A neotropical Miocene pollen database employing image-based search and semantic modeling.

PubMed

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W; Jaramillo, Carlos; Shyu, Chi-Ren

2014-08-01

Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery.

Auto-Generated Semantic Processing Services

NASA Technical Reports Server (NTRS)

Davis, Rodney; Hupf, Greg

2009-01-01

Auto-Generated Semantic Processing (AGSP) Services is a suite of software tools for automated generation of other computer programs, denoted cross-platform semantic adapters, that support interoperability of computer-based communication systems that utilize a variety of both new and legacy communication software running in a variety of operating- system/computer-hardware combinations. AGSP has numerous potential uses in military, space-exploration, and other government applications as well as in commercial telecommunications. The cross-platform semantic adapters take advantage of common features of computer- based communication systems to enforce semantics, messaging protocols, and standards of processing of streams of binary data to ensure integrity of data and consistency of meaning among interoperating systems. The auto-generation aspect of AGSP Services reduces development time and effort by emphasizing specification and minimizing implementation: In effect, the design, building, and debugging of software for effecting conversions among complex communication protocols, custom device mappings, and unique data-manipulation algorithms is replaced with metadata specifications that map to an abstract platform-independent communications model. AGSP Services is modular and has been shown to be easily integrable into new and legacy NASA flight and ground communication systems.

Language networks associated with computerized semantic indices.

PubMed

Pakhomov, Serguei V S; Jones, David T; Knopman, David S

2015-01-01

Tests of generative semantic verbal fluency are widely used to study organization and representation of concepts in the human brain. Previous studies demonstrated that clustering and switching behavior during verbal fluency tasks is supported by multiple brain mechanisms associated with semantic memory and executive control. Previous work relied on manual assessments of semantic relatedness between words and grouping of words into semantic clusters. We investigated a computational linguistic approach to measuring the strength of semantic relatedness between words based on latent semantic analysis of word co-occurrences in a subset of a large online encyclopedia. We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected by cognitive impairment. We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions. This study shows that computerized semantic indices complement traditional assessments of verbal fluency to provide a more complete account of the relationship between brain and verbal behavior involved organization and retrieval of lexical information from memory. Copyright © 2014 Elsevier Inc. All rights reserved.

Semantic Annotation of Computational Components

NASA Technical Reports Server (NTRS)

Vanderbilt, Peter; Mehrotra, Piyush

2004-01-01

This paper describes a methodology to specify machine-processable semantic descriptions of computational components to enable them to be shared and reused. A particular focus of this scheme is to enable automatic compositon of such components into simple work-flows.

Thai Language Sentence Similarity Computation Based on Syntactic Structure and Semantic Vector

NASA Astrophysics Data System (ADS)

Wang, Hongbin; Feng, Yinhan; Cheng, Liang

2018-03-01

Sentence similarity computation plays an increasingly important role in text mining, Web page retrieval, machine translation, speech recognition and question answering systems. Thai language as a kind of resources scarce language, it is not like Chinese language with HowNet and CiLin resources. So the Thai sentence similarity research faces some challenges. In order to solve this problem of the Thai language sentence similarity computation. This paper proposes a novel method to compute the similarity of Thai language sentence based on syntactic structure and semantic vector. This method firstly uses the Part-of-Speech (POS) dependency to calculate two sentences syntactic structure similarity, and then through the word vector to calculate two sentences semantic similarity. Finally, we combine the two methods to calculate two Thai language sentences similarity. The proposed method not only considers semantic, but also considers the sentence syntactic structure. The experiment result shows that this method in Thai language sentence similarity computation is feasible.

Semantic Boost on Episodic Associations: An Empirically-Based Computational Model

ERIC Educational Resources Information Center

Silberman, Yaron; Bentin, Shlomo; Miikkulainen, Risto

2007-01-01

Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words…

An Intelligent Semantic E-Learning Framework Using Context-Aware Semantic Web Technologies

ERIC Educational Resources Information Center

Huang, Weihong; Webster, David; Wood, Dawn; Ishaya, Tanko

2006-01-01

Recent developments of e-learning specifications such as Learning Object Metadata (LOM), Sharable Content Object Reference Model (SCORM), Learning Design and other pedagogy research in semantic e-learning have shown a trend of applying innovative computational techniques, especially Semantic Web technologies, to promote existing content-focused…

Computer-Based Semantic Network in Molecular Biology: A Demonstration.

ERIC Educational Resources Information Center

Callman, Joshua L.; And Others

This paper analyzes the hardware and software features that would be desirable in a computer-based semantic network system for representing biology knowledge. It then describes in detail a prototype network of molecular biology knowledge that has been developed using Filevision software and a Macintosh computer. The prototype contains about 100…

Attractor Dynamics and Semantic Neighborhood Density: Processing Is Slowed by Near Neighbors and Speeded by Distant Neighbors

PubMed Central

Mirman, Daniel; Magnuson, James S.

2008-01-01

The authors investigated semantic neighborhood density effects on visual word processing to examine the dynamics of activation and competition among semantic representations. Experiment 1 validated feature-based semantic representations as a basis for computing semantic neighborhood density and suggested that near and distant neighbors have opposite effects on word processing. Experiment 2 confirmed these results: Word processing was slower for dense near neighborhoods and faster for dense distant neighborhoods. Analysis of a computational model showed that attractor dynamics can produce this pattern of neighborhood effects. The authors argue for reconsideration of traditional models of neighborhood effects in terms of attractor dynamics, which allow both inhibitory and facilitative effects to emerge. PMID:18194055

Linking consistency with object/thread semantics - An approach to robust computation

NASA Technical Reports Server (NTRS)

Chen, Raymond C.; Dasgupta, Partha

1989-01-01

This paper presents an object/thread based paradigm that links data consistency with object/thread semantics. The paradigm can be used to achieve a wide range of consistency semantics from strict atomic transactions to standard process semantics. The paradigm supports three types of data consistency. Object programmers indicate the type of consistency desired on a per-operation basis and the system performs automatic concurrency control and recovery management to ensure that those consistency requirements are met. This allows programmers to customize consistency and recovery on a per-application basis without having to supply complicated, custom recovery management schemes. The paradigm allows robust and nonrobust computation to operate concurrently on the same data in a well defined manner. The operating system needs to support only one vehicle of computation - the thread.

A neotropical Miocene pollen database employing image-based search and semantic modeling1

PubMed Central

Han, Jing Ginger; Cao, Hongfei; Barb, Adrian; Punyasena, Surangi W.; Jaramillo, Carlos; Shyu, Chi-Ren

2014-01-01

• Premise of the study: Digital microscopic pollen images are being generated with increasing speed and volume, producing opportunities to develop new computational methods that increase the consistency and efficiency of pollen analysis and provide the palynological community a computational framework for information sharing and knowledge transfer. • Methods: Mathematical methods were used to assign trait semantics (abstract morphological representations) of the images of neotropical Miocene pollen and spores. Advanced database-indexing structures were built to compare and retrieve similar images based on their visual content. A Web-based system was developed to provide novel tools for automatic trait semantic annotation and image retrieval by trait semantics and visual content. • Results: Mathematical models that map visual features to trait semantics can be used to annotate images with morphology semantics and to search image databases with improved reliability and productivity. Images can also be searched by visual content, providing users with customized emphases on traits such as color, shape, and texture. • Discussion: Content- and semantic-based image searches provide a powerful computational platform for pollen and spore identification. The infrastructure outlined provides a framework for building a community-wide palynological resource, streamlining the process of manual identification, analysis, and species discovery. PMID:25202648

Ontology based heterogeneous materials database integration and semantic query

NASA Astrophysics Data System (ADS)

Zhao, Shuai; Qian, Quan

2017-10-01

Materials digital data, high throughput experiments and high throughput computations are regarded as three key pillars of materials genome initiatives. With the fast growth of materials data, the integration and sharing of data is very urgent, that has gradually become a hot topic of materials informatics. Due to the lack of semantic description, it is difficult to integrate data deeply in semantic level when adopting the conventional heterogeneous database integration approaches such as federal database or data warehouse. In this paper, a semantic integration method is proposed to create the semantic ontology by extracting the database schema semi-automatically. Other heterogeneous databases are integrated to the ontology by means of relational algebra and the rooted graph. Based on integrated ontology, semantic query can be done using SPARQL. During the experiments, two world famous First Principle Computational databases, OQMD and Materials Project are used as the integration targets, which show the availability and effectiveness of our method.

Semantic Coherence Facilitates Distributional Learning.

PubMed

Ouyang, Long; Boroditsky, Lera; Frank, Michael C

2017-04-01

Computational models have shown that purely statistical knowledge about words' linguistic contexts is sufficient to learn many properties of words, including syntactic and semantic category. For example, models can infer that "postman" and "mailman" are semantically similar because they have quantitatively similar patterns of association with other words (e.g., they both tend to occur with words like "deliver," "truck," "package"). In contrast to these computational results, artificial language learning experiments suggest that distributional statistics alone do not facilitate learning of linguistic categories. However, experiments in this paradigm expose participants to entirely novel words, whereas real language learners encounter input that contains some known words that are semantically organized. In three experiments, we show that (a) the presence of familiar semantic reference points facilitates distributional learning and (b) this effect crucially depends both on the presence of known words and the adherence of these known words to some semantic organization. Copyright © 2016 Cognitive Science Society, Inc.

Designing learning management system interoperability in semantic web

NASA Astrophysics Data System (ADS)

Anistyasari, Y.; Sarno, R.; Rochmawati, N.

2018-01-01

The extensive adoption of learning management system (LMS) has set the focus on the interoperability requirement. Interoperability is the ability of different computer systems, applications or services to communicate, share and exchange data, information, and knowledge in a precise, effective and consistent way. Semantic web technology and the use of ontologies are able to provide the required computational semantics and interoperability for the automation of tasks in LMS. The purpose of this study is to design learning management system interoperability in the semantic web which currently has not been investigated deeply. Moodle is utilized to design the interoperability. Several database tables of Moodle are enhanced and some features are added. The semantic web interoperability is provided by exploited ontology in content materials. The ontology is further utilized as a searching tool to match user’s queries and available courses. It is concluded that LMS interoperability in Semantic Web is possible to be performed.

Incrementally Dissociating Syntax and Semantics

ERIC Educational Resources Information Center

Brennan, Jonathan R.

2010-01-01

A basic challenge for research into the neurobiology of language is understanding how the brain combines words to make complex representations. Linguistic theory divides this task into several computations including syntactic structure building and semantic composition. The close relationship between these computations, however, poses a strong…

Predicting Word Maturity from Frequency and Semantic Diversity: A Computational Study

ERIC Educational Resources Information Center

Jorge-Botana, Guillermo; Olmos, Ricardo; Sanjosé, Vicente

2017-01-01

Semantic word representation changes over different ages of childhood until it reaches its adult form. One method to formally model this change is the word maturity paradigm. This method uses a text sample for each age, including adult age, and transforms the samples into a semantic space by means of Latent Semantic Analysis. The representation of…

Adult and Child Semantic Neighbors of the Kroll and Potter (1984) Nonobjects

PubMed Central

Storkel, Holly L.; Adlof, Suzanne M.

