciplinary Systems Engineering

NASA Astrophysics Data System (ADS)

Jarke, Matthias; Nissen, Hans W.; Rose, Thomas; Schmitz, Dominik

Small and medium-sized enterprises (SMEs) are important drivers for innovation. In particular, project-driven SMEs that closely cooperate with their customers have specific needs in regard to information engineering of their development process. They need a fast requirements capture since this is most often included in the (unpaid) offer development phase. At the same time, they need to maintain and reuse the knowledge and experiences they have gathered in previous projects extensively as it is their core asset. The situation is complicated further if the application field crosses disciplinary boundaries. To bridge the gaps and perspectives, we focus on shared goals and dependencies captured in models at a conceptual level. Such a model-based approach also offers a smarter connection to subsequent development stages, including a high share of automated code generation. In the approach presented here, the agent- and goal-oriented formalism i * is therefore extended by domain models to facilitate information organization. This extension permits a domain model-based similarity search, and a model-based transformation towards subsequent development stages. Our approach also addresses the evolution of domain models reflecting the experiences from completed projects. The approach is illustrated with a case study on software-intensive control systems in an SME of the automotive domain.

Temporal and contextual knowledge in model-based expert systems

NASA Technical Reports Server (NTRS)

Toth-Fejel, Tihamer; Heher, Dennis

1987-01-01

A basic paradigm that allows representation of physical systems with a focus on context and time is presented. Paragon provides the capability to quickly capture an expert's knowledge in a cognitively resonant manner. From that description, Paragon creates a simulation model in LISP, which when executed, verifies that the domain expert did not make any mistakes. The Achille's heel of rule-based systems has been the lack of a systematic methodology for testing, and Paragon's developers are certain that the model-based approach overcomes that problem. The reason this testing is now possible is that software, which is very difficult to test, has in essence been transformed into hardware.

General and Domain-Specific Contributions to Creative Ideation and Creative Performance

PubMed Central

An, Donggun; Runco, Mark A.

2016-01-01

The general objective of this study was to reexamine two views of creativity, one positing that there is a general creative capacity or talent and the other that creativity is domain-specific. These two views were compared by (a) testing correlations among measures of domain-general and domain-specific creativity and (b) examining how the general and the specific measures was each related to indices of knowledge, motivation, and personality. Participants were 147 college students enrolled in a foreign language course. Data were collected on participants’ domain knowledge, motivation, and creative personality, as well as four measures representing “General or Domain-Specific Creative Ideation” or “Creative Performance and Activity”. Results indicated that the four measures of creativity were correlated with one another, except for “General Performance and Activity” and “Domain-Specific Ideation.” A canonical correlation indicated that knowledge, motivation, and personality were significantly correlated with the four creativity measures (Rc = .49, p < .01). Multiple regressions uncovered particular relationships consistent with the view that creativity has both general and domain-specific contributions. Limitations, such as the focus on one domain, and future directions are discussed. PMID:27872664

Advancing interprofessional patient safety education for medical, nursing, and pharmacy learners during clinical rotations.

PubMed

Thom, Kerri A; Heil, Emily L; Croft, Lindsay D; Duffy, Alison; Morgan, Daniel J; Johantgen, Mary

2016-11-01

Clinical errors are common and can lead to adverse events and patient death. Health professionals must work within interprofessional teams to provide safe and effective care to patients, yet current curricula is lacking with regards to interprofessional education and patient safety. We describe the development and implementation of an interprofessional course aimed at medical, nursing, and pharmacy learners during their clinical training at a large academic medical centre. The course objectives were based on core competencies for interprofessional education and patient safety. The course was offered as recurring three 1-hour sessions, including case-based discussions and a mock root cause analysis. Forty-three students attended at least one session over a 7-month period. We performed a cross-sectional survey of participants to assess readiness for interprofessional learning and a before and after comparison of patient safety knowledge. All students reported a high level of readiness for interprofessional learning, indicating an interest in interprofessional opportunities. In general, understanding and knowledge of the four competency domains in patient safety was low before the course and 100% of students reported an increase in knowledge in these domains after participating in the course.

Disciplinary knowledge of K-3 teachers and their knowledge calibration in the domain of early literacy.

PubMed

Cunningham, Anne E; Perry, Kathryn E; Stanovich, Keith E; Stanovich, Paula J

2004-06-01

Recently, investigators have begun to pay increasing attention to the role of teachers' domain specific knowledge in the area of reading, and its implications for both classroom practice and student learning. The aims of the present study were to assess kindergarten to third-grade teachers' actual and perceived reading-related subject matter knowledge, and to investigate the extent to which teachers calibrate their reading related subject matter knowledge by examining relationships between actual and perceived knowledge. Results indicated that while teachers demonstrated limited knowledge of children's literature, phoneme awareness, and phonics, the majority of these same teachers evaluated their knowledge levels quite positively. Teachers demonstrated some ability to calibrate their own knowledge levels in the area of children's literature, yet they were poorly calibrated in the domains of phoneme awareness and phonics. These findings suggest that teachers tend to overestimate their reading related subject matter knowledge and are often unaware of what they know and do not know. Implications for the design of teacher education at both the preservice and inservice levels are discussed.

Text-based discovery in biomedicine: the architecture of the DAD-system.

PubMed

Weeber, M; Klein, H; Aronson, A R; Mork, J G; de Jong-van den Berg, L T; Vos, R

2000-01-01

Current scientific research takes place in highly specialized contexts with poor communication between disciplines as a likely consequence. Knowledge from one discipline may be useful for the other without researchers knowing it. As scientific publications are a condensation of this knowledge, literature-based discovery tools may help the individual scientist to explore new useful domains. We report on the development of the DAD-system, a concept-based Natural Language Processing system for PubMed citations that provides the biomedical researcher such a tool. We describe the general architecture and illustrate its operation by a simulation of a well-known text-based discovery: The favorable effects of fish oil on patients suffering from Raynaud's disease [1].

Knowledge-based processing for aircraft flight control

NASA Technical Reports Server (NTRS)

Painter, John H.; Glass, Emily; Economides, Gregory; Russell, Paul

1994-01-01

This Contractor Report documents research in Intelligent Control using knowledge-based processing in a manner dual to methods found in the classic stochastic decision, estimation, and control discipline. Such knowledge-based control has also been called Declarative, and Hybid. Software architectures were sought, employing the parallelism inherent in modern object-oriented modeling and programming. The viewpoint adopted was that Intelligent Control employs a class of domain-specific software architectures having features common over a broad variety of implementations, such as management of aircraft flight, power distribution, etc. As much attention was paid to software engineering issues as to artificial intelligence and control issues. This research considered that particular processing methods from the stochastic and knowledge-based worlds are duals, that is, similar in a broad context. They provide architectural design concepts which serve as bridges between the disparate disciplines of decision, estimation, control, and artificial intelligence. This research was applied to the control of a subsonic transport aircraft in the airport terminal area.

Social exclusion of older persons: a scoping review and conceptual framework.

PubMed

Walsh, Kieran; Scharf, Thomas; Keating, Norah

2017-03-01

As a concept, social exclusion has considerable potential to explain and respond to disadvantage in later life. However, in the context of ageing populations, the construct remains ambiguous. A disjointed evidence-base, spread across disparate disciplines, compounds the challenge of developing a coherent understanding of exclusion in older age. This article addresses this research deficit by presenting the findings of a two-stage scoping review encompassing seven separate reviews of the international literature pertaining to old-age social exclusion. Stage one involved a review of conceptual frameworks on old-age exclusion, identifying conceptual understandings and key domains of later-life exclusion. Stage two involved scoping reviews on each domain (six in all). Stage one identified six conceptual frameworks on old-age exclusion and six common domains across these frameworks: neighbourhood and community; services, amenities and mobility; social relations; material and financial resources; socio-cultural aspects; and civic participation. International literature concentrated on the first four domains, but indicated a general lack of research knowledge and of theoretical development. Drawing on all seven scoping reviews and a knowledge synthesis, the article presents a new definition and conceptual framework relating to old-age exclusion.

Theory and ontology for sharing temporal knowledge

NASA Technical Reports Server (NTRS)

Loganantharaj, Rasiah

1996-01-01

Using current technology, the sharing or re-using of knowledge-bases is very difficult, if not impossible. ARPA has correctly recognized the problem and funded a knowledge sharing initiative. One of the outcomes of this project is a formal language called Knowledge Interchange Format (KIF) for representing knowledge that could be translated into other languages. Capturing and representing design knowledge and reasoning with them have become very important for NASA who is a pioneer of innovative design of unique products. For upgrading an existing design for changing technology, needs, or requirements, it is essential to understand the design rationale, design choices, options and other relevant information associated with the design. Capturing such information and presenting them in the appropriate form are part of the ongoing Design Knowledge Capture project of NASA. The behavior of an object and various other aspects related to time are captured by the appropriate temporal knowledge. The captured design knowledge will be represented in such a way that various groups of NASA who are interested in various aspects of the design cycle should be able to access and use the design knowledge effectively. To facilitate knowledge sharing among these groups, one has to develop a very well defined ontology. Ontology is a specification of conceptualization. In the literature several specific domains were studied and some well defined ontologies were developed for such domains. However, very little, or no work has been done in the area of representing temporal knowledge to facilitate sharing. During the ASEE summer program, I have investigated several temporal models and have proposed a theory for time that is flexible to accommodate the time elements, such as, points and intervals, and is capable of handling the qualitative and quantitative temporal constraints. I have also proposed a primitive temporal ontology using which other relevant temporal ontologies can be built. I have investigated various issues of sharing knowledge and have proposed a formal framework for modeling the concept of knowledge sharing. This work may be implemented and tested in the software environment supplied by Knowledge Based System, Inc.

Requirements and Solutions for Personalized Health Systems.

PubMed

Blobel, Bernd; Ruotsalainen, Pekka; Lopez, Diego M; Oemig, Frank

2017-01-01

Organizational, methodological and technological paradigm changes enable a precise, personalized, predictive, preventive and participative approach to health and social services supported by multiple actors from different domains at diverse level of knowledge and skills. Interoperability has to advance beyond Information and Communication Technologies (ICT) concerns, including the real world business domains and their processes, but also the individual context of all actors involved. The paper introduces and compares personalized health definitions, summarizes requirements and principles for pHealth systems, and considers intelligent interoperability. It addresses knowledge representation and harmonization, decision intelligence, and usability as crucial issues in pHealth. On this basis, a system-theoretical, ontology-based, policy-driven reference architecture model for open and intelligent pHealth ecosystems and its transformation into an appropriate ICT design and implementation is proposed.

Training Peer-Feedback Skills on Geometric Construction Tasks: Role of Domain Knowledge and Peer-Feedback Levels

ERIC Educational Resources Information Center

Alqassab, Maryam; Strijbos, Jan-Willem; Ufer, Stefan

2018-01-01

Peer feedback is widely used to train assessment skills and to support collaborative learning of various learning tasks, but research on peer feedback in the domain of mathematics is limited. Although domain knowledge seems to be a prerequisite for peer-feedback provision, it only recently received attention in the peer-feedback literature. In…

Contributions of Domain-General Cognitive Resources and Different Forms of Arithmetic Development to Pre-Algebraic Knowledge

ERIC Educational Resources Information Center

Fuchs, Lynn S.; Compton, Donald L.; Fuchs, Douglas; Powell, Sarah R.; Schumacher, Robin F.; Hamlett, Carol L.; Vernier, Emily; Namkung, Jessica M.; Vukovic, Rose K.

2012-01-01

The purpose of this study was to investigate the contributions of domain-general cognitive resources and different forms of arithmetic development to individual differences in pre-algebraic knowledge. Children (n = 279, mean age = 7.59 years) were assessed on 7 domain-general cognitive resources as well as arithmetic calculations and word problems…

Vulnerable Women’s Self-Care Needs in Knowledge, Attitude and Practice Concerning Sexually Transmitted Diseases

PubMed Central

Alimohammadi, Nasrollah; Baghersad, Zahra; Boroumandfar, Zahra

2016-01-01

Background: Vulnerable women are prone to sexually transmitted diseases (STD) due to their special conditions and poor knowledge about these diseases in the society. Therefore, the present study aimed to determine the vulnerable women’s self-care needs in knowledge, attitude and practice concerning STD. Methods: This is a cross-sectional-descriptive study conducted in 2014. The data collection was carried out using a self-administered structured questionnaire. 120 vulnerable women referring to centers affiliated to health and well-being center in Isfahan participated in this study. They were selected through proportional rationing sampling and filled out a researcher developed questionnaire containing information on personal characteristics, self-care knowledge, attitude, and practice needs toward the STD. The data were analyzed using statistical methods including Spearman & Pearson correlation co-efficient, independent t-test and ANOVA. All analyses were carried out using SPSS, 20. Results: Based on the results, most of the subjects mentioned that their priorities of self-care needs in domains of knowledge, attitude and practice were “familiarization with the types and contamination ways of sexually transmitted diseases” (57.9%); “diagnosis of STD only makes us anxious” (24.8), and “the method of washing the genital area before and after intercourse” 41.3%), respectively. There was a significant association among marital status, education, history of addiction, and self-care needs in domains of knowledge, attitude and practice (P<0.05). Conclusion: Results showed that vulnerable women not only knew their need about STD, but also paid attention to their attitude and practice needs toward STD. Therefore, educational programs should be designed and administrated by the experts, based on vulnerable women’s self-care needs concerning their knowledge, attitude and practice to prevent and control STD in vulnerable individuals. PMID:27382588

Outcome and Impact Evaluation of a Transgender Health Course for Health Profession Students.

PubMed

Braun, Hannan M; Garcia-Grossman, Ilana R; Quiñones-Rivera, Andrea; Deutsch, Madeline B

2017-02-01

Being transgender is associated with numerous health disparities, and transgender individuals face mistreatment and discrimination in healthcare settings. At the same time, healthcare professionals report inadequate preparation to care for transgender people, and patients often have to teach their own medical providers about transgender care. Our study aimed to evaluate the impact of an elective course for health profession students in transgender health that was implemented to address these gaps in provider knowledge. Students participated in a 10-session, lunch-hour elective course during the spring of 2015. To evaluate impact, course participants completed pre-, immediately post-, and 3-month postcourse questionnaires, including a previously validated nine-item transphobia scale, to determine the course's effect on knowledge, attitudes, and beliefs about transgender health. Forty-six students completed the pre- and immediately postelective questionnaire (74% response rate). Compared with pre-elective surveys, immediately postelective scores demonstrated increased knowledge in most domains and reduced transphobia. Specific knowledge domains with improvements included terminology, best practices for collecting gender identity, awareness of the DSM-V gender dysphoria diagnosis, medications used for gender affirmation, and relevant federal policies. A previously validated transphobia scale was found to have good reliability in the current sample. This elective course led to positive short-term changes in measures of multiple knowledge domains and reduced measures of transphobia among health profession students. Further study is needed to assess the long-term impact. Our methods and findings, including the demonstration of reliability of a previously validated nine-item transphobia scale, serve as formative data for the future development of theory-based transgender medicine curricula and measures.

Building a semi-automatic ontology learning and construction system for geosciences

NASA Astrophysics Data System (ADS)

Babaie, H. A.; Sunderraman, R.; Zhu, Y.