2008-01-01

Purpose The purpose was to determine the number of semantic neighbors, namely semantic set size, for 88 nonobjects (Kroll & Potter, 1984) and determine how semantic set size related to other measures and age. Method Data were collected from 82 adults and 92 preschool children in a discrete association task. The nonobjects were presented via computer, and participants reported the first word that came to mind that was meaningfully related to the nonobject. Words reported by two or more participants were considered semantic neighbors. The strength of each neighbor was computed as the proportion of participants who reported the neighbor. Results Results showed that semantic set size was not significantly correlated with objectlikeness ratings or object decision reaction times from Kroll and Potter (1984). However, semantic set size was significantly negatively correlated with the strength of the strongest neighbor(s). In terms of age effects, adult and child semantic set sizes were significantly positively correlated and the majority of numeric differences were on the order of 0–3 neighbors. Comparison of actual neighbors showed greater discrepancies; however, this varied by neighbor strength. Conclusions Semantic set size can be determined for nonobjects. Specific guidelines are suggested for using these nonobjects in future research. PMID:19252127

A Semantic Grid Oriented to E-Tourism

NASA Astrophysics Data System (ADS)

Zhang, Xiao Ming

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations

PubMed Central

Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. PMID:24632078

An ’Active Vision’ Computational Model of Visual Search for Human-Computer Interaction

DTIC Science & Technology

2009-01-01

semantically related (e.g. cashew , peanut, almond) or randomly grouped (e.g. elm, eraser, potato). Groups were either labeled or not. In some...colored region were further semantically related (e.g. nuts with candy, and clothing with cosmetics). Layouts always contained 28 eight groups with

Grounding Collaborative Learning in Semantics-Based Critiquing

ERIC Educational Resources Information Center

Cheung, William K.; Mørch, Anders I.; Wong, Kelvin C.; Lee, Cynthia; Liu, Jiming; Lam, Mason H.

2007-01-01

In this article we investigate the use of latent semantic analysis (LSA), critiquing systems, and knowledge building to support computer-based teaching of English composition. We have built and tested an English composition critiquing system that makes use of LSA to analyze student essays and compute feedback by comparing their essays with…

Extracting Useful Semantic Information from Large Scale Corpora of Text

ERIC Educational Resources Information Center

Mendoza, Ray Padilla, Jr.

2012-01-01

Extracting and representing semantic information from large scale corpora is at the crux of computer-assisted knowledge generation. Semantic information depends on collocation extraction methods, mathematical models used to represent distributional information, and weighting functions which transform the space. This dissertation provides a…

Semantic Web Services Challenge, Results from the First Year. Series: Semantic Web And Beyond, Volume 8.

NASA Astrophysics Data System (ADS)

Petrie, C.; Margaria, T.; Lausen, H.; Zaremba, M.

Explores trade-offs among existing approaches. Reveals strengths and weaknesses of proposed approaches, as well as which aspects of the problem are not yet covered. Introduces software engineering approach to evaluating semantic web services. Service-Oriented Computing is one of the most promising software engineering trends because of the potential to reduce the programming effort for future distributed industrial systems. However, only a small part of this potential rests on the standardization of tools offered by the web services stack. The larger part of this potential rests upon the development of sufficient semantics to automate service orchestration. Currently there are many different approaches to semantic web service descriptions and many frameworks built around them. A common understanding, evaluation scheme, and test bed to compare and classify these frameworks in terms of their capabilities and shortcomings, is necessary to make progress in developing the full potential of Service-Oriented Computing. The Semantic Web Services Challenge is an open source initiative that provides a public evaluation and certification of multiple frameworks on common industrially-relevant problem sets. This edited volume reports on the first results in developing common understanding of the various technologies intended to facilitate the automation of mediation, choreography and discovery for Web Services using semantic annotations. Semantic Web Services Challenge: Results from the First Year is designed for a professional audience composed of practitioners and researchers in industry. Professionals can use this book to evaluate SWS technology for their potential practical use. The book is also suitable for advanced-level students in computer science.

"Truth be told" - Semantic memory as the scaffold for veridical communication.

PubMed

Hayes, Brett K; Ramanan, Siddharth; Irish, Muireann

2018-01-01

Theoretical accounts placing episodic memory as central to constructive and communicative functions neglect the role of semantic memory. We argue that the decontextualized nature of semantic schemas largely supersedes the computational bottleneck and error-prone nature of episodic memory. Rather, neuroimaging and neuropsychological evidence of episodic-semantic interactions suggest that an integrative framework more accurately captures the mechanisms underpinning social communication.

Ubiquitous Computing Services Discovery and Execution Using a Novel Intelligent Web Services Algorithm

PubMed Central

Choi, Okkyung; Han, SangYong

2007-01-01

Ubiquitous Computing makes it possible to determine in real time the location and situations of service requesters in a web service environment as it enables access to computers at any time and in any place. Though research on various aspects of ubiquitous commerce is progressing at enterprises and research centers, both domestically and overseas, analysis of a customer's personal preferences based on semantic web and rule based services using semantics is not currently being conducted. This paper proposes a Ubiquitous Computing Services System that enables a rule based search as well as semantics based search to support the fact that the electronic space and the physical space can be combined into one and the real time search for web services and the construction of efficient web services thus become possible.

Putting semantics into the semantic web: how well can it capture biology?

PubMed

Kazic, Toni

2006-01-01

Could the Semantic Web work for computations of biological interest in the way it's intended to work for movie reviews and commercial transactions? It would be wonderful if it could, so it's worth looking to see if its infrastructure is adequate to the job. The technologies of the Semantic Web make several crucial assumptions. I examine those assumptions; argue that they create significant problems; and suggest some alternative ways of achieving the Semantic Web's goals for biology.

BIOSSES: a semantic sentence similarity estimation system for the biomedical domain.

PubMed

Sogancioglu, Gizem; Öztürk, Hakime; Özgür, Arzucan

2017-07-15

The amount of information available in textual format is rapidly increasing in the biomedical domain. Therefore, natural language processing (NLP) applications are becoming increasingly important to facilitate the retrieval and analysis of these data. Computing the semantic similarity between sentences is an important component in many NLP tasks including text retrieval and summarization. A number of approaches have been proposed for semantic sentence similarity estimation for generic English. However, our experiments showed that such approaches do not effectively cover biomedical knowledge and produce poor results for biomedical text. We propose several approaches for sentence-level semantic similarity computation in the biomedical domain, including string similarity measures and measures based on the distributed vector representations of sentences learned in an unsupervised manner from a large biomedical corpus. In addition, ontology-based approaches are presented that utilize general and domain-specific ontologies. Finally, a supervised regression based model is developed that effectively combines the different similarity computation metrics. A benchmark data set consisting of 100 sentence pairs from the biomedical literature is manually annotated by five human experts and used for evaluating the proposed methods. The experiments showed that the supervised semantic sentence similarity computation approach obtained the best performance (0.836 correlation with gold standard human annotations) and improved over the state-of-the-art domain-independent systems up to 42.6% in terms of the Pearson correlation metric. A web-based system for biomedical semantic sentence similarity computation, the source code, and the annotated benchmark data set are available at: http://tabilab.cmpe.boun.edu.tr/BIOSSES/ . gizemsogancioglu@gmail.com or arzucan.ozgur@boun.edu.tr. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Quest for a Computerised Semantics.

ERIC Educational Resources Information Center

Leslie, Adrian R.

The objective of this thesis was to colligate the various strands of research in the literature of computational linguistics that have to do with the computational treatment of semantic content so as to encode it into a computerized dictionary. In chapter 1 the course of mechanical translation (1947-1960) and quantitative linguistics is traced to…

Philosophy of Language. Course Notes for a Tutorial on Computational Semantics.

ERIC Educational Resources Information Center

Wilks, Yorick

This course was part of a tutorial focusing on the state of computational semantics, i.e., the state of work on natural language within the artificial intelligence (AI) paradigm. The discussion in the course centered on the philosophers Richard Montague and Ludwig Wittgenstein. The course was divided into three sections: (1)…

The Semantic Web in Teacher Education

ERIC Educational Resources Information Center

Czerkawski, Betül Özkan

2014-01-01

The Semantic Web enables increased collaboration among computers and people by organizing unstructured data on the World Wide Web. Rather than a separate body, the Semantic Web is a functional extension of the current Web made possible by defining relationships among websites and other online content. When explicitly defined, these relationships…

Structure and Deterioration of Semantic Memory: A Neuropsychological and Computational Investigation

ERIC Educational Resources Information Center

Rogers, Timothy T.; Lambon Ralph, Matthew A.; Garrard, Peter; Bozeat, Sasha; McClelland, James L.; Hodges, John R.; Patterson, Karalyn

2004-01-01

Wernicke (1900, as cited in G. H. Eggert, 1977) suggested that semantic knowledge arises from the interaction of perceptual representations of objects and words. The authors present a parallel distributed processing implementation of this theory, in which semantic representations emerge from mechanisms that acquire the mappings between visual…

Computable visually observed phenotype ontological framework for plants

PubMed Central

2011-01-01

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

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language

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

De Jong, Wibe A.; Walker, Andrew M.; Hanwell, Marcus D.

Background Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper the generation of semantically rich data from the NWChem computational chemistry software is discussed within the Chemical Markup Language (CML) framework. Results The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files used by the computational chemistry software. Conclusions The production of CML compliant XMLmore » files for the computational chemistry software NWChem can be relatively easily accomplished using the FoX library. A unified computational chemistry or CompChem convention and dictionary needs to be developed through a community-based effort. The long-term goal is to enable a researcher to do Google-style chemistry and physics searches.« less

GenieTutor: A Computer Assisted Second-Language Learning System Based on Semantic and Grammar Correctness Evaluations

ERIC Educational Resources Information Center

Kwon, Oh-Woog; Lee, Kiyoung; Kim, Young-Kil; Lee, Yunkeun

2015-01-01

This paper introduces a Dialog-Based Computer-Assisted second-Language Learning (DB-CALL) system using semantic and grammar correctness evaluations and the results of its experiment. While the system dialogues with English learners about a given topic, it automatically evaluates the grammar and content properness of their English utterances, then…

Distinctive Features Hold a Privileged Status in the Computation of Word Meaning: Implications for Theories of Semantic Memory

ERIC Educational Resources Information Center

Cree, George S.; McNorgan, Chris; McRae, Ken

2006-01-01

The authors present data from 2 feature verification experiments designed to determine whether distinctive features have a privileged status in the computation of word meaning. They use an attractor-based connectionist model of semantic memory to derive predictions for the experiments. Contrary to central predictions of the conceptual structure…

Latent Semantic Analysis as a Method of Content-Based Image Retrieval in Medical Applications

ERIC Educational Resources Information Center

Makovoz, Gennadiy

2010-01-01

The research investigated whether a Latent Semantic Analysis (LSA)-based approach to image retrieval can map pixel intensity into a smaller concept space with good accuracy and reasonable computational cost. From a large set of M computed tomography (CT) images, a retrieval query found all images for a particular patient based on semantic…

Studies of Human Memory and Language Processing.

ERIC Educational Resources Information Center

Collins, Allan M.

The purposes of this study were to determine the nature of human semantic memory and to obtain knowledge usable in the future development of computer systems that can converse with people. The work was based on a computer model which is designed to comprehend English text, relating the text to information stored in a semantic data base that is…

Automated Semantic Indices Related to Cognitive Function and Rate of Cognitive Decline

ERIC Educational Resources Information Center

Pakhomov, Serguei V. S.; Hemmy, Laura S.; Lim, Kelvin O.

2012-01-01

The objective of our study is to introduce a fully automated, computational linguistic technique to quantify semantic relations between words generated on a standard semantic verbal fluency test and to determine its cognitive and clinical correlates. Cognitive differences between patients with Alzheimer's disease and mild cognitive impairment are…

High-Dimensional Semantic Space Accounts of Priming

ERIC Educational Resources Information Center

Jones, Michael N.; Kintsch, Walter; Mewhort, Douglas J. K.

2006-01-01

A broad range of priming data has been used to explore the structure of semantic memory and to test between models of word representation. In this paper, we examine the computational mechanisms required to learn distributed semantic representations for words directly from unsupervised experience with language. To best account for the variety of…

Research on Extension of Sparql Ontology Query Language Considering the Computation of Indoor Spatial Relations

NASA Astrophysics Data System (ADS)

Li, C.; Zhu, X.; Guo, W.; Liu, Y.; Huang, H.

2015-05-01

A method suitable for indoor complex semantic query considering the computation of indoor spatial relations is provided According to the characteristics of indoor space. This paper designs ontology model describing the space related information of humans, events and Indoor space objects (e.g. Storey and Room) as well as their relations to meet the indoor semantic query. The ontology concepts are used in IndoorSPARQL query language which extends SPARQL syntax for representing and querying indoor space. And four types specific primitives for indoor query, "Adjacent", "Opposite", "Vertical" and "Contain", are defined as query functions in IndoorSPARQL used to support quantitative spatial computations. Also a method is proposed to analysis the query language. Finally this paper adopts this method to realize indoor semantic query on the study area through constructing the ontology model for the study building. The experimental results show that the method proposed in this paper can effectively support complex indoor space semantic query.

Social Semantics for an Effective Enterprise

NASA Technical Reports Server (NTRS)

Berndt, Sarah; Doane, Mike

2012-01-01

An evolution of the Semantic Web, the Social Semantic Web (s2w), facilitates knowledge sharing with "useful information based on human contributions, which gets better as more people participate." The s2w reaches beyond the search box to move us from a collection of hyperlinked facts, to meaningful, real time context. When focused through the lens of Enterprise Search, the Social Semantic Web facilitates the fluid transition of meaningful business information from the source to the user. It is the confluence of human thought and computer processing structured with the iterative application of taxonomies, folksonomies, ontologies, and metadata schemas. The importance and nuances of human interaction are often deemphasized when focusing on automatic generation of semantic markup, which results in dissatisfied users and unrealized return on investment. Users consistently qualify the value of information sets through the act of selection, making them the de facto stakeholders of the Social Semantic Web. Employers are the ultimate beneficiaries of s2w utilization with a better informed, more decisive workforce; one not achieved with an IT miracle technology, but by improved human-computer interactions. Johnson Space Center Taxonomist Sarah Berndt and Mike Doane, principal owner of Term Management, LLC discuss the planning, development, and maintenance stages for components of a semantic system while emphasizing the necessity of a Social Semantic Web for the Enterprise. Identification of risks and variables associated with layering the successful implementation of a semantic system are also modeled.

Problem Solving with General Semantics.

ERIC Educational Resources Information Center

Hewson, David

1996-01-01

Discusses how to use general semantics formulations to improve problem solving at home or at work--methods come from the areas of artificial intelligence/computer science, engineering, operations research, and psychology. (PA)

DOORS to the semantic web and grid with a PORTAL for biomedical computing.

PubMed

Taswell, Carl

2008-03-01

The semantic web remains in the early stages of development. It has not yet achieved the goals envisioned by its founders as a pervasive web of distributed knowledge and intelligence. Success will be attained when a dynamic synergism can be created between people and a sufficient number of infrastructure systems and tools for the semantic web in analogy with those for the original web. The domain name system (DNS), web browsers, and the benefits of publishing web pages motivated many people to register domain names and publish web sites on the original web. An analogous resource label system, semantic search applications, and the benefits of collaborative semantic networks will motivate people to register resource labels and publish resource descriptions on the semantic web. The Domain Ontology Oriented Resource System (DOORS) and Problem Oriented Registry of Tags and Labels (PORTAL) are proposed as infrastructure systems for resource metadata within a paradigm that can serve as a bridge between the original web and the semantic web. The Internet Registry Information Service (IRIS) registers [corrected] domain names while DNS publishes domain addresses with mapping of names to addresses for the original web. Analogously, PORTAL registers resource labels and tags while DOORS publishes resource locations and descriptions with mapping of labels to locations for the semantic web. BioPORT is proposed as a prototype PORTAL registry specific for the problem domain of biomedical computing.

Methods and apparatus for multi-resolution replication of files in a parallel computing system using semantic information

DOEpatents

Faibish, Sorin; Bent, John M.; Tzelnic, Percy; Grider, Gary; Torres, Aaron

2015-10-20

Techniques are provided for storing files in a parallel computing system using different resolutions. A method is provided for storing at least one file generated by a distributed application in a parallel computing system. The file comprises one or more of a complete file and a sub-file. The method comprises the steps of obtaining semantic information related to the file; generating a plurality of replicas of the file with different resolutions based on the semantic information; and storing the file and the plurality of replicas of the file in one or more storage nodes of the parallel computing system. The different resolutions comprise, for example, a variable number of bits and/or a different sub-set of data elements from the file. A plurality of the sub-files can be merged to reproduce the file.

Knowledge-Base Semantic Gap Analysis for the Vulnerability Detection

NASA Astrophysics Data System (ADS)

Wu, Raymond; Seki, Keisuke; Sakamoto, Ryusuke; Hisada, Masayuki

Web security became an alert in internet computing. To cope with ever-rising security complexity, semantic analysis is proposed to fill-in the gap that the current approaches fail to commit. Conventional methods limit their focus to the physical source codes instead of the abstraction of semantics. It bypasses new types of vulnerability and causes tremendous business loss.

Semantic Metrics for Analysis of Software

NASA Technical Reports Server (NTRS)

Etzkorn, Letha H.; Cox, Glenn W.; Farrington, Phil; Utley, Dawn R.; Ghalston, Sampson; Stein, Cara

2005-01-01

A recently conceived suite of object-oriented software metrics focus is on semantic aspects of software, in contradistinction to traditional software metrics, which focus on syntactic aspects of software. Semantic metrics represent a more human-oriented view of software than do syntactic metrics. The semantic metrics of a given computer program are calculated by use of the output of a knowledge-based analysis of the program, and are substantially more representative of software quality and more readily comprehensible from a human perspective than are the syntactic metrics.

Bibliography on the Semantics of Human Language. Linguistic Bibliography Series, No. 1.

ERIC Educational Resources Information Center

Hofmann, Thomas R.

This bibliography, prepared and stored on a computer, is intended to aid in locating works on the semantics of human language. As a reference bibliography, it presents as many different places and modes of publication as possible, and includes as wide a range of subjects as possible. It is intended to cover all of linguistic semantics, with…

Hierarchical layered and semantic-based image segmentation using ergodicity map

NASA Astrophysics Data System (ADS)

Yadegar, Jacob; Liu, Xiaoqing

2010-04-01

Image segmentation plays a foundational role in image understanding and computer vision. Although great strides have been made and progress achieved on automatic/semi-automatic image segmentation algorithms, designing a generic, robust, and efficient image segmentation algorithm is still challenging. Human vision is still far superior compared to computer vision, especially in interpreting semantic meanings/objects in images. We present a hierarchical/layered semantic image segmentation algorithm that can automatically and efficiently segment images into hierarchical layered/multi-scaled semantic regions/objects with contextual topological relationships. The proposed algorithm bridges the gap between high-level semantics and low-level visual features/cues (such as color, intensity, edge, etc.) through utilizing a layered/hierarchical ergodicity map, where ergodicity is computed based on a space filling fractal concept and used as a region dissimilarity measurement. The algorithm applies a highly scalable, efficient, and adaptive Peano- Cesaro triangulation/tiling technique to decompose the given image into a set of similar/homogenous regions based on low-level visual cues in a top-down manner. The layered/hierarchical ergodicity map is built through a bottom-up region dissimilarity analysis. The recursive fractal sweep associated with the Peano-Cesaro triangulation provides efficient local multi-resolution refinement to any level of detail. The generated binary decomposition tree also provides efficient neighbor retrieval mechanisms for contextual topological object/region relationship generation. Experiments have been conducted within the maritime image environment where the segmented layered semantic objects include the basic level objects (i.e. sky/land/water) and deeper level objects in the sky/land/water surfaces. Experimental results demonstrate the proposed algorithm has the capability to robustly and efficiently segment images into layered semantic objects/regions with contextual topological relationships.

[Artificial intelligence meeting neuropsychology. Semantic memory in normal and pathological aging].

PubMed

Aimé, Xavier; Charlet, Jean; Maillet, Didier; Belin, Catherine

2015-03-01

Artificial intelligence (IA) is the subject of much research, but also many fantasies. It aims to reproduce human intelligence in its learning capacity, knowledge storage and computation. In 2014, the Defense Advanced Research Projects Agency (DARPA) started the restoring active memory (RAM) program that attempt to develop implantable technology to bridge gaps in the injured brain and restore normal memory function to people with memory loss caused by injury or disease. In another IA's field, computational ontologies (a formal and shared conceptualization) try to model knowledge in order to represent a structured and unambiguous meaning of the concepts of a target domain. The aim of these structures is to ensure a consensual understanding of their meaning and a univariant use (the same concept is used by all to categorize the same individuals). The first representations of knowledge in the AI's domain are largely based on model tests of semantic memory. This one, as a component of long-term memory is the memory of words, ideas, concepts. It is the only declarative memory system that resists so remarkably to the effects of age. In contrast, non-specific cognitive changes may decrease the performance of elderly in various events and instead report difficulties of access to semantic representations that affect the semantics stock itself. Some dementias, like semantic dementia and Alzheimer's disease, are linked to alteration of semantic memory. We propose in this paper, using the computational ontologies model, a formal and relatively thin modeling, in the service of neuropsychology: 1) for the practitioner with decision support systems, 2) for the patient as cognitive prosthesis outsourced, and 3) for the researcher to study semantic memory.

Building a Semantic Framework for eScience

NASA Astrophysics Data System (ADS)

Movva, S.; Ramachandran, R.; Maskey, M.; Li, X.

2009-12-01

The e-Science vision focuses on the use of advanced computing technologies to support scientists. Recent research efforts in this area have focused primarily on “enabling” use of infrastructure resources for both data and computational access especially in Geosciences. One of the existing gaps in the existing e-Science efforts has been the failure to incorporate stable semantic technologies within the design process itself. In this presentation, we describe our effort in designing a framework for e-Science built using Service Oriented Architecture. Our framework provides users capabilities to create science workflows and mine distributed data. Our e-Science framework is being designed around a mass market tool to promote reusability across many projects. Semantics is an integral part of this framework and our design goal is to leverage the latest stable semantic technologies. The use of these stable semantic technologies will provide the users of our framework the useful features such as: allow search engines to find their content with RDFa tags; create RDF triple data store for their content; create RDF end points to share with others; and semantically mash their content with other online content available as RDF end point.

Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

PubMed

Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

2016-01-01

Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

Interleaving Semantic Web Reasoning and Service Discovery to Enforce Context-Sensitive Security and Privacy Policies

DTIC Science & Technology

2005-07-01

policies in pervasive computing environments. In this context, the owner of information sources (e.g. user, sensor, application, or organization...work in decentralized trust management and semantic web technologies . Section 3 introduces an Information Disclosure Agent architecture for...Norman Sadeh July 2005 CMU-ISRI-05-113 School of Computer Science, Carnegie Mellon University 5000 Forbes Avenue, Pittsburgh, PA, 15213

Developing Visualization Techniques for Semantics-based Information Networks

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Hall, David R.

2003-01-01

Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

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

NASA Astrophysics Data System (ADS)

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

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

Co-occurrence frequency evaluated with large language corpora boosts semantic priming effects.

PubMed

Brunellière, Angèle; Perre, Laetitia; Tran, ThiMai; Bonnotte, Isabelle

2017-09-01

In recent decades, many computational techniques have been developed to analyse the contextual usage of words in large language corpora. The present study examined whether the co-occurrence frequency obtained from large language corpora might boost purely semantic priming effects. Two experiments were conducted: one with conscious semantic priming, the other with subliminal semantic priming. Both experiments contrasted three semantic priming contexts: an unrelated priming context and two related priming contexts with word pairs that are semantically related and that co-occur either frequently or infrequently. In the conscious priming presentation (166-ms stimulus-onset asynchrony, SOA), a semantic priming effect was recorded in both related priming contexts, which was greater with higher co-occurrence frequency. In the subliminal priming presentation (66-ms SOA), no significant priming effect was shown, regardless of the related priming context. These results show that co-occurrence frequency boosts pure semantic priming effects and are discussed with reference to models of semantic network.

Mediator infrastructure for information integration and semantic data integration environment for biomedical research.