2013-12-01

We are developing an ontology learning and construction framework that allows continuous, semi-automatic knowledge extraction, verification, validation, and maintenance by potentially a very large group of collaborating domain experts in any geosciences field. The system brings geoscientists from the side-lines to the center stage of ontology building, allowing them to collaboratively construct and enrich new ontologies, and merge, align, and integrate existing ontologies and tools. These constantly evolving ontologies can more effectively address community's interests, purposes, tools, and change. The goal is to minimize the cost and time of building ontologies, and maximize the quality, usability, and adoption of ontologies by the community. Our system will be a domain-independent ontology learning framework that applies natural language processing, allowing users to enter their ontology in a semi-structured form, and a combined Semantic Web and Social Web approach that lets direct participation of geoscientists who have no skill in the design and development of their domain ontologies. A controlled natural language (CNL) interface and an integrated authoring and editing tool automatically convert syntactically correct CNL text into formal OWL constructs. The WebProtege-based system will allow a potentially large group of geoscientists, from multiple domains, to crowd source and participate in the structuring of their knowledge model by sharing their knowledge through critiquing, testing, verifying, adopting, and updating of the concept models (ontologies). We will use cloud storage for all data and knowledge base components of the system, such as users, domain ontologies, discussion forums, and semantic wikis that can be accessed and queried by geoscientists in each domain. We will use NoSQL databases such as MongoDB as a service in the cloud environment. MongoDB uses the lightweight JSON format, which makes it convenient and easy to build Web applications using just HTML5 and Javascript, thereby avoiding cumbersome server side coding present in the traditional approaches. The JSON format used in MongoDB is also suitable for storing and querying RDF data. We will store the domain ontologies and associated linked data in JSON/RDF formats. Our Web interface will be built upon the open source and configurable WebProtege ontology editor. We will develop a simplified mobile version of our user interface which will automatically detect the hosting device and adjust the user interface layout to accommodate different screen sizes. We will also use the Semantic Media Wiki that allows the user to store and query the data within the wiki pages. By using HTML 5, JavaScript, and WebGL, we aim to create an interactive, dynamic, and multi-dimensional user interface that presents various geosciences data sets in a natural and intuitive way.

Development of a knowledge acquisition tool for an expert system flight status monitor

NASA Technical Reports Server (NTRS)

Disbrow, J. D.; Duke, E. L.; Regenie, V. A.

1986-01-01

Two of the main issues in artificial intelligence today are knowledge acquisition dion and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. The knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use is discussed. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

Development of a knowledge acquisition tool for an expert system flight status monitor

NASA Technical Reports Server (NTRS)

Disbrow, J. D.; Duke, E. L.; Regenie, V. A.

1986-01-01

Two of the main issues in artificial intelligence today are knowledge acquisition and knowledge representation. The Dryden Flight Research Facility of NASA's Ames Research Center is presently involved in the design and implementation of an expert system flight status monitor that will provide expertise and knowledge to aid the flight systems engineer in monitoring today's advanced high-performance aircraft. The flight status monitor can be divided into two sections: the expert system itself and the knowledge acquisition tool. This paper discusses the knowledge acquisition tool, the means it uses to extract knowledge from the domain expert, and how that knowledge is represented for computer use. An actual aircraft system has been codified by this tool with great success. Future real-time use of the expert system has been facilitated by using the knowledge acquisition tool to easily generate a logically consistent and complete knowledge base.

General Intelligence, Emotional Intelligence and Academic Knowledge as Predictors of Creativity Domains: A Study of Gifted Students

ERIC Educational Resources Information Center

Sahin, Feyzullah

2016-01-01

Creativity of the individual is dependent on numerous factors, such as knowledge, general intelligence and emotional intelligence. The general purpose of this study is to investigate the effect of general intelligence, emotional intelligence and academic knowledge on the emerging of domain-specific creativity. The study was conducted on 178…

Motor Knowledge Is One Dimension for Concept Organization: Further Evidence from a Chinese Semantic Dementia Case

ERIC Educational Resources Information Center

Lin, Nan; Guo, Qihao; Han, Zaizhu; Bi, Yanchao

2011-01-01

Neuropsychological and neuroimaging studies have indicated that motor knowledge is one potential dimension along which concepts are organized. Here we present further direct evidence for the effects of motor knowledge in accounting for categorical patterns across object domains (living vs. nonliving) and grammatical domains (nouns vs. verbs), as…

Designing Interaction as a Learning Process: Supporting Users' Domain Knowledge Development in Interaction

ERIC Educational Resources Information Center

Choi, Jung-Min

2010-01-01

The primary concern in current interaction design is focused on how to help users solve problems and achieve goals more easily and efficiently. While users' sufficient knowledge acquisition of operating a product or system is considered important, their acquisition of problem-solving knowledge in the task domain has largely been disregarded. As a…

Teaching Efficacy: Exploring Relationships between Mathematics and Science Self-Efficacy Beliefs, PCK and Domain Knowledge among Preservice Teachers from the United States

ERIC Educational Resources Information Center

Thomson, Margareta Maria; DiFrancesca, Daniell; Carrier, Sarah; Lee, Carrie

2017-01-01

This mixed-methods study investigated the relationships among preservice teachers' efficacy beliefs, pedagogical content knowledge (PCK) and their domain knowledge (DK) as related to mathematics and science teaching. Quantitative results revealed that participants' PCK was significantly correlated with their mathematics and science efficacy…

Design and isolation of ribozyme-substrate pairs using RNase P-based ribozymes containing altered substrate binding sites.

PubMed Central

Mobley, E M; Pan, T

1999-01-01

Substrate recognition and cleavage by the bacterial RNase P RNA requires two domains, a specificity domain, or S-domain, and a catalytic domain, or C-domain. The S-domain binds the T stem-loop region in a pre-tRNA substrate to confer specificity for tRNA substrates. In this work, the entire S-domain of the Bacillus subtilis RNase P RNA is replaced with an artificial substrate binding module. New RNA substrates are isolated by in vitro selection using two libraries containing random regions of 60 nt. At the end of the selection, the cleavage rates of the substrate library are approximately 0.7 min(-1)in 10 mM MgCl(2)at 37 degrees C, approximately 4-fold better than the cleavage of a pre-tRNA substrate by the wild-type RNase P RNA under the same conditions. The contribution of the S-domain replacement to the catalytic efficiency is from 6- to 22 000-fold. Chemical and nuclease mapping of two ribozyme-product complexes shows that this contribution correlates with direct interactions between the S-domain replacement and the selected substrate. These results demonstrate the feasibility of design and isolation of RNase P-based, matching ribozyme-substrate pairs without prior knowledge of the sequence or structure of the interactive modules in the ribozyme or substrate. PMID:10518624

Inquiry-based science education: towards a pedagogical framework for primary school teachers

NASA Astrophysics Data System (ADS)

van Uum, Martina S. J.; Verhoeff, Roald P.; Peeters, Marieke

2016-02-01

Inquiry-based science education (IBSE) has been promoted as an inspiring way of learning science by engaging pupils in designing and conducting their own scientific investigations. For primary school teachers, the open nature of IBSE poses challenges as they often lack experience in supporting their pupils during the different phases of an open IBSE project, such as formulating a research question and designing and conducting an investigation. The current study aims to meet these challenges by presenting a pedagogical framework in which four domains of scientific knowledge are addressed in seven phases of inquiry. The framework is based on video analyses of pedagogical interventions by primary school teachers participating in open IBSE projects. Our results show that teachers can guide their pupils successfully through the process of open inquiry by explicitly addressing the conceptual, epistemic, social and/or procedural domain of scientific knowledge in the subsequent phases of inquiry. The paper concludes by suggesting further research to validate our framework and to develop a pedagogy for primary school teachers to guide their pupils through the different phases of open inquiry.

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.

Detailed Knowledge About HIV Epidemiology and Transmission Dynamics and Their Associations With Preventive and Risk Behaviors Among Gay, Bisexual, and Other Men Who Have Sex With Men in the United States