PubMed

Grethe, Jeffrey S; Ross, Edward; Little, David; Sanders, Brian; Gupta, Amarnath; Astakhov, Vadim

2009-01-01

This paper presents current progress in the development of semantic data integration environment which is a part of the Biomedical Informatics Research Network (BIRN; http://www.nbirn.net) project. BIRN is sponsored by the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH). A goal is the development of a cyberinfrastructure for biomedical research that supports advance data acquisition, data storage, data management, data integration, data mining, data visualization, and other computing and information processing services over the Internet. Each participating institution maintains storage of their experimental or computationally derived data. Mediator-based data integration system performs semantic integration over the databases to enable researchers to perform analyses based on larger and broader datasets than would be available from any single institution's data. This paper describes recent revision of the system architecture, implementation, and capabilities of the semantically based data integration environment for BIRN.

The semantic web and computer vision: old AI meets new AI

NASA Astrophysics Data System (ADS)

Mundy, J. L.; Dong, Y.; Gilliam, A.; Wagner, R.

2018-04-01

There has been vast process in linking semantic information across the billions of web pages through the use of ontologies encoded in the Web Ontology Language (OWL) based on the Resource Description Framework (RDF). A prime example is the Wikipedia where the knowledge contained in its more than four million pages is encoded in an ontological database called DBPedia http://wiki.dbpedia.org/. Web-based query tools can retrieve semantic information from DBPedia encoded in interlinked ontologies that can be accessed using natural language. This paper will show how this vast context can be used to automate the process of querying images and other geospatial data in support of report changes in structures and activities. Computer vision algorithms are selected and provided with context based on natural language requests for monitoring and analysis. The resulting reports provide semantically linked observations from images and 3D surface models.

Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

PubMed

Depeursinge, Adrien; Kurtz, Camille; Beaulieu, Christopher; Napel, Sandy; Rubin, Daniel

2014-08-01

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

Semantic Pattern Analysis for Verbal Fluency Based Assessment of Neurological Disorders

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

Sukumar, Sreenivas R; Ainsworth, Keela C; Brown, Tyler C

In this paper, we present preliminary results of semantic pattern analysis of verbal fluency tests used for assessing cognitive psychological and neuropsychological disorders. We posit that recent advances in semantic reasoning and artificial intelligence can be combined to create a standardized computer-aided diagnosis tool to automatically evaluate and interpret verbal fluency tests. Towards that goal, we derive novel semantic similarity (phonetic, phonemic and conceptual) metrics and present the predictive capability of these metrics on a de-identified dataset of participants with and without neurological disorders.

Guidance of visual attention by semantic information in real-world scenes

PubMed Central

Wu, Chia-Chien; Wick, Farahnaz Ahmed; Pomplun, Marc

2014-01-01

Recent research on attentional guidance in real-world scenes has focused on object recognition within the context of a scene. This approach has been valuable for determining some factors that drive the allocation of visual attention and determine visual selection. This article provides a review of experimental work on how different components of context, especially semantic information, affect attentional deployment. We review work from the areas of object recognition, scene perception, and visual search, highlighting recent studies examining semantic structure in real-world scenes. A better understanding on how humans parse scene representations will not only improve current models of visual attention but also advance next-generation computer vision systems and human-computer interfaces. PMID:24567724

A computational language approach to modeling prose recall in schizophrenia

PubMed Central

Rosenstein, Mark; Diaz-Asper, Catherine; Foltz, Peter W.; Elvevåg, Brita

2014-01-01

Many cortical disorders are associated with memory problems. In schizophrenia, verbal memory deficits are a hallmark feature. However, the exact nature of this deficit remains elusive. Modeling aspects of language features used in memory recall have the potential to provide means for measuring these verbal processes. We employ computational language approaches to assess time-varying semantic and sequential properties of prose recall at various retrieval intervals (immediate, 30 min and 24 h later) in patients with schizophrenia, unaffected siblings and healthy unrelated control participants. First, we model the recall data to quantify the degradation of performance with increasing retrieval interval and the effect of diagnosis (i.e., group membership) on performance. Next we model the human scoring of recall performance using an n-gram language sequence technique, and then with a semantic feature based on Latent Semantic Analysis. These models show that automated analyses of the recalls can produce scores that accurately mimic human scoring. The final analysis addresses the validity of this approach by ascertaining the ability to predict group membership from models built on the two classes of language features. Taken individually, the semantic feature is most predictive, while a model combining the features improves accuracy of group membership prediction slightly above the semantic feature alone as well as over the human rating approach. We discuss the implications for cognitive neuroscience of such a computational approach in exploring the mechanisms of prose recall. PMID:24709122

Research on Interactive Acquisition and Use of Knowledge.

DTIC Science & Technology

1983-11-01

complex and challenging endeavor. Computer scientists faced with the problem of managing software complexity have de - veloped strict design disciplines...handle most-indeed, probably all-- phenomena in the syntax and semantics of natural language. It has also turned out to be well suited for the classes of...Semantics The previous grammar performs a de facto coordination of syntax and semantics by requiring that the (syntactically) preverbal NP play the

Architecture for WSN Nodes Integration in Context Aware Systems Using Semantic Messages

NASA Astrophysics Data System (ADS)

Larizgoitia, Iker; Muguira, Leire; Vazquez, Juan Ignacio

Wireless sensor networks (WSN) are becoming extremely popular in the development of context aware systems. Traditionally WSN have been focused on capturing data, which was later analyzed and interpreted in a server with more computational power. In this kind of scenario the problem of representing the sensor information needs to be addressed. Every node in the network might have different sensors attached; therefore their correspondent packet structures will be different. The server has to be aware of the meaning of every single structure and data in order to be able to interpret them. Multiple sensors, multiple nodes, multiple packet structures (and not following a standard format) is neither scalable nor interoperable. Context aware systems have solved this problem with the use of semantic technologies. They provide a common framework to achieve a standard definition of any domain. Nevertheless, these representations are computationally expensive, so a WSN cannot afford them. The work presented in this paper tries to bridge the gap between the sensor information and its semantic representation, by defining a simple architecture that enables the definition of this information natively in a semantic way, achieving the integration of the semantic information in the network packets. This will have several benefits, the most important being the possibility of promoting every WSN node to a real semantic information source.

A Semantics of Synchronization.

DTIC Science & Technology

1980-09-01

suggestion of having very hungry philosophers. One can easily imagine the complexity of the equivalent implementation using semaphores . Synchronization types...Edinburgh, July 1978. [STAR79] Stark, E.W., " Semaphore Primitives and Fair Mutual Exclusion," TM-158, Laboratory for Computer Science, M.I.T., Cambridge...AD-AQ91 015 MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR COMPUTE--ETC F/S 9/2 A SEMANTICS OF SYNCHRONIZATION .(U) .C SEP 80 C A SEAQUIST N00015-75

The Formal Semantics of PVS

NASA Technical Reports Server (NTRS)

Owre, Sam; Shankar, Natarajan

1999-01-01

A specification language is a medium for expressing what is computed rather than how it is computed. Specification languages share some features with programming languages but are also different in several important ways. For our purpose, a specification language is a logic within which the behavior of computational systems can be formalized. Although a specification can be used to simulate the behavior of such systems, we mainly use specifications to state and prove system properties with mechanical assistance. We present the formal semantics of the specification language of SRI's Prototype Verification System (PVS). This specification language is based on the simply typed lambda calculus. The novelty in PVS is that it contains very expressive language features whose static analysis (e.g., typechecking) requires the assistance of a theorem prover. The formal semantics illuminates several of the design considerations underlying PVS, the interaction between theorem proving and typechecking.

SciFlo: Semantically-Enabled Grid Workflow for Collaborative Science

NASA Astrophysics Data System (ADS)

Yunck, T.; Wilson, B. D.; Raskin, R.; Manipon, G.

2005-12-01

SciFlo is a system for Scientific Knowledge Creation on the Grid using a Semantically-Enabled Dataflow Execution Environment. SciFlo leverages Simple Object Access Protocol (SOAP) Web Services and the Grid Computing standards (WS-* standards and the Globus Alliance toolkits), and enables scientists to do multi-instrument Earth Science by assembling reusable SOAP Services, native executables, local command-line scripts, and python codes into a distributed computing flow (a graph of operators). SciFlo's XML dataflow documents can be a mixture of concrete operators (fully bound operations) and abstract template operators (late binding via semantic lookup). All data objects and operators can be both simply typed (simple and complex types in XML schema) and semantically typed using controlled vocabularies (linked to OWL ontologies such as SWEET). By exploiting ontology-enhanced search and inference, one can discover (and automatically invoke) Web Services and operators that have been semantically labeled as performing the desired transformation, and adapt a particular invocation to the proper interface (number, types, and meaning of inputs and outputs). The SciFlo client & server engines optimize the execution of such distributed data flows and allow the user to transparently find and use datasets and operators without worrying about the actual location of the Grid resources. The scientist injects a distributed computation into the Grid by simply filling out an HTML form or directly authoring the underlying XML dataflow document, and results are returned directly to the scientist's desktop. A Visual Programming tool is also being developed, but it is not required. Once an analysis has been specified for a granule or day of data, it can be easily repeated with different control parameters and over months or years of data. SciFlo uses and preserves semantics, and also generates and infers new semantic annotations. Specifically, the SciFlo engine uses semantic metadata to understand (infer) what it is doing and potentially improve the data flow; preserves semantics by saving links to the semantics of (metadata describing) the input datasets, related datasets, and the data transformations (algorithms) used to generate downstream products; generates new metadata by allowing the user to add semantic annotations to the generated data products (or simply accept automatically generated provenance annotations); and infers new semantic metadata by understanding and applying logic to the semantics of the data and the transformations performed. Much ontology development still needs to be done but, nevertheless, SciFlo documents provide a substrate for using and preserving more semantics as ontologies develop. We will give a live demonstration of the growing SciFlo network using an example dataflow in which atmospheric temperature and water vapor profiles from three Earth Observing System (EOS) instruments are retrieved using SOAP (geo-location query & data access) services, co-registered, and visually & statistically compared on demand (see http://sciflo.jpl.nasa.gov for more information).

Learning semantic and visual similarity for endomicroscopy video retrieval.

PubMed

Andre, Barbara; Vercauteren, Tom; Buchner, Anna M; Wallace, Michael B; Ayache, Nicholas

2012-06-01

Content-based image retrieval (CBIR) is a valuable computer vision technique which is increasingly being applied in the medical community for diagnosis support. However, traditional CBIR systems only deliver visual outputs, i.e., images having a similar appearance to the query, which is not directly interpretable by the physicians. Our objective is to provide a system for endomicroscopy video retrieval which delivers both visual and semantic outputs that are consistent with each other. In a previous study, we developed an adapted bag-of-visual-words method for endomicroscopy retrieval, called "Dense-Sift," that computes a visual signature for each video. In this paper, we present a novel approach to complement visual similarity learning with semantic knowledge extraction, in the field of in vivo endomicroscopy. We first leverage a semantic ground truth based on eight binary concepts, in order to transform these visual signatures into semantic signatures that reflect how much the presence of each semantic concept is expressed by the visual words describing the videos. Using cross-validation, we demonstrate that, in terms of semantic detection, our intuitive Fisher-based method transforming visual-word histograms into semantic estimations outperforms support vector machine (SVM) methods with statistical significance. In a second step, we propose to improve retrieval relevance by learning an adjusted similarity distance from a perceived similarity ground truth. As a result, our distance learning method allows to statistically improve the correlation with the perceived similarity. We also demonstrate that, in terms of perceived similarity, the recall performance of the semantic signatures is close to that of visual signatures and significantly better than those of several state-of-the-art CBIR methods. The semantic signatures are thus able to communicate high-level medical knowledge while being consistent with the low-level visual signatures and much shorter than them. In our resulting retrieval system, we decide to use visual signatures for perceived similarity learning and retrieval, and semantic signatures for the output of an additional information, expressed in the endoscopist own language, which provides a relevant semantic translation of the visual retrieval outputs.

Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics.