PubMed Central

Sullivan, Stephen P; Stephenson, Rob B

2017-01-01

Background Gay, bisexual, and other men who have sex with men (GBMSM) in the United States remain disproportionately affected by human immunodeficiency virus (HIV). Yet their testing frequency is suboptimal and condomless anal sex (CAS) is increasing. Behavioral theories posit that information about HIV is a pivotal construct in individual risk reduction. However, measurements of knowledge have traditionally focused on ever hearing about HIV and being aware of the most common routes of spread. Objective Using a national Web-based sample of sexually active GBMSM, we sought to (1) quantify levels of detailed knowledge about HIV epidemiology and transmission dynamics, (2) describe variations in detailed knowledge levels across demographic strata, and (3) evaluate potential associations of increasing levels of detailed knowledge with HIV testing in the past year and engaging in CAS with a male partner in the past 3 months. Methods GBMSM were recruited through a social networking website (Facebook) from August to September 2015 and asked 17 knowledge-based questions pertaining to the following 2 domains using a Web-based survey: HIV epidemiology (9 questions including statistics on incidence, prevalence, and distribution) and HIV transmission dynamics (8 questions including modes of spread and per-act transmission probabilities). Ordinal domain-specific indices of detailed knowledge were created for each respondent by summing their number of correct responses. Separate cumulative logit models were used to identify factors independently associated with each index, and multivariable logistic regression models were used to characterize associations with HIV testing history and recently engaging in CAS. Results Of the 1064 participants in our study, only half (49.62%, 528/1064) had been tested for HIV in the past year, and almost half (47.84%, 509/1064) had engaged in CAS with a male partner in the past 3 months. Majority scored 3 of 9 epidemiology questions correct (26.88%, 286/1064) and 5 of 8 transmission dynamics questions correct (25.00%, 266/1064). Participants younger than 35 years, of non-Hispanic non-white or Hispanic race and ethnicity, with lower educational levels, and who reported a sexual orientation other than homosexual or gay were significantly less knowledgeable about HIV transmission dynamics. Increasing levels of knowledge about this domain were independently associated with testing in the past year (adjusted odds ratio for each additional correct response: 1.10, 95% CI 1.01-1.20) but not with recent CAS. Increasing knowledge about HIV epidemiology was not associated with either outcome. Conclusions Increasing detailed knowledge about HIV epidemiology might not be as important as educating sexually active GBMSM regarding transmission dynamics. Researchers and practitioners designing prevention messages targeting GBMSM should bear in mind that not all knowledge is equal and that some aspects might have a greater positive impact than others. Future research to identify influential content and contemporary modes of delivery is needed. PMID:28264795

AIAA Aerospace America Magazine - Year in Review Article, 2010

NASA Technical Reports Server (NTRS)

Figueroa, Fernando

2010-01-01

NASA Stennis Space Center has implemented a pilot operational Integrated System Health Management (ISHM) capability. The implementation was done for the E-2 Rocket Engine Test Stand and a Chemical Steam Generator (CSG) test article; and validated during operational testing. The CSG test program is a risk mitigation activity to support building of the new A-3 Test Stand, which will be a highly complex facility for testing of engines in high altitude conditions. The foundation of the ISHM capability are knowledge-based integrated domain models for the test stand and CSG, with physical and model-based elements represented by objects the domain models enable modular and evolutionary ISHM functionality.

Directive sources in acoustic discrete-time domain simulations based on directivity diagrams.

PubMed

Escolano, José; López, José J; Pueo, Basilio

2007-06-01

Discrete-time domain methods provide a simple and flexible way to solve initial boundary value problems. With regard to the sources in such methods, only monopoles or dipoles can be considered. However, in many problems such as room acoustics, the radiation of realistic sources is directional-dependent and their directivity patterns have a clear influence on the total sound field. In this letter, a method to synthesize the directivity of sources is proposed, especially in cases where the knowledge is only based on discrete values of the directivity diagram. Some examples have been carried out in order to show the behavior and accuracy of the proposed method.

The effect of an android-based application on the knowledge of the caregivers of children with cerebral palsy

PubMed Central

Ghazisaeedi, Marjan; Safari, Ameneh; Sheikhtaheri, Abbas; Dalvand, Hamid

2016-01-01

Background: Mobile health and e-learning may have a significant impact on training patients, physicians, students and caregivers. This study aimed to evaluate the effect of using an educational mobile application on the knowledge of the caregivers of children with cerebral palsy (CP). Methods: We used a previously developed mobile application. The knowledge of 17 caregivers of children with CP (including parents) about the daily care of their children was evaluated through a self-assessment and a test with multiple-choice and true-false questions. Next, the application, which included several educational modules for the daily care of the children with CP, was given to the caregivers to use continually for two months. After this period, the knowledge of the caregivers was evaluated by the same tools. Data analysis was performed by SPSS-16, using paired-sample t-test or Wilcoxon test. Results: The effect of the use of this educational application on the knowledge of caregivers in all childcare domains, except for eating, was reported to be significant (p<0.05). Furthermore, the results of the multiplechoice test revealed that this application increased the knowledge of caregivers in all domains except playing (p<0.05). Conclusion: Training through novel technologies such as Smartphone along with their applications can improve the knowledge of caregivers about the daily care of children with cerebral palsy. PMID:28491831

The effect of an android-based application on the knowledge of the caregivers of children with cerebral palsy.

PubMed

Ghazisaeedi, Marjan; Safari, Ameneh; Sheikhtaheri, Abbas; Dalvand, Hamid

2016-01-01

Background: Mobile health and e-learning may have a significant impact on training patients, physicians, students and caregivers. This study aimed to evaluate the effect of using an educational mobile application on the knowledge of the caregivers of children with cerebral palsy (CP). Methods: We used a previously developed mobile application. The knowledge of 17 caregivers of children with CP (including parents) about the daily care of their children was evaluated through a self-assessment and a test with multiple-choice and true-false questions. Next, the application, which included several educational modules for the daily care of the children with CP, was given to the caregivers to use continually for two months. After this period, the knowledge of the caregivers was evaluated by the same tools. Data analysis was performed by SPSS-16, using paired-sample t-test or Wilcoxon test. Results: The effect of the use of this educational application on the knowledge of caregivers in all childcare domains, except for eating, was reported to be significant (p<0.05). Furthermore, the results of the multiplechoice test revealed that this application increased the knowledge of caregivers in all domains except playing (p<0.05). Conclusion: Training through novel technologies such as Smartphone along with their applications can improve the knowledge of caregivers about the daily care of children with cerebral palsy.

Query-oriented evidence extraction to support evidence-based medicine practice.

PubMed

Sarker, Abeed; Mollá, Diego; Paris, Cecile

2016-02-01

Evidence-based medicine practice requires medical practitioners to rely on the best available evidence, in addition to their expertise, when making clinical decisions. The medical domain boasts a large amount of published medical research data, indexed in various medical databases such as MEDLINE. As the size of this data grows, practitioners increasingly face the problem of information overload, and past research has established the time-associated obstacles faced by evidence-based medicine practitioners. In this paper, we focus on the problem of automatic text summarisation to help practitioners quickly find query-focused information from relevant documents. We utilise an annotated corpus that is specialised for the task of evidence-based summarisation of text. In contrast to past summarisation approaches, which mostly rely on surface level features to identify salient pieces of texts that form the summaries, our approach focuses on the use of corpus-based statistics, and domain-specific lexical knowledge for the identification of summary contents. We also apply a target-sentence-specific summarisation technique that reduces the problem of underfitting that persists in generic summarisation models. In automatic evaluations run over a large number of annotated summaries, our extractive summarisation technique statistically outperforms various baseline and benchmark summarisation models with a percentile rank of 96.8%. A manual evaluation shows that our extractive summarisation approach is capable of selecting content with high recall and precision, and may thus be used to generate bottom-line answers to practitioners' queries. Our research shows that the incorporation of specialised data and domain-specific knowledge can significantly improve text summarisation performance in the medical domain. Due to the vast amounts of medical text available, and the high growth of this form of data, we suspect that such summarisation techniques will address the time-related obstacles associated with evidence-based medicine. Copyright © 2015 Elsevier Inc. All rights reserved.

Nursing constraint models for electronic health records: a vision for domain knowledge governance.

PubMed

Hovenga, Evelyn; Garde, Sebastian; Heard, Sam

2005-12-01

Various forms of electronic health records (EHRs) are currently being introduced in several countries. Nurses are primary stakeholders and need to ensure that their information and knowledge needs are being met by such systems information sharing between health care providers to enable them to improve the quality and efficiency of health care service delivery for all subjects of care. The latest international EHR standards have adopted the openEHR approach of two-level modelling. The first level is a stable information model determining structure, while the second level consists of constraint models or 'archetypes' that reflect the specifications or clinician rules for how clinical information needs to be represented to enable unambiguous data sharing. The current state of play in terms of international health informatics standards development activities is providing the nursing profession with a unique opportunity and challenge. Much work has been undertaken internationally in the area of nursing terminologies and evidence-based practice. This paper argues that to make the most of these emerging technologies and EHRs we must now concentrate on developing a process to identify, document, implement, manage and govern our nursing domain knowledge as well as contribute to the development of relevant international standards. It is argued that one comprehensive nursing terminology, such as the ICNP or SNOMED CT is simply too complex and too difficult to maintain. As the openEHR archetype approach does not rely heavily on big standardised terminologies, it offers more flexibility during standardisation of clinical concepts and it ensures open, future-proof electronic health records. We conclude that it is highly desirable for the nursing profession to adopt this openEHR approach as a means of documenting and governing the nursing profession's domain knowledge. It is essential for the nursing profession to develop its domain knowledge constraint models (archetypes) collaboratively in an international context.

Dynamic Uncertain Causality Graph for Knowledge Representation and Reasoning: Utilization of Statistical Data and Domain Knowledge in Complex Cases.

PubMed

Zhang, Qin; Yao, Quanying

2018-05-01

The dynamic uncertain causality graph (DUCG) is a newly presented framework for uncertain causality representation and probabilistic reasoning. It has been successfully applied to online fault diagnoses of large, complex industrial systems, and decease diagnoses. This paper extends the DUCG to model more complex cases than what could be previously modeled, e.g., the case in which statistical data are in different groups with or without overlap, and some domain knowledge and actions (new variables with uncertain causalities) are introduced. In other words, this paper proposes to use -mode, -mode, and -mode of the DUCG to model such complex cases and then transform them into either the standard -mode or the standard -mode. In the former situation, if no directed cyclic graph is involved, the transformed result is simply a Bayesian network (BN), and existing inference methods for BNs can be applied. In the latter situation, an inference method based on the DUCG is proposed. Examples are provided to illustrate the methodology.

Inferring protein domains associated with drug side effects based on drug-target interaction network

PubMed Central

2013-01-01

Background Most phenotypic effects of drugs are involved in the interactions between drugs and their target proteins, however, our knowledge about the molecular mechanism of the drug-target interactions is very limited. One of challenging issues in recent pharmaceutical science is to identify the underlying molecular features which govern drug-target interactions. Results In this paper, we make a systematic analysis of the correlation between drug side effects and protein domains, which we call "pharmacogenomic features," based on the drug-target interaction network. We detect drug side effects and protein domains that appear jointly in known drug-target interactions, which is made possible by using classifiers with sparse models. It is shown that the inferred pharmacogenomic features can be used for predicting potential drug-target interactions. We also discuss advantages and limitations of the pharmacogenomic features, compared with the chemogenomic features that are the associations between drug chemical substructures and protein domains. Conclusion The inferred side effect-domain association network is expected to be useful for estimating common drug side effects for different protein families and characteristic drug side effects for specific protein domains. PMID:24565527

Shaping Our World: Digital Storytelling and the Authoring of Society

ERIC Educational Resources Information Center

Brzoska, Karen Lynn

2009-01-01

Globalization, networked societies, and a knowledge-based economy engender increasing reliance on digital communication technologies for the dissemination of information and ideas (Castells, Fernandez-Ardevol, Qiu & Sey, 2006). While the technological revolution has broadened access this digital domain, participants often adopt the passive…

Interpersonal Communication in the Undergraduate Core.

ERIC Educational Resources Information Center

Wolff, Leanne O.

The interpersonal communication course at an Ohio college is based on several assumptions about the nature of interpersonal communication. First, the course should emphasize both the ability to demonstrate knowledge of the communicative behavior appropriate in a given situation (cognitive domain) and the performance necessary for communication…

Towards Building an AOP-based Prenatal Developmental Toxicity Ontology (CEFIC LRI Workshop - Brussels)

EPA Science Inventory

Ontologies are a way to formalize domain-specific scientific knowledge. A developmental ontology would help researchers describe the pathways and processes critical to embryonic development and provide a way to link their chemical disruption to adverse outcomes. Designing one for...

Organization and integration of biomedical knowledge with concept maps for key peroxisomal pathways.

PubMed

Willemsen, A M; Jansen, G A; Komen, J C; van Hooff, S; Waterham, H R; Brites, P M T; Wanders, R J A; van Kampen, A H C

2008-08-15

One important area of clinical genomics research involves the elucidation of molecular mechanisms underlying (complex) disorders which eventually may lead to new diagnostic or drug targets. To further advance this area of clinical genomics one of the main challenges is the acquisition and integration of data, information and expert knowledge for specific biomedical domains and diseases. Currently the required information is not very well organized but scattered over biological and biomedical databases, basic text books, scientific literature and experts' minds and may be highly specific, heterogeneous, complex and voluminous. We present a new framework to construct knowledge bases with concept maps for presentation of information and the web ontology language OWL for the representation of information. We demonstrate this framework through the construction of a peroxisomal knowledge base, which focuses on four key peroxisomal pathways and several related genetic disorders. All 155 concept maps in our knowledge base are linked to at least one other concept map, which allows the visualization of one big network of related pieces of information. The peroxisome knowledge base is available from www.bioinformaticslaboratory.nl (Support-->Web applications). Supplementary data is available from www.bioinformaticslaboratory.nl (Research-->Output--> Publications--> KB_SuppInfo)

Data Model Management for Space Information Systems

NASA Technical Reports Server (NTRS)

Hughes, J. Steven; Crichton, Daniel J.; Ramirez, Paul; Mattmann, chris

2006-01-01

The Reference Architecture for Space Information Management (RASIM) suggests the separation of the data model from software components to promote the development of flexible information management systems. RASIM allows the data model to evolve independently from the software components and results in a robust implementation that remains viable as the domain changes. However, the development and management of data models within RASIM are difficult and time consuming tasks involving the choice of a notation, the capture of the model, its validation for consistency, and the export of the model for implementation. Current limitations to this approach include the lack of ability to capture comprehensive domain knowledge, the loss of significant modeling information during implementation, the lack of model visualization and documentation capabilities, and exports being limited to one or two schema types. The advent of the Semantic Web and its demand for sophisticated data models has addressed this situation by providing a new level of data model management in the form of ontology tools. In this paper we describe the use of a representative ontology tool to capture and manage a data model for a space information system. The resulting ontology is implementation independent. Novel on-line visualization and documentation capabilities are available automatically, and the ability to export to various schemas can be added through tool plug-ins. In addition, the ingestion of data instances into the ontology allows validation of the ontology and results in a domain knowledge base. Semantic browsers are easily configured for the knowledge base. For example the export of the knowledge base to RDF/XML and RDFS/XML and the use of open source metadata browsers provide ready-made user interfaces that support both text- and facet-based search. This paper will present the Planetary Data System (PDS) data model as a use case and describe the import of the data model into an ontology tool. We will also describe the current effort to provide interoperability with the European Space Agency (ESA)/Planetary Science Archive (PSA) which is critically dependent on a common data model.

From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF.

PubMed

Kılıç, Sefa; Sagitova, Dinara M; Wolfish, Shoshannah; Bely, Benoit; Courtot, Mélanie; Ciufo, Stacy; Tatusova, Tatiana; O'Donovan, Claire; Chibucos, Marcus C; Martin, Maria J; Erill, Ivan

2016-01-01

Domain-specific databases are essential resources for the biomedical community, leveraging expert knowledge to curate published literature and provide access to referenced data and knowledge. The limited scope of these databases, however, poses important challenges on their infrastructure, visibility, funding and usefulness to the broader scientific community. CollecTF is a community-oriented database documenting experimentally validated transcription factor (TF)-binding sites in the Bacteria domain. In its quest to become a community resource for the annotation of transcriptional regulatory elements in bacterial genomes, CollecTF aims to move away from the conventional data-repository paradigm of domain-specific databases. Through the adoption of well-established ontologies, identifiers and collaborations, CollecTF has progressively become also a portal for the annotation and submission of information on transcriptional regulatory elements to major biological sequence resources (RefSeq, UniProtKB and the Gene Ontology Consortium). This fundamental change in database conception capitalizes on the domain-specific knowledge of contributing communities to provide high-quality annotations, while leveraging the availability of stable information hubs to promote long-term access and provide high-visibility to the data. As a submission portal, CollecTF generates TF-binding site information through direct annotation of RefSeq genome records, definition of TF-based regulatory networks in UniProtKB entries and submission of functional annotations to the Gene Ontology. As a database, CollecTF provides enhanced search and browsing, targeted data exports, binding motif analysis tools and integration with motif discovery and search platforms. This innovative approach will allow CollecTF to focus its limited resources on the generation of high-quality information and the provision of specialized access to the data.Database URL: http://www.collectf.org/. © The Author(s) 2016. Published by Oxford University Press.