PubMed

Marelli, Marco; Baroni, Marco

2015-07-01

The present work proposes a computational model of morpheme combination at the meaning level. The model moves from the tenets of distributional semantics, and assumes that word meanings can be effectively represented by vectors recording their co-occurrence with other words in a large text corpus. Given this assumption, affixes are modeled as functions (matrices) mapping stems onto derived forms. Derived-form meanings can be thought of as the result of a combinatorial procedure that transforms the stem vector on the basis of the affix matrix (e.g., the meaning of nameless is obtained by multiplying the vector of name with the matrix of -less). We show that this architecture accounts for the remarkable human capacity of generating new words that denote novel meanings, correctly predicting semantic intuitions about novel derived forms. Moreover, the proposed compositional approach, once paired with a whole-word route, provides a new interpretative framework for semantic transparency, which is here partially explained in terms of ease of the combinatorial procedure and strength of the transformation brought about by the affix. Model-based predictions are in line with the modulation of semantic transparency on explicit intuitions about existing words, response times in lexical decision, and morphological priming. In conclusion, we introduce a computational model to account for morpheme combination at the meaning level. The model is data-driven, theoretically sound, and empirically supported, and it makes predictions that open new research avenues in the domain of semantic processing. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

Semantic ambiguity effects on traditional Chinese character naming: A corpus-based approach.

PubMed

Chang, Ya-Ning; Lee, Chia-Ying

2017-11-09

Words are considered semantically ambiguous if they have more than one meaning and can be used in multiple contexts. A number of recent studies have provided objective ambiguity measures by using a corpus-based approach and have demonstrated ambiguity advantages in both naming and lexical decision tasks. Although the predictive power of objective ambiguity measures has been examined in several alphabetic language systems, the effects in logographic languages remain unclear. Moreover, most ambiguity measures do not explicitly address how the various contexts associated with a given word relate to each other. To explore these issues, we computed the contextual diversity (Adelman, Brown, & Quesada, Psychological Science, 17; 814-823, 2006) and semantic ambiguity (Hoffman, Lambon Ralph, & Rogers, Behavior Research Methods, 45; 718-730, 2013) of traditional Chinese single-character words based on the Academia Sinica Balanced Corpus, where contextual diversity was used to evaluate the present semantic space. We then derived a novel ambiguity measure, namely semantic variability, by computing the distance properties of the distinct clusters grouped by the contexts that contained a given word. We demonstrated that semantic variability was superior to semantic diversity in accounting for the variance in naming response times, suggesting that considering the substructure of the various contexts associated with a given word can provide a relatively fine scale of ambiguity information for a word. All of the context and ambiguity measures for 2,418 Chinese single-character words are provided as supplementary materials.

Executive Semantic Processing Is Underpinned by a Large-scale Neural Network: Revealing the Contribution of Left Prefrontal, Posterior Temporal, and Parietal Cortex to Controlled Retrieval and Selection Using TMS

ERIC Educational Resources Information Center

Whitney, Carin; Kirk, Marie; O'Sullivan, Jamie; Ralph, Matthew A. Lambon; Jefferies, Elizabeth

2012-01-01

To understand the meanings of words and objects, we need to have knowledge about these items themselves plus executive mechanisms that compute and manipulate semantic information in a task-appropriate way. The neural basis for semantic control remains controversial. Neuroimaging studies have focused on the role of the left inferior frontal gyrus…

Learning for Semantic Parsing with Kernels under Various Forms of Supervision

DTIC Science & Technology

2007-08-01

natural language sentences to their formal executable meaning representations. This is a challenging problem and is critical for developing computing...sentences are semantically tractable. This indi- cates that Geoquery is more challenging domain for semantic parsing than ATIS. In the past, there have been a...Combining parsers. In Proceedings of the Conference on Em- pirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/ VLC -99), pp. 187–194

A Computational Linguistic Measure of Clustering Behavior on Semantic Verbal Fluency Task Predicts Risk of Future Dementia in the Nun Study

PubMed Central

Pakhomov, Serguei V.S.; Hemmy, Laura S.

2014-01-01

Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer’s disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters. We evaluated a novel automated computational linguistic approach for quantifying clustering behavior. Our approach is based on Latent Semantic Analysis (LSA) for computing strength of semantic relatedness between pairs of words produced in response to SVF test. The mean size of semantic clusters (MCS) and semantic chains (MChS) are calculated based on pairwise relatedness values between words. We evaluated the predictive validity of these measures on a set of 239 participants in the Nun Study, a longitudinal study of aging. All were cognitively intact at baseline assessment, measured with the CERAD battery, and were followed in 18 month waves for up to 20 years. The onset of either dementia or memory impairment were used as outcomes in Cox proportional hazards models adjusted for age and education and censored at follow up waves 5 (6.3 years) and 13 (16.96 years). Higher MCS was associated with 38% reduction in dementia risk at wave 5 and 26% reduction at wave 13, but not with the onset of memory impairment. Higher (+1 SD) MChS was associated with 39% dementia risk reduction at wave 5 but not wave 13, and association with memory impairment was not significant. Higher traditional SVF scores were associated with 22–29% memory impairment and 35–40% dementia risk reduction. SVF scores were not correlated with either MCS or MChS. Our study suggests that an automated approach to measuring clustering behavior can be used to estimate dementia risk in cognitively normal individuals. PMID:23845236

A computational linguistic measure of clustering behavior on semantic verbal fluency task predicts risk of future dementia in the nun study.

PubMed

Pakhomov, Serguei V S; Hemmy, Laura S

2014-06-01

Generative semantic verbal fluency (SVF) tests show early and disproportionate decline relative to other abilities in individuals developing Alzheimer's disease. Optimal performance on SVF tests depends on the efficiency of using clustered organization of semantically related items and the ability to switch between clusters. Traditional approaches to clustering and switching have relied on manual determination of clusters. We evaluated a novel automated computational linguistic approach for quantifying clustering behavior. Our approach is based on Latent Semantic Analysis (LSA) for computing strength of semantic relatedness between pairs of words produced in response to SVF test. The mean size of semantic clusters (MCS) and semantic chains (MChS) are calculated based on pairwise relatedness values between words. We evaluated the predictive validity of these measures on a set of 239 participants in the Nun Study, a longitudinal study of aging. All were cognitively intact at baseline assessment, measured with the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) battery, and were followed in 18-month waves for up to 20 years. The onset of either dementia or memory impairment were used as outcomes in Cox proportional hazards models adjusted for age and education and censored at follow-up waves 5 (6.3 years) and 13 (16.96 years). Higher MCS was associated with 38% reduction in dementia risk at wave 5 and 26% reduction at wave 13, but not with the onset of memory impairment. Higher [+1 standard deviation (SD)] MChS was associated with 39% dementia risk reduction at wave 5 but not wave 13, and association with memory impairment was not significant. Higher traditional SVF scores were associated with 22-29% memory impairment and 35-40% dementia risk reduction. SVF scores were not correlated with either MCS or MChS. Our study suggests that an automated approach to measuring clustering behavior can be used to estimate dementia risk in cognitively normal individuals. Copyright © 2013 Elsevier Ltd. All rights reserved.

Approaching semantic interoperability in Health Level Seven

PubMed Central

Alschuler, Liora

2010-01-01

‘Semantic Interoperability’ is a driving objective behind many of Health Level Seven's standards. The objective in this paper is to take a step back, and consider what semantic interoperability means, assess whether or not it has been achieved, and, if not, determine what concrete next steps can be taken to get closer. A framework for measuring semantic interoperability is proposed, using a technique called the ‘Single Logical Information Model’ framework, which relies on an operational definition of semantic interoperability and an understanding that interoperability improves incrementally. Whether semantic interoperability tomorrow will enable one computer to talk to another, much as one person can talk to another person, is a matter for speculation. It is assumed, however, that what gets measured gets improved, and in that spirit this framework is offered as a means to improvement. PMID:21106995

Structure, function, and behaviour of computational models in systems biology

PubMed Central

2013-01-01

Background Systems Biology develops computational models in order to understand biological phenomena. The increasing number and complexity of such “bio-models” necessitate computer support for the overall modelling task. Computer-aided modelling has to be based on a formal semantic description of bio-models. But, even if computational bio-models themselves are represented precisely in terms of mathematical expressions their full meaning is not yet formally specified and only described in natural language. Results We present a conceptual framework – the meaning facets – which can be used to rigorously specify the semantics of bio-models. A bio-model has a dual interpretation: On the one hand it is a mathematical expression which can be used in computational simulations (intrinsic meaning). On the other hand the model is related to the biological reality (extrinsic meaning). We show that in both cases this interpretation should be performed from three perspectives: the meaning of the model’s components (structure), the meaning of the model’s intended use (function), and the meaning of the model’s dynamics (behaviour). In order to demonstrate the strengths of the meaning facets framework we apply it to two semantically related models of the cell cycle. Thereby, we make use of existing approaches for computer representation of bio-models as much as possible and sketch the missing pieces. Conclusions The meaning facets framework provides a systematic in-depth approach to the semantics of bio-models. It can serve two important purposes: First, it specifies and structures the information which biologists have to take into account if they build, use and exchange models. Secondly, because it can be formalised, the framework is a solid foundation for any sort of computer support in bio-modelling. The proposed conceptual framework establishes a new methodology for modelling in Systems Biology and constitutes a basis for computer-aided collaborative research. PMID:23721297

What is in a contour map? A region-based logical formalization of contour semantics

USGS Publications Warehouse

Usery, E. Lynn; Hahmann, Torsten

2015-01-01

This paper analyses and formalizes contour semantics in a first-order logic ontology that forms the basis for enabling computational common sense reasoning about contour information. The elicited contour semantics comprises four key concepts – contour regions, contour lines, contour values, and contour sets – and their subclasses and associated relations, which are grounded in an existing qualitative spatial ontology. All concepts and relations are illustrated and motivated by physical-geographic features identifiable on topographic contour maps. The encoding of the semantics of contour concepts in first-order logic and a derived conceptual model as basis for an OWL ontology lay the foundation for fully automated, semantically-aware qualitative and quantitative reasoning about contours.

SemVisM: semantic visualizer for medical image

NASA Astrophysics Data System (ADS)

Landaeta, Luis; La Cruz, Alexandra; Baranya, Alexander; Vidal, María.-Esther

2015-01-01

SemVisM is a toolbox that combines medical informatics and computer graphics tools for reducing the semantic gap between low-level features and high-level semantic concepts/terms in the images. This paper presents a novel strategy for visualizing medical data annotated semantically, combining rendering techniques, and segmentation algorithms. SemVisM comprises two main components: i) AMORE (A Modest vOlume REgister) to handle input data (RAW, DAT or DICOM) and to initially annotate the images using terms defined on medical ontologies (e.g., MesH, FMA or RadLex), and ii) VOLPROB (VOlume PRObability Builder) for generating the annotated volumetric data containing the classified voxels that belong to a particular tissue. SemVisM is built on top of the semantic visualizer ANISE.1

Real-time image annotation by manifold-based biased Fisher discriminant analysis

NASA Astrophysics Data System (ADS)

Ji, Rongrong; Yao, Hongxun; Wang, Jicheng; Sun, Xiaoshuai; Liu, Xianming

2008-01-01

Automatic Linguistic Annotation is a promising solution to bridge the semantic gap in content-based image retrieval. However, two crucial issues are not well addressed in state-of-art annotation algorithms: 1. The Small Sample Size (3S) problem in keyword classifier/model learning; 2. Most of annotation algorithms can not extend to real-time online usage due to their low computational efficiencies. This paper presents a novel Manifold-based Biased Fisher Discriminant Analysis (MBFDA) algorithm to address these two issues by transductive semantic learning and keyword filtering. To address the 3S problem, Co-Training based Manifold learning is adopted for keyword model construction. To achieve real-time annotation, a Bias Fisher Discriminant Analysis (BFDA) based semantic feature reduction algorithm is presented for keyword confidence discrimination and semantic feature reduction. Different from all existing annotation methods, MBFDA views image annotation from a novel Eigen semantic feature (which corresponds to keywords) selection aspect. As demonstrated in experiments, our manifold-based biased Fisher discriminant analysis annotation algorithm outperforms classical and state-of-art annotation methods (1.K-NN Expansion; 2.One-to-All SVM; 3.PWC-SVM) in both computational time and annotation accuracy with a large margin.