Examining, Documenting, and Modeling the Problem Space of a Variable Domain

DTIC Science & Technology

2002-06-14

Feature-Oriented Domain Analysis ( FODA ) .............................................................................................. 9...development of this proposed process include: Feature-Oriented Domain Analysis ( FODA ) [3,4], Organization Domain Modeling (ODM) [2,5,6], Family-Oriented...configuration knowledge using generators [2]. 8 Existing Methods of Domain Engineering Feature-Oriented Domain Analysis ( FODA ) FODA is a domain

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.

Improving Cyber-Security of Smart Grid Systems via Anomaly Detection and Linguistic Domain Knowledge

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

Ondrej Linda; Todd Vollmer; Milos Manic

The planned large scale deployment of smart grid network devices will generate a large amount of information exchanged over various types of communication networks. The implementation of these critical systems will require appropriate cyber-security measures. A network anomaly detection solution is considered in this work. In common network architectures multiple communications streams are simultaneously present, making it difficult to build an anomaly detection solution for the entire system. In addition, common anomaly detection algorithms require specification of a sensitivity threshold, which inevitably leads to a tradeoff between false positives and false negatives rates. In order to alleviate these issues, thismore » paper proposes a novel anomaly detection architecture. The designed system applies the previously developed network security cyber-sensor method to individual selected communication streams allowing for learning accurate normal network behavior models. Furthermore, the developed system dynamically adjusts the sensitivity threshold of each anomaly detection algorithm based on domain knowledge about the specific network system. It is proposed to model this domain knowledge using Interval Type-2 Fuzzy Logic rules, which linguistically describe the relationship between various features of the network communication and the possibility of a cyber attack. The proposed method was tested on experimental smart grid system demonstrating enhanced cyber-security.« less

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.

TARGET's role in knowledge acquisition, engineering, validation, and documentation

NASA Technical Reports Server (NTRS)

Levi, Keith R.

1994-01-01

We investigate the use of the TARGET task analysis tool for use in the development of rule-based expert systems. We found TARGET to be very helpful in the knowledge acquisition process. It enabled us to perform knowledge acquisition with one knowledge engineer rather than two. In addition, it improved communication between the domain expert and knowledge engineer. We also found it to be useful for both the rule development and refinement phases of the knowledge engineering process. Using the network in these phases required us to develop guidelines that enabled us to easily translate the network into production rules. A significant requirement for TARGET remaining useful throughout the knowledge engineering process was the need to carefully maintain consistency between the network and the rule representations. Maintaining consistency not only benefited the knowledge engineering process, but also has significant payoffs in the areas of validation of the expert system and documentation of the knowledge in the system.

From hospital information system components to the medical record and clinical guidelines & protocols.

PubMed

Veloso, M; Estevão, N; Ferreira, P; Rodrigues, R; Costa, C T; Barahona, P

1997-01-01

This paper introduces an ongoing project towards the development of a new generation HIS, aiming at the integration of clinical and administrative information within a common framework. Its design incorporates explicit knowledge about domain objects and professional activities to be processed by the system together with related knowledge management services and act management services. The paper presents the conceptual model of the proposed HIS architecture, that supports a rich and fully integrated patient data model, enabling the implementation of a dynamic electronic patient record tightly coupled with computerised guideline knowledge bases.

Understanding practice: the factors that influence management of mild traumatic brain injury in the emergency department--a qualitative study using the Theoretical Domains Framework.

PubMed

Tavender, Emma J; Bosch, Marije; Gruen, Russell L; Green, Sally E; Knott, Jonathan; Francis, Jill J; Michie, Susan; O'Connor, Denise A

2014-01-13

Mild traumatic brain injury is a frequent cause of presentation to emergency departments. Despite the availability of clinical practice guidelines in this area, there is variation in practice. One of the aims of the Neurotrauma Evidence Translation program is to develop and evaluate a targeted, theory- and evidence-informed intervention to improve the management of mild traumatic brain injury in Australian emergency departments. This study is the first step in the intervention development process and uses the Theoretical Domains Framework to explore the factors perceived to influence the uptake of four key evidence-based recommended practices for managing mild traumatic brain injury. Semi-structured interviews were conducted with emergency staff in the Australian state of Victoria. The interview guide was developed using the Theoretical Domains Framework to explore current practice and to identify the factors perceived to influence practice. Two researchers coded the interview transcripts using thematic content analysis. A total of 42 participants (9 Directors, 20 doctors and 13 nurses) were interviewed over a seven-month period. The results suggested that (i) the prospective assessment of post-traumatic amnesia was influenced by: knowledge; beliefs about consequences; environmental context and resources; skills; social/professional role and identity; and beliefs about capabilities; (ii) the use of guideline-developed criteria or decision rules to inform the appropriate use of a CT scan was influenced by: knowledge; beliefs about consequences; environmental context and resources; memory, attention and decision processes; beliefs about capabilities; social influences; skills and behavioral regulation; (iii) providing verbal and written patient information on discharge was influenced by: beliefs about consequences; environmental context and resources; memory, attention and decision processes; social/professional role and identity; and knowledge; (iv) the practice of providing brief, routine follow-up on discharge was influenced by: environmental context and resources; social/professional role and identity; knowledge; beliefs about consequences; and motivation and goals. Using the Theoretical Domains Framework, factors thought to influence the management of mild traumatic brain injury in the emergency department were identified. These factors present theoretically based targets for a future intervention.

PASBio: predicate-argument structures for event extraction in molecular biology

PubMed Central

Wattarujeekrit, Tuangthong; Shah, Parantu K; Collier, Nigel

2004-01-01

Background The exploitation of information extraction (IE), a technology aiming to provide instances of structured representations from free-form text, has been rapidly growing within the molecular biology (MB) research community to keep track of the latest results reported in literature. IE systems have traditionally used shallow syntactic patterns for matching facts in sentences but such approaches appear inadequate to achieve high accuracy in MB event extraction due to complex sentence structure. A consensus in the IE community is emerging on the necessity for exploiting deeper knowledge structures such as through the relations between a verb and its arguments shown by predicate-argument structure (PAS). PAS is of interest as structures typically correspond to events of interest and their participating entities. For this to be realized within IE a key knowledge component is the definition of PAS frames. PAS frames for non-technical domains such as newswire are already being constructed in several projects such as PropBank, VerbNet, and FrameNet. Knowledge from PAS should enable more accurate applications in several areas where sentence understanding is required like machine translation and text summarization. In this article, we explore the need to adapt PAS for the MB domain and specify PAS frames to support IE, as well as outlining the major issues that require consideration in their construction. Results We introduce PASBio by extending a model based on PropBank to the MB domain. The hypothesis we explore is that PAS holds the key for understanding relationships describing the roles of genes and gene products in mediating their biological functions. We chose predicates describing gene expression, molecular interactions and signal transduction events with the aim of covering a number of research areas in MB. Analysis was performed on sentences containing a set of verbal predicates from MEDLINE and full text journals. Results confirm the necessity to analyze PAS specifically for MB domain. Conclusions At present PASBio contains the analyzed PAS of over 30 verbs, publicly available on the Internet for use in advanced applications. In the future we aim to expand the knowledge base to cover more verbs and the nominal form of each predicate. PMID:15494078

A middle man approach to knowledge acquisition in expert systems

NASA Technical Reports Server (NTRS)

Jordan, Janice A.; Lin, Min-Jin; Mayer, Richard J.; Sterle, Mark E.

1990-01-01

The Weed Control Advisor (WCA) is a robust expert system that has been successfully implemented on an IBM AT class microcomputer in CLIPS. The goal of the WCA was to demonstrate the feasibility of providing an economical, efficient, user friendly system through which Texas rice producers could obtain expert level knowledge regarding herbicide application for weed control. During the development phase of the WCA, an improved knowledge acquisition method which we call the Middle Man Approach (MMA) was applied to facilitate the communication process between the domain experts and the knowledge engineer. The MMA served to circumvent the problems associated with the more traditional forms of knowledge acquisition by placing the Middle Man, a semi-expert in the problem domain with some computer expertise, at the site of system development. The middle man was able to contribute to system development in two major ways. First, the Middle Man had experience working in rice production and could assume many of the responsibilities normally performed by the domain experts such as explaining the background of the problem domain and determining the important relations. Second, the Middle Man was familiar with computers and worked closely with the system developers to update the rules after the domain experts reviewed the prototype, contribute to the help menus and explanation portions of the expert system, conduct the testing that is required to insure that the expert system gives the expected results answer questions in a timely way, help the knowledge engineer structure the domain knowledge into a useable form, and provide insight into the end user's profile which helped in the development of the simple user friendly interface. The final results were not only that both time expended and costs were greatly reduced by using the MMA, but the quality of the system was improved. This papa will introduce the WCA system and then discuss traditional knowledge acquisition along with some of the problems often associated with it, the MMA methodology, and its application to the WCA development.

Knowledge-based verification of clinical guidelines by detection of anomalies.

PubMed

Duftschmid, G; Miksch, S

2001-04-01

As shown in numerous studies, a significant part of published clinical guidelines is tainted with different types of semantical errors that interfere with their practical application. The adaptation of generic guidelines, necessitated by circumstances such as resource limitations within the applying organization or unexpected events arising in the course of patient care, further promotes the introduction of defects. Still, most current approaches for the automation of clinical guidelines are lacking mechanisms, which check the overall correctness of their output. In the domain of software engineering in general and in the domain of knowledge-based systems (KBS) in particular, a common strategy to examine a system for potential defects consists in its verification. The focus of this work is to present an approach, which helps to ensure the semantical correctness of clinical guidelines in a three-step process. We use a particular guideline specification language called Asbru to demonstrate our verification mechanism. A scenario-based evaluation of our method is provided based on a guideline for the artificial ventilation of newborn infants. The described approach is kept sufficiently general in order to allow its application to several other guideline representation formats.

Knowledge and intelligent computing system in medicine.

PubMed

Pandey, Babita; Mishra, R B

2009-03-01

Knowledge-based systems (KBS) and intelligent computing systems have been used in the medical planning, diagnosis and treatment. The KBS consists of rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) whereas intelligent computing method (ICM) encompasses genetic algorithm (GA), artificial neural network (ANN), fuzzy logic (FL) and others. The combination of methods in KBS such as CBR-RBR, CBR-MBR and RBR-CBR-MBR and the combination of methods in ICM is ANN-GA, fuzzy-ANN, fuzzy-GA and fuzzy-ANN-GA. The combination of methods from KBS to ICM is RBR-ANN, CBR-ANN, RBR-CBR-ANN, fuzzy-RBR, fuzzy-CBR and fuzzy-CBR-ANN. In this paper, we have made a study of different singular and combined methods (185 in number) applicable to medical domain from mid 1970s to 2008. The study is presented in tabular form, showing the methods and its salient features, processes and application areas in medical domain (diagnosis, treatment and planning). It is observed that most of the methods are used in medical diagnosis very few are used for planning and moderate number in treatment. The study and its presentation in this context would be helpful for novice researchers in the area of medical expert system.

The Cognitive Processes Used in Team Collaboration During Asynchronous, Distributed Decision Making

DTIC Science & Technology

2004-06-01

Transfer Conventions (IPtcp) IP: Solution Alternatives (IPsa) KB: Collaborative Knowledge (KBck) KB: Shared Understanding ( KBsu ) KB: Domain...Gill.” KBsu : Knowledge Building (shared understanding) = using facts to justify a solution. “I think Eddie did it because he was hard of hearing...KB: Collaborative Knowledge (KBck) KB: Shared Understanding ( KBsu ) KB: Domain Expertise (IPde) * * ** ** ** = significant Results 15

On developing a knowledge base in infancy.

PubMed

Mandler, J M; McDonough, L

1998-11-01

The development of conceptual categories from 7 to 11 months of age was explored in 5 experiments using an object-examination task. Infants in this age range categorized the global domains of animals, vehicles, and furniture. Plants and kitchen utensils were tested at 11 months, and these domains were also categorized. When 9-month-olds were tested on kitchen utensils, they did not categorize them. Subdivisions within the animal and furniture domains were also examined. Infants did not show any subcategorization of furniture. In the animal domain both 9- and 11-month-olds responded to the life-form distinction between dogs and birds, but they did not differentiate the mammal categories of dogs and cats until 11 months. This early organization of the conceptual system into global domains that become increasingly differentiated is discussed in relation to the adult conceptual system and its breakdown in semantic dementia.

FTDD973: A multimedia knowledge-based system and methodology for operator training and diagnostics

NASA Technical Reports Server (NTRS)

Hekmatpour, Amir; Brown, Gary; Brault, Randy; Bowen, Greg

1993-01-01

FTDD973 (973 Fabricator Training, Documentation, and Diagnostics) is an interactive multimedia knowledge based system and methodology for computer-aided training and certification of operators, as well as tool and process diagnostics in IBM's CMOS SGP fabrication line (building 973). FTDD973 is an example of what can be achieved with modern multimedia workstations. Knowledge-based systems, hypertext, hypergraphics, high resolution images, audio, motion video, and animation are technologies that in synergy can be far more useful than each by itself. FTDD973's modular and object-oriented architecture is also an example of how improvements in software engineering are finally making it possible to combine many software modules into one application. FTDD973 is developed in ExperMedia/2; and OS/2 multimedia expert system shell for domain experts.

A development optical course based on optical fiber white light interference

NASA Astrophysics Data System (ADS)

Jiang, Haili; Sun, Qiuhua; Zhao, Yancheng; Li, Qingbo

2017-08-01

The Michelson interferometer is a very important instrument in optical part for college physics teaching. But most students only know the instrument itself and don't know how to use it in practical engineering problems. A case about optical fiber white light interference based on engineering practice was introduced in the optical teaching of college physics and then designed a development course of university physical optics part. This system based on low-coherence white light interferometric technology can be used to measure distribution strain or temperature. It also could be used in the case of temperature compensation mode.This teaching design can use the knowledge transfer rule to enable students to apply the basic knowledge in the university physics to the new knowledge domain, which can promote the students' ability of using scientific methods to solve complex engineering problems.

Choosing Appropriate Theories for Understanding Hospital Reporting of Adverse Drug Events, a Theoretical Domains Framework Approach.

PubMed

Shalviri, Gloria; Yazdizadeh, Bahareh; Mirbaha, Fariba; Gholami, Kheirollah; Majdzadeh, Reza

2018-01-01

Adverse drug events (ADEs) may cause serious injuries including death. Spontaneous reporting of ADEs plays a great role in detection and prevention of them; however, underreporting always exists. Although several interventions have been utilized to solve this problem, they are mainly based on experience and the rationale for choosing them has no theoretical base. The vast variety of behavioural theories makes it difficult to choose appropriate theory. Theoretical domains framework (TDF) is suggested as a solution. The objective of this study was to select the best theory for evaluating ADE reporting in hospitals based on TDF. We carried out three focus group discussions with hospital pharmacists and nurses, based on TDF questions. The analysis was performed through five steps including coding discussions transcript, extracting beliefs, selecting relevant domains, matching related constructs to the extracted beliefs, and determining the appropriate theories in each domain. The theory with the highest number of matched domains and constructs was selected as the theory of choice. A total of six domains were identified relevant to ADE reporting, including "Knowledge", "Skills", "Beliefs about consequences", "Motivation and goals", "Environmental context and resources" and "Social influences". We found theory of planned behavior as the comprehensive theory to study factors influencing ADE reporting in hospitals, since it was relevant theory in five out of six relevant domains and the common theory in 55 out of 75 identified beliefs. In conclusion, we suggest theory of planned behavior for further studies on designing appropriate interventions to increase ADE reporting in hospitals.

Assessing ISLLC-Based Dispositions of Educational Leadership Candidates

ERIC Educational Resources Information Center

Rea, Dorothy; Carter, Cecil F.; Wilkerson, Judy R.; Valesky, Thomas; Lang, William

2011-01-01

The Council of Chief State School Officers (CCSSO), through the Interstate School Leaders Licensure Consortium (ISLLC), developed standards for the knowledge, skills, and dispositions necessary for effective practice by educational leaders (CCSSO, 1996). These standards provide a viable content domain from which to assess leader cognitive and…

The Biomedicalization of Aging: Dangers and Dilemmas.