SLEEC: Semantics-Rich Libraries for Effective Exascale Computation. Final Technical Report

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

Milind, Kulkarni

SLEEC (Semantics-rich Libraries for Effective Exascale Computation) was a project funded by the Department of Energy X-Stack Program, award number DE-SC0008629. The initial project period was September 2012–August 2015. The project was renewed for an additional year, expiring August 2016. Finally, the project received a no-cost extension, leading to a final expiry date of August 2017. Modern applications, especially those intended to run at exascale, are not written from scratch. Instead, they are built by stitching together various carefully-written, hand-tuned libraries. Correctly composing these libraries is difficult, but traditional compilers are unable to effectively analyze and transform across abstraction layers.more » Domain specific compilers integrate semantic knowledge into compilers, allowing them to transform applications that use particular domain-specific languages, or domain libraries. But they do not help when new domains are developed, or applications span multiple domains. SLEEC aims to fix these problems. To do so, we are building generic compiler and runtime infrastructures that are semantics-aware but not domain-specific. By performing optimizations related to the semantics of a domain library, the same infrastructure can be made generic and apply across multiple domains.« less

Semantic Similarity between Web Documents Using Ontology

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Building a biomedical semantic network in Wikipedia with Semantic Wiki Links

PubMed Central

Good, Benjamin M.; Clarke, Erik L.; Loguercio, Salvatore; Su, Andrew I.

2012-01-01

Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in Wikipedia to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, we introduce a straightforward method that allows Wikipedia editors to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, we demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries. Database URL: http://en.wikipedia.org/wiki/Portal:Gene_Wiki PMID:22434829

Building a biomedical semantic network in Wikipedia with Semantic Wiki Links.

PubMed

Good, Benjamin M; Clarke, Erik L; Loguercio, Salvatore; Su, Andrew I

2012-01-01

Wikipedia is increasingly used as a platform for collaborative data curation, but its current technical implementation has significant limitations that hinder its use in biocuration applications. Specifically, while editors can easily link between two articles in Wikipedia to indicate a relationship, there is no way to indicate the nature of that relationship in a way that is computationally accessible to the system or to external developers. For example, in addition to noting a relationship between a gene and a disease, it would be useful to differentiate the cases where genetic mutation or altered expression causes the disease. Here, we introduce a straightforward method that allows Wikipedia editors to embed computable semantic relations directly in the context of current Wikipedia articles. In addition, we demonstrate two novel applications enabled by the presence of these new relationships. The first is a dynamically generated information box that can be rendered on all semantically enhanced Wikipedia articles. The second is a prototype gene annotation system that draws its content from the gene-centric articles on Wikipedia and exposes the new semantic relationships to enable previously impossible, user-defined queries. DATABASE URL: http://en.wikipedia.org/wiki/Portal:Gene_Wiki.

Interests diffusion on a semantic multiplex. Comparing Computer Science and American Physical Society communities

NASA Astrophysics Data System (ADS)

D'Agostino, Gregorio; De Nicola, Antonio

2016-10-01

Exploiting the information about members of a Social Network (SN) represents one of the most attractive and dwelling subjects for both academic and applied scientists. The community of Complexity Science and especially those researchers working on multiplex social systems are devoting increasing efforts to outline general laws, models, and theories, to the purpose of predicting emergent phenomena in SN's (e.g. success of a product). On the other side the semantic web community aims at engineering a new generation of advanced services tailored to specific people needs. This implies defining constructs, models and methods for handling the semantic layer of SNs. We combined models and techniques from both the former fields to provide a hybrid approach to understand a basic (yet complex) phenomenon: the propagation of individual interests along the social networks. Since information may move along different social networks, one should take into account a multiplex structure. Therefore we introduced the notion of "Semantic Multiplex". In this paper we analyse two different semantic social networks represented by authors publishing in the Computer Science and those in the American Physical Society Journals. The comparison allows to outline common and specific features.

Semantic Similarity between Web Documents Using Ontology

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

A Computational Analysis of Complex Noun Phrases in Navy Messages

DTIC Science & Technology

1984-07-01

Hirschman. Automated Determination of Suhlanguage Syntactic Usage. Proc. COLING 84) (current volume). [Hirschman 1082 ] Hirsehman, L. Constraints on...Restricted Semantic Domains. de Grnyter New York, 1082 . [Levi 1078] Levi, J.N. The Syntaz and Semantics of Com- plez Nominals, Academic Press, New York

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

No one way ticket from orthography to semantics in recognition memory: N400 and P200 effects of associations.

PubMed

Stuellein, Nicole; Radach, Ralph R; Jacobs, Arthur M; Hofmann, Markus J

2016-05-15

Computational models of word recognition already successfully used associative spreading from orthographic to semantic levels to account for false memories. But can they also account for semantic effects on event-related potentials in a recognition memory task? To address this question, target words in the present study had either many or few semantic associates in the stimulus set. We found larger P200 amplitudes and smaller N400 amplitudes for old words in comparison to new words. Words with many semantic associates led to larger P200 amplitudes and a smaller N400 in comparison to words with a smaller number of semantic associations. We also obtained inverted response time and accuracy effects for old and new words: faster response times and fewer errors were found for old words that had many semantic associates, whereas new words with a large number of semantic associates produced slower response times and more errors. Both behavioral and electrophysiological results indicate that semantic associations between words can facilitate top-down driven lexical access and semantic integration in recognition memory. Our results support neurophysiologically plausible predictions of the Associative Read-Out Model, which suggests top-down connections from semantic to orthographic layers. Copyright © 2016 Elsevier B.V. All rights reserved.

Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.

PubMed

Qiao, Hong; Li, Yinlin; Li, Fengfu; Xi, Xuanyang; Wu, Wei

2016-10-01

Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability.

Semantic encoding of relational databases in wireless networks

NASA Astrophysics Data System (ADS)

Benjamin, David P.; Walker, Adrian

2005-03-01

Semantic Encoding is a new, patented technology that greatly increases the speed of transmission of distributed databases over networks, especially over ad hoc wireless networks, while providing a novel method of data security. It reduces bandwidth consumption and storage requirements, while speeding up query processing, encryption and computation of digital signatures. We describe the application of Semantic Encoding in a wireless setting and provide an example of its operation in which a compression of 290:1 would be achieved.

Interaction between Phonological and Semantic Representations: Time Matters

ERIC Educational Resources Information Center

Chen, Qi; Mirman, Daniel

2015-01-01

Computational modeling and eye-tracking were used to investigate how phonological and semantic information interact to influence the time course of spoken word recognition. We extended our recent models (Chen & Mirman, 2012; Mirman, Britt, & Chen, 2013) to account for new evidence that competition among phonological neighbors influences…

Semantic Processing for Communicative Exercises in Foreign-Language Learning.

ERIC Educational Resources Information Center

Mulford, George W.

1989-01-01

Outlines the history of semantically based programs that have influenced the design of computer assisted language instruction (CALI) programs. Describes early attempts to make intelligent CALI as well as current projects, including the Foreign Language Adventure Game, developed at the University of Delaware. Describes some important…

The role of attention in subliminal semantic processing: A mouse tracking study.

PubMed

Xiao, Kunchen; Yamauchi, Takashi

2017-01-01

Recent evidence suggests that top-down attention facilitates unconscious semantic processing. To clarify the role of attention in unconscious semantic processing, we traced trajectories of the computer mouse in a semantic priming task and scrutinized the extent to which top-down attention enhances unconscious semantic processing in four different stimulus-onset asynchrony (SOA: 50, 200, 500, or 1000ms) conditions. Participants judged whether a target digit (e.g., "6") was larger or smaller than five, preceded by a masked priming digit (e.g., "9"). The pre-prime duration changed randomly from trial to trial to disrupt participants' top-down attention in an uncued condition (in a cued condition, a green square cue was presented to facilitate participants' top-down attention). The results show that top-down attention modifies the time course of subliminal semantic processing, and the temporal attention window lasts more than 1000ms; attention facilitated by the cue may amplify semantic priming to some extent, yet the amplification effect of attention is relatively minor.

Semantic similarity measure in biomedical domain leverage web search engine.

PubMed

Chen, Chi-Huang; Hsieh, Sheau-Ling; Weng, Yung-Ching; Chang, Wen-Yung; Lai, Feipei

2010-01-01

Semantic similarity measure plays an essential role in Information Retrieval and Natural Language Processing. In this paper we propose a page-count-based semantic similarity measure and apply it in biomedical domains. Previous researches in semantic web related applications have deployed various semantic similarity measures. Despite the usefulness of the measurements in those applications, measuring semantic similarity between two terms remains a challenge task. The proposed method exploits page counts returned by the Web Search Engine. We define various similarity scores for two given terms P and Q, using the page counts for querying P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using lexico-syntactic patterns with page counts. These different similarity scores are integrated adapting support vector machines, to leverage the robustness of semantic similarity measures. Experimental results on two datasets achieve correlation coefficients of 0.798 on the dataset provided by A. Hliaoutakis, 0.705 on the dataset provide by T. Pedersen with physician scores and 0.496 on the dataset provided by T. Pedersen et al. with expert scores.

F-OWL: An Inference Engine for Semantic Web

NASA Technical Reports Server (NTRS)

Zou, Youyong; Finin, Tim; Chen, Harry

2004-01-01

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

UBioLab: a web-LABoratory for Ubiquitous in-silico experiments.

PubMed

Bartocci, E; Di Berardini, M R; Merelli, E; Vito, L

2012-03-01

The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists -for what concerns their management and visualization- and for bioinformaticians -for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle -and possibly to handle in a transparent and uniform way- aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features -as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques- give evidence of an effort in such a direction. The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) "type" of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.

Quantifying Narrative Ability in Autism Spectrum Disorder: A Computational Linguistic Analysis of Narrative Coherence

PubMed Central

Losh, Molly; Gordon, Peter C.

2014-01-01

Autism Spectrum Disorder (ASD) is characterized by difficulties with social communication and functioning, and ritualistic/repetitive behaviors (American Psychiatric Association, 2013). While substantial heterogeneity exists in symptom expression, impairments in language discourse skills, including narrative, are universally observed (Tager-Flusberg, Paul, & Lord, 2005). This study applied a computational linguistic tool, Latent Semantic Analysis (LSA), to objectively characterize narrative performance in ASD across two narrative contexts differing in interpersonal and cognitive demands. Results indicated that individuals with ASD produced narratives comparable in semantic content to those from controls when narrating from a picture book, but produced narratives diminished in semantic quality in a more demanding narrative recall task. Results are discussed in terms of the utility of LSA as a quantitative, objective, and efficient measure of narrative ability. PMID:24915929

Individual differences in white matter microstructure predict semantic control.

PubMed

Nugiel, Tehila; Alm, Kylie H; Olson, Ingrid R

2016-12-01

In everyday conversation, we make many rapid choices between competing concepts and words in order to convey our intent. This process is termed semantic control, and it is thought to rely on information transmission between a distributed semantic store in the temporal lobes and a more discrete region, optimized for retrieval and selection, in the left inferior frontal gyrus. Here, we used diffusion tensor imaging in a group of neurologically normal young adults to investigate the relationship between semantic control and white matter tracts that have been implicated in semantic memory retrieval. Participants completed a verb generation task that taps semantic control (Snyder & Munakata, 2008; Snyder et al., 2010) and underwent a diffusion imaging scan. Deterministic tractography was performed to compute indices representing the microstructural properties of the inferior fronto-occipital fasciculus (IFOF), the uncinate fasciculus (UF), and the inferior longitudinal fasciculus (ILF). Microstructural measures of the UF failed to predict semantic control performance. However, there was a significant relationship between microstructure of the left IFOF and ILF and individual differences in semantic control. Our findings support the view put forth by Duffau (2013) that the IFOF is a key structural pathway in semantic retrieval.

Picture grammars in classification and semantic interpretation of 3D coronary vessels visualisations

NASA Astrophysics Data System (ADS)

Ogiela, M. R.; Tadeusiewicz, R.; Trzupek, M.

2009-09-01

The work presents the new opportunity for making semantic descriptions and analysis of medical structures, especially coronary vessels CT spatial reconstructions, with the use of AI graph-based linguistic formalisms. In the paper there will be discussed the manners of applying methods of computational intelligence to the development of a syntactic semantic description of spatial visualisations of the heart's coronary vessels. Such descriptions may be used for both smart ordering of images while archiving them and for their semantic searches in medical multimedia databases. Presented methodology of analysis can furthermore be used for attaining other goals related performance of computer-assisted semantic interpretation of selected elements and/or the entire 3D structure of the coronary vascular tree. These goals are achieved through the use of graph-based image formalisms based on IE graphs generating grammars that allow discovering and automatic semantic interpretation of irregularities visualised on the images obtained during diagnostic examinations of the heart muscle. The basis for the construction of 3D reconstructions of biological objects used in this work are visualisations obtained from helical CT scans, yet the method itself may be applied also for other methods of medical 3D images acquisition. The obtained semantic information makes it possible to make a description of the structure focused on the semantics of various morphological forms of the visualised vessels from the point of view of the operation of coronary circulation and the blood supply of the heart muscle. Thanks to these, the analysis conducted allows fast and — to a great degree — automated interpretation of the semantics of various morphological changes in the coronary vascular tree, and especially makes it possible to detect these stenoses in the lumen of the vessels that can cause critical decrease in blood supply to extensive or especially important fragments of the heart muscle.

Latent semantic analysis.

PubMed

Evangelopoulos, Nicholas E

2013-11-01

This article reviews latent semantic analysis (LSA), a theory of meaning as well as a method for extracting that meaning from passages of text, based on statistical computations over a collection of documents. LSA as a theory of meaning defines a latent semantic space where documents and individual words are represented as vectors. LSA as a computational technique uses linear algebra to extract dimensions that represent that space. This representation enables the computation of similarity among terms and documents, categorization of terms and documents, and summarization of large collections of documents using automated procedures that mimic the way humans perform similar cognitive tasks. We present some technical details, various illustrative examples, and discuss a number of applications from linguistics, psychology, cognitive science, education, information science, and analysis of textual data in general. WIREs Cogn Sci 2013, 4:683-692. doi: 10.1002/wcs.1254 CONFLICT OF INTEREST: The author has declared no conflicts of interest for this article. For further resources related to this article, please visit the WIREs website. © 2013 John Wiley & Sons, Ltd.

The influence of multiple trials and computer-mediated communication on collaborative and individual semantic recall.

PubMed

Hinds, Joanne M; Payne, Stephen J

2018-04-01

Collaborative inhibition is a phenomenon where collaborating groups experience a decrement in recall when interacting with others. Despite this, collaboration has been found to improve subsequent individual recall. We explore these effects in semantic recall, which is seldom studied in collaborative retrieval. We also examine "parallel CMC", a synchronous form of computer-mediated communication that has previously been found to improve collaborative recall [Hinds, J. M., & Payne, S. J. (2016). Collaborative inhibition and semantic recall: Improving collaboration through computer-mediated communication. Applied Cognitive Psychology, 30(4), 554-565]. Sixty three triads completed a semantic recall task, which involved generating words beginning with "PO" or "HE" across three recall trials, in one of three retrieval conditions: Individual-Individual-Individual (III), Face-to-face-Face-to-Face-Individual (FFI) and Parallel-Parallel-Individual (PPI). Collaborative inhibition was present across both collaborative conditions. Individual recall in Recall 3 was higher when participants had previously collaborated in comparison to recalling three times individually. There was no difference between face-to-face and parallel CMC recall, however subsidiary analyses of instance repetitions and subjective organisation highlighted differences in group members' approaches to recall in terms of organisation and attention to others' contributions. We discuss the implications of these findings in relation to retrieval strategy disruption.

Development of Category-based Induction and Semantic Knowledge

ERIC Educational Resources Information Center

Fisher, Anna V.; Godwin, Karrie E.; Matlen, Bryan J.; Unger, Layla

2015-01-01

Category-based induction is a hallmark of mature cognition; however, little is known about its origins. This study evaluated the hypothesis that category-based induction is related to semantic development. Computational studies suggest that early on there is little differentiation among concepts, but learning and development lead to increased…

Towards Text Copyright Detection Using Metadata in Web Applications

ERIC Educational Resources Information Center

Poulos, Marios; Korfiatis, Nikolaos; Bokos, George

2011-01-01

Purpose: This paper aims to present the semantic content identifier (SCI), a permanent identifier, computed through a linear-time onion-peeling algorithm that enables the extraction of semantic features from a text, and the integration of this information within the permanent identifier. Design/methodology/approach: The authors employ SCI to…

Computer Programs for the Semantic Differential: Further Modifications.

ERIC Educational Resources Information Center

Lawson, Edwin D.; And Others

The original nine programs for semantic differential analysis have been condensed into three programs which have been further refined and augmented. They yield: (1) means, standard deviations, and standard errors for each subscale on each concept; (2) Evaluation, Potency, and Activity (EPA) means, standard deviations, and standard errors; (3)…

Parametric Effects of Syntactic-Semantic Conflict in Broca's Area during Sentence Processing

ERIC Educational Resources Information Center

Thothathiri, Malathi; Kim, Albert; Trueswell, John C.; Thompson-Schill, Sharon L.

2012-01-01

The hypothesized role of Broca's area in sentence processing ranges from domain-general executive function to domain-specific computation that is specific to certain syntactic structures. We examined this issue by manipulating syntactic structure and conflict between syntactic and semantic cues in a sentence processing task. Functional…

The semantics of Chemical Markup Language (CML) for computational chemistry : CompChem.

PubMed

Phadungsukanan, Weerapong; Kraft, Markus; Townsend, Joe A; Murray-Rust, Peter

2012-08-07

: This paper introduces a subdomain chemistry format for storing computational chemistry data called CompChem. It has been developed based on the design, concepts and methodologies of Chemical Markup Language (CML) by adding computational chemistry semantics on top of the CML Schema. The format allows a wide range of ab initio quantum chemistry calculations of individual molecules to be stored. These calculations include, for example, single point energy calculation, molecular geometry optimization, and vibrational frequency analysis. The paper also describes the supporting infrastructure, such as processing software, dictionaries, validation tools and database repositories. In addition, some of the challenges and difficulties in developing common computational chemistry dictionaries are discussed. The uses of CompChem are illustrated by two practical applications.

The semantics of Chemical Markup Language (CML) for computational chemistry : CompChem

PubMed Central

2012-01-01

This paper introduces a subdomain chemistry format for storing computational chemistry data called CompChem. It has been developed based on the design, concepts and methodologies of Chemical Markup Language (CML) by adding computational chemistry semantics on top of the CML Schema. The format allows a wide range of ab initio quantum chemistry calculations of individual molecules to be stored. These calculations include, for example, single point energy calculation, molecular geometry optimization, and vibrational frequency analysis. The paper also describes the supporting infrastructure, such as processing software, dictionaries, validation tools and database repositories. In addition, some of the challenges and difficulties in developing common computational chemistry dictionaries are discussed. The uses of CompChem are illustrated by two practical applications. PMID:22870956

Rule-based support system for multiple UMLS semantic type assignments

PubMed Central

Geller, James; He, Zhe; Perl, Yehoshua; Morrey, C. Paul; Xu, Julia

2012-01-01

Background When new concepts are inserted into the UMLS, they are assigned one or several semantic types from the UMLS Semantic Network by the UMLS editors. However, not every combination of semantic types is permissible. It was observed that many concepts with rare combinations of semantic types have erroneous semantic type assignments or prohibited combinations of semantic types. The correction of such errors is resource-intensive. Objective We design a computational system to inform UMLS editors as to whether a specific combination of two, three, four, or five semantic types is permissible or prohibited or questionable. Methods We identify a set of inclusion and exclusion instructions in the UMLS Semantic Network documentation and derive corresponding rule-categories as well as rule-categories from the UMLS concept content. We then design an algorithm adviseEditor based on these rule-categories. The algorithm specifies rules for an editor how to proceed when considering a tuple (pair, triple, quadruple, quintuple) of semantic types to be assigned to a concept. Results Eight rule-categories were identified. A Web-based system was developed to implement the adviseEditor algorithm, which returns for an input combination of semantic types whether it is permitted, prohibited or (in a few cases) requires more research. The numbers of semantic type pairs assigned to each rule-category are reported. Interesting examples for each rule-category are illustrated. Cases of semantic type assignments that contradict rules are listed, including recently introduced ones. Conclusion The adviseEditor system implements explicit and implicit knowledge available in the UMLS in a system that informs UMLS editors about the permissibility of a desired combination of semantic types. Using adviseEditor might help accelerate the work of the UMLS editors and prevent erroneous semantic type assignments. PMID:23041716

Accelerating Cancer Systems Biology Research through Semantic Web Technology

PubMed Central

Wang, Zhihui; Sagotsky, Jonathan; Taylor, Thomas; Shironoshita, Patrick; Deisboeck, Thomas S.

2012-01-01

Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute’s caBIG®, so users can not only interact with the DMR through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers’ intellectual property. PMID:23188758

Accelerating cancer systems biology research through Semantic Web technology.

PubMed

Wang, Zhihui; Sagotsky, Jonathan; Taylor, Thomas; Shironoshita, Patrick; Deisboeck, Thomas S

2013-01-01

Cancer systems biology is an interdisciplinary, rapidly expanding research field in which collaborations are a critical means to advance the field. Yet the prevalent database technologies often isolate data rather than making it easily accessible. The Semantic Web has the potential to help facilitate web-based collaborative cancer research by presenting data in a manner that is self-descriptive, human and machine readable, and easily sharable. We have created a semantically linked online Digital Model Repository (DMR) for storing, managing, executing, annotating, and sharing computational cancer models. Within the DMR, distributed, multidisciplinary, and inter-organizational teams can collaborate on projects, without forfeiting intellectual property. This is achieved by the introduction of a new stakeholder to the collaboration workflow, the institutional licensing officer, part of the Technology Transfer Office. Furthermore, the DMR has achieved silver level compatibility with the National Cancer Institute's caBIG, so users can interact with the DMR not only through a web browser but also through a semantically annotated and secure web service. We also discuss the technology behind the DMR leveraging the Semantic Web, ontologies, and grid computing to provide secure inter-institutional collaboration on cancer modeling projects, online grid-based execution of shared models, and the collaboration workflow protecting researchers' intellectual property. Copyright © 2012 Wiley Periodicals, Inc.

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language.

PubMed

de Jong, Wibe A; Walker, Andrew M; Hanwell, Marcus D

2013-05-24

Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper an end-to-end use of semantically rich data in computational chemistry is demonstrated utilizing the Chemical Markup Language (CML) framework. Semantically rich data is generated by the NWChem computational chemistry software with the FoX library and utilized by the Avogadro molecular editor for analysis and visualization. The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files and molecular orbitals used by the computational chemistry software. Draft dictionary entries and a format for molecular orbitals within CML CompChem were developed. The Avogadro application was extended to read in CML data, and display molecular geometry and electronic structure in the GUI allowing for an end-to-end solution where Avogadro can create input structures, generate input files, NWChem can run the calculation and Avogadro can then read in and analyse the CML output produced. The developments outlined in this paper will be made available in future releases of NWChem, FoX, and Avogadro. The production of CML compliant XML files for computational chemistry software such as NWChem can be accomplished relatively easily using the FoX library. The CML data can be read in by a newly developed reader in Avogadro and analysed or visualized in various ways. A community-based effort is needed to further develop the CML CompChem convention and dictionary. This will enable the long-term goal of allowing a researcher to run simple "Google-style" searches of chemistry and physics and have the results of computational calculations returned in a comprehensible form alongside articles from the published literature.

From data to analysis: linking NWChem and Avogadro with the syntax and semantics of Chemical Markup Language

PubMed Central

2013-01-01

Background Multidisciplinary integrated research requires the ability to couple the diverse sets of data obtained from a range of complex experiments and computer simulations. Integrating data requires semantically rich information. In this paper an end-to-end use of semantically rich data in computational chemistry is demonstrated utilizing the Chemical Markup Language (CML) framework. Semantically rich data is generated by the NWChem computational chemistry software with the FoX library and utilized by the Avogadro molecular editor for analysis and visualization. Results The NWChem computational chemistry software has been modified and coupled to the FoX library to write CML compliant XML data files. The FoX library was expanded to represent the lexical input files and molecular orbitals used by the computational chemistry software. Draft dictionary entries and a format for molecular orbitals within CML CompChem were developed. The Avogadro application was extended to read in CML data, and display molecular geometry and electronic structure in the GUI allowing for an end-to-end solution where Avogadro can create input structures, generate input files, NWChem can run the calculation and Avogadro can then read in and analyse the CML output produced. The developments outlined in this paper will be made available in future releases of NWChem, FoX, and Avogadro. Conclusions The production of CML compliant XML files for computational chemistry software such as NWChem can be accomplished relatively easily using the FoX library. The CML data can be read in by a newly developed reader in Avogadro and analysed or visualized in various ways. A community-based effort is needed to further develop the CML CompChem convention and dictionary. This will enable the long-term goal of allowing a researcher to run simple “Google-style” searches of chemistry and physics and have the results of computational calculations returned in a comprehensible form alongside articles from the published literature. PMID:23705910

An Interactive Multimedia Learning Environment for VLSI Built with COSMOS

ERIC Educational Resources Information Center

Angelides, Marios C.; Agius, Harry W.

2002-01-01

This paper presents Bigger Bits, an interactive multimedia learning environment that teaches students about VLSI within the context of computer electronics. The system was built with COSMOS (Content Oriented semantic Modelling Overlay Scheme), which is a modelling scheme that we developed for enabling the semantic content of multimedia to be used…

A Schema Theory Account of Some Cognitive Processes in Complex Learning. Technical Report No. 81.

ERIC Educational Resources Information Center

Munro, Allen; Rigney, Joseph W.

Procedural semantics models have diminished the distinction between data structures and procedures in computer simulations of human intelligence. This development has theoretical consequences for models of cognition. One type of procedural semantics model, called schema theory, is presented, and a variety of cognitive processes are explained in…

Algorithmic Procedure for Finding Semantically Related Journals.

ERIC Educational Resources Information Center

Pudovkin, Alexander I.; Garfield, Eugene

2002-01-01

Using citations, papers and references as parameters a relatedness factor (RF) is computed for a series of journals. Sorting these journals by the RF produces a list of journals most closely related to a specified starting journal. The method appears to select a set of journals that are semantically most similar to the target journal. The…

Word maturity indices with latent semantic analysis: why, when, and where is Procrustes rotation applied?

PubMed

Jorge-Botana, Guillermo; Olmos, Ricardo; Luzón, José M

2018-01-01

The aim of this paper is to describe and explain one useful computational methodology to model the semantic development of word representation: Word maturity. In particular, the methodology is based on the longitudinal word monitoring created by Kirylev and Landauer using latent semantic analysis for the representation of lexical units. The paper is divided into two parts. First, the steps required to model the development of the meaning of words are explained in detail. We describe the technical and theoretical aspects of each step. Second, we provide a simple example of application of this methodology with some simple tools that can be used by applied researchers. This paper can serve as a user-friendly guide for researchers interested in modeling changes in the semantic representations of words. Some current aspects of the technique and future directions are also discussed. WIREs Cogn Sci 2018, 9:e1457. doi: 10.1002/wcs.1457 This article is categorized under: Computer Science > Natural Language Processing Linguistics > Language Acquisition Psychology > Development and Aging. © 2017 Wiley Periodicals, Inc.

Visual analytics for semantic queries of TerraSAR-X image content

NASA Astrophysics Data System (ADS)

Espinoza-Molina, Daniela; Alonso, Kevin; Datcu, Mihai

2015-10-01

With the continuous image product acquisition of satellite missions, the size of the image archives is considerably increasing every day as well as the variety and complexity of their content, surpassing the end-user capacity to analyse and exploit them. Advances in the image retrieval field have contributed to the development of tools for interactive exploration and extraction of the images from huge archives using different parameters like metadata, key-words, and basic image descriptors. Even though we count on more powerful tools for automated image retrieval and data analysis, we still face the problem of understanding and analyzing the results. Thus, a systematic computational analysis of these results is required in order to provide to the end-user a summary of the archive content in comprehensible terms. In this context, visual analytics combines automated analysis with interactive visualizations analysis techniques for an effective understanding, reasoning and decision making on the basis of very large and complex datasets. Moreover, currently several researches are focused on associating the content of the images with semantic definitions for describing the data in a format to be easily understood by the end-user. In this paper, we present our approach for computing visual analytics and semantically querying the TerraSAR-X archive. Our approach is mainly composed of four steps: 1) the generation of a data model that explains the information contained in a TerraSAR-X product. The model is formed by primitive descriptors and metadata entries, 2) the storage of this model in a database system, 3) the semantic definition of the image content based on machine learning algorithms and relevance feedback, and 4) querying the image archive using semantic descriptors as query parameters and computing the statistical analysis of the query results. The experimental results shows that with the help of visual analytics and semantic definitions we are able to explain the image content using semantic terms and the relations between them answering questions such as what is the percentage of urban area in a region? or what is the distribution of water bodies in a city?

UBioLab: a web-laboratory for ubiquitous in-silico experiments.

PubMed

Bartocci, Ezio; Cacciagrano, Diletta; Di Berardini, Maria Rita; Merelli, Emanuela; Vito, Leonardo

2012-07-09

The huge and dynamic amount of bioinformatic resources (e.g., data and tools) available nowadays in Internet represents a big challenge for biologists –for what concerns their management and visualization– and for bioinformaticians –for what concerns the possibility of rapidly creating and executing in-silico experiments involving resources and activities spread over the WWW hyperspace. Any framework aiming at integrating such resources as in a physical laboratory has imperatively to tackle –and possibly to handle in a transparent and uniform way– aspects concerning physical distribution, semantic heterogeneity, co-existence of different computational paradigms and, as a consequence, of different invocation interfaces (i.e., OGSA for Grid nodes, SOAP for Web Services, Java RMI for Java objects, etc.). The framework UBioLab has been just designed and developed as a prototype following the above objective. Several architectural features –as those ones of being fully Web-based and of combining domain ontologies, Semantic Web and workflow techniques– give evidence of an effort in such a direction. The integration of a semantic knowledge management system for distributed (bioinformatic) resources, a semantic-driven graphic environment for defining and monitoring ubiquitous workflows and an intelligent agent-based technology for their distributed execution allows UBioLab to be a semantic guide for bioinformaticians and biologists providing (i) a flexible environment for visualizing, organizing and inferring any (semantics and computational) "type" of domain knowledge (e.g., resources and activities, expressed in a declarative form), (ii) a powerful engine for defining and storing semantic-driven ubiquitous in-silico experiments on the domain hyperspace, as well as (iii) a transparent, automatic and distributed environment for correct experiment executions.

The role of attention in subliminal semantic processing: A mouse tracking study

PubMed Central

Xiao, Kunchen; Yamauchi, Takashi

2017-01-01

Recent evidence suggests that top-down attention facilitates unconscious semantic processing. To clarify the role of attention in unconscious semantic processing, we traced trajectories of the computer mouse in a semantic priming task and scrutinized the extent to which top-down attention enhances unconscious semantic processing in four different stimulus-onset asynchrony (SOA: 50, 200, 500, or 1000ms) conditions. Participants judged whether a target digit (e.g., “6”) was larger or smaller than five, preceded by a masked priming digit (e.g., “9”). The pre-prime duration changed randomly from trial to trial to disrupt participants’ top-down attention in an uncued condition (in a cued condition, a green square cue was presented to facilitate participants’ top-down attention). The results show that top-down attention modifies the time course of subliminal semantic processing, and the temporal attention window lasts more than 1000ms; attention facilitated by the cue may amplify semantic priming to some extent, yet the amplification effect of attention is relatively minor. PMID:28609460

Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD.

PubMed

Bullinaria, John A; Levy, Joseph P

2012-09-01

In a previous article, we presented a systematic computational study of the extraction of semantic representations from the word-word co-occurrence statistics of large text corpora. The conclusion was that semantic vectors of pointwise mutual information values from very small co-occurrence windows, together with a cosine distance measure, consistently resulted in the best representations across a range of psychologically relevant semantic tasks. This article extends that study by investigating the use of three further factors--namely, the application of stop-lists, word stemming, and dimensionality reduction using singular value decomposition (SVD)--that have been used to provide improved performance elsewhere. It also introduces an additional semantic task and explores the advantages of using a much larger corpus. This leads to the discovery and analysis of improved SVD-based methods for generating semantic representations (that provide new state-of-the-art performance on a standard TOEFL task) and the identification and discussion of problems and misleading results that can arise without a full systematic study.

Computerized Analysis of Verbal Fluency: Normative Data and the Effects of Repeated Testing, Simulated Malingering, and Traumatic Brain Injury

PubMed Central

Wyma, John M.; Herron, Timothy J.; Yund, E. William

2016-01-01

In verbal fluency (VF) tests, subjects articulate words in a specified category during a short test period (typically 60 s). Verbal fluency tests are widely used to study language development and to evaluate memory retrieval in neuropsychiatric disorders. Performance is usually measured as the total number of correct words retrieved. Here, we describe the properties of a computerized VF (C-VF) test that tallies correct words and repetitions while providing additional lexical measures of word frequency, syllable count, and typicality. In addition, the C-VF permits (1) the analysis of the rate of responding over time, and (2) the analysis of the semantic relationships between words using a new method, Explicit Semantic Analysis (ESA), as well as the established semantic clustering and switching measures developed by Troyer et al. (1997). In Experiment 1, we gathered normative data from 180 subjects ranging in age from 18 to 82 years in semantic (“animals”) and phonemic (letter “F”) conditions. The number of words retrieved in 90 s correlated with education and daily hours of computer-use. The rate of word production declined sharply over time during both tests. In semantic conditions, correct-word scores correlated strongly with the number of ESA and Troyer-defined semantic switches as well as with an ESA-defined semantic organization index (SOI). In phonemic conditions, ESA revealed significant semantic influences in the sequence of words retrieved. In Experiment 2, we examined the test-retest reliability of different measures across three weekly tests in 40 young subjects. Different categories were used for each semantic (“animals”, “parts of the body”, and “foods”) and phonemic (letters “F”, “A”, and “S”) condition. After regressing out the influences of education and computer-use, we found that correct-word z-scores in the first session did not differ from those of the subjects in Experiment 1. Word production was uniformly greater in semantic than phonemic conditions. Intraclass correlation coefficients (ICCs) of correct-word z-scores were higher for phonemic (0.91) than semantic (0.77) tests. In semantic conditions, good reliability was also seen for the SOI (ICC = 0.68) and ESA-defined switches in semantic categories (ICC = 0.62). In Experiment 3, we examined the performance of subjects from Experiment 2 when instructed to malinger: 38% showed abnormal (p< 0.05) performance in semantic conditions. Simulated malingerers with abnormal scores could be distinguished with 80% sensitivity and 89% specificity from subjects with abnormal scores in Experiment 1 using lexical, temporal, and semantic measures. In Experiment 4, we tested patients with mild and severe traumatic brain injury (mTBI and sTBI). Patients with mTBI performed within the normal range, while patients with sTBI showed significant impairments in correct-word z-scores and category shifts. The lexical, temporal, and semantic measures of the C-VF provide an automated and comprehensive description of verbal fluency performance. PMID:27936001

Facilitation and interference in naming: A consequence of the same learning process?

PubMed

Hughes, Julie W; Schnur, Tatiana T

2017-08-01

Our success with naming depends on what we have named previously, a phenomenon thought to reflect learning processes. Repeatedly producing the same name facilitates language production (i.e., repetition priming), whereas producing semantically related names hinders subsequent performance (i.e., semantic interference). Semantic interference is found whether naming categorically related items once (continuous naming