ERIC Educational Resources Information Center

Estes, Carroll L.; Binney, Elizabeth A.

1989-01-01

Sees "biomedicalization of aging" as socially constructing old age as process of decremental physical decline and placing aging under domain and control of biomedicine. Examines effects of medicalization on scientific enterprise and development of knowledge base in aging, status and work of professions, policy, and public perception.…

Knowledge representation to support reasoning based on multiple models

NASA Technical Reports Server (NTRS)

Gillam, April; Seidel, Jorge P.; Parker, Alice C.

1990-01-01

Model Based Reasoning is a powerful tool used to design and analyze systems, which are often composed of numerous interactive, interrelated subsystems. Models of the subsystems are written independently and may be used together while they are still under development. Thus the models are not static. They evolve as information becomes obsolete, as improved artifact descriptions are developed, and as system capabilities change. Researchers are using three methods to support knowledge/data base growth, to track the model evolution, and to handle knowledge from diverse domains. First, the representation methodology is based on having pools, or types, of knowledge from which each model is constructed. In addition information is explicit. This includes the interactions between components, the description of the artifact structure, and the constraints and limitations of the models. The third principle we have followed is the separation of the data and knowledge from the inferencing and equation solving mechanisms. This methodology is used in two distinct knowledge-based systems: one for the design of space systems and another for the synthesis of VLSI circuits. It has facilitated the growth and evolution of our models, made accountability of results explicit, and provided credibility for the user community. These capabilities have been implemented and are being used in actual design projects.

A viewpoint-based case-based reasoning approach utilising an enterprise architecture ontology for experience management

NASA Astrophysics Data System (ADS)

Martin, Andreas; Emmenegger, Sandro; Hinkelmann, Knut; Thönssen, Barbara

2017-04-01

The accessibility of project knowledge obtained from experiences is an important and crucial issue in enterprises. This information need about project knowledge can be different from one person to another depending on the different roles he or she has. Therefore, a new ontology-based case-based reasoning (OBCBR) approach that utilises an enterprise ontology is introduced in this article to improve the accessibility of this project knowledge. Utilising an enterprise ontology improves the case-based reasoning (CBR) system through the systematic inclusion of enterprise-specific knowledge. This enterprise-specific knowledge is captured using the overall structure given by the enterprise ontology named ArchiMEO, which is a partial ontological realisation of the enterprise architecture framework (EAF) ArchiMate. This ontological representation, containing historical cases and specific enterprise domain knowledge, is applied in a new OBCBR approach. To support the different information needs of different stakeholders, this OBCBR approach has been built in such a way that different views, viewpoints, concerns and stakeholders can be considered. This is realised using a case viewpoint model derived from the ISO/IEC/IEEE 42010 standard. The introduced approach was implemented as a demonstrator and evaluated using an application case that has been elicited from a business partner in the Swiss research project.

Reasoning in molecular genetics: From a cognitive model to instructional design

NASA Astrophysics Data System (ADS)

Duncan, Ravit Golan

Effective instruction strives to help students construct deep and meaningful understandings in a domain. A key component of designing such instruction is a good understanding of relevant aspects of student cognition in the domain. This entails understanding both the cognitive obstacles to learning and the knowledge elements that are crucial to successful reasoning in the domain. While understandings of student cognition are not a prescription for design, they can nonetheless help instructional-designers and design-researchers focus the design and suggest where and what scaffolding should be incorporated into the instructional sequence and activities. In this dissertation I first discuss my research of the cognitive aspects of reasoning in molecular genetics. By studying both high school and college level students' reasoning about genetic phenomena, I have constructed a conceptual model of reasoning in this domain. The model depicts critical types of domain-specific knowledge, the relationships between them, and their role in facilitating reasoning about genetic phenomena. I then describe the design and evaluation of a high school project-based curricular unit in genetics. The unit was developed by a collaborative team of teachers and a researcher and was enacted in a local public high school. The design process was closely guided by our understandings of student cognition in genetics and the resulting instructional intervention was aimed at scaffolding student engagement with important disciplinary strategies and concepts.

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach

PubMed Central

2012-01-01

Background Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field. Methods We present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors. Results We participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points. Conclusions We have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data. PMID:22759462

Event extraction of bacteria biotopes: a knowledge-intensive NLP-based approach.

PubMed

Ratkovic, Zorana; Golik, Wiktoria; Warnier, Pierre

2012-06-26

Bacteria biotopes cover a wide range of diverse habitats including animal and plant hosts, natural, medical and industrial environments. The high volume of publications in the microbiology domain provides a rich source of up-to-date information on bacteria biotopes. This information, as found in scientific articles, is expressed in natural language and is rarely available in a structured format, such as a database. This information is of great importance for fundamental research and microbiology applications (e.g., medicine, agronomy, food, bioenergy). The automatic extraction of this information from texts will provide a great benefit to the field. We present a new method for extracting relationships between bacteria and their locations using the Alvis framework. Recognition of bacteria and their locations was achieved using a pattern-based approach and domain lexical resources. For the detection of environment locations, we propose a new approach that combines lexical information and the syntactic-semantic analysis of corpus terms to overcome the incompleteness of lexical resources. Bacteria location relations extend over sentence borders, and we developed domain-specific rules for dealing with bacteria anaphors. We participated in the BioNLP 2011 Bacteria Biotope (BB) task with the Alvis system. Official evaluation results show that it achieves the best performance of participating systems. New developments since then have increased the F-score by 4.1 points. We have shown that the combination of semantic analysis and domain-adapted resources is both effective and efficient for event information extraction in the bacteria biotope domain. We plan to adapt the method to deal with a larger set of location types and a large-scale scientific article corpus to enable microbiologists to integrate and use the extracted knowledge in combination with experimental data.

What helps or hinders midwives to implement physical activity guidelines for obese pregnant women? A questionnaire survey using the Theoretical Domains Framework.

PubMed

McParlin, Catherine; Bell, Ruth; Robson, Stephen C; Muirhead, Colin R; Araújo-Soares, Vera

2017-06-01

to investigate barriers and facilitators to physical activity (PA) guideline implementation for midwives when advising obese pregnant women. a cross-sectional, self-completion, anonymous questionnaire was designed using the Theoretical Domains Framework. this framework was developed to evaluate the implementation of guidelines by health care professionals. A total of 40 questions were included. These were informed by previous research on pregnant women's and midwives views, knowledge and attitudes to PA, and supported by national evidence based guidelines. Demographic information and free text comments were also collected. three diverse NHS Trusts in the North East of England. all midwives employed by two hospital Trusts and the community midwives from the third Trust (n=375) were invited to participate. mean domain scores were calculated. Factor and regression analysis were performed to describe which theoretical domains may be influencing practice. Free text comments were analysed thematically. 192 (53%) questionnaires were returned. Mean domain scores were highest for social professional role and knowledge, and lowest for skills, beliefs about capabilities and behaviour regulation. Regression analysis indicated that skills and memory/attention/decision domains had a statistically significant influence on midwives discussing PA with obese pregnant women and advising them accordingly. Midwives comments indicated that they felt it was part of their role to discuss PA with all pregnant women but felt they lacked the skills and resources to do so effectively. midwives seem to have the necessary knowledge about the need/importance of PA advice for obese women and believe it is part of their role, but perceive they lack necessary skills and resources, and do not plan or prioritise the discussion regarding PA with obese pregnant woman. designing interventions that improve skills, promote routine enquiry regarding PA and provide resources (eg. information, referral pathways) may help improve midwives' PA advice. Copyright © 2016 Elsevier Ltd. All rights reserved.

Mining knowledge from corpora: an application to retrieval and indexing.

PubMed

Soualmia, Lina F; Dahamna, Badisse; Darmoni, Stéfan

2008-01-01

The present work aims at discovering new associations between medical concepts to be exploited as input in retrieval and indexing. Association rules method is applied to documents. The process is carried out on three major document categories referring to e-health information consumers: health professionals, students and lay people. Association rules evaluation is founded on statistical measures combined with domain knowledge. Association rules represent existing relations between medical concepts (60.62%) and new knowledge (54.21%). Based on observations, 463 expert rules are defined by medical librarians for retrieval and indexing. Association rules bear out existing relations, produce new knowledge and support users and indexers in document retrieval and indexing.

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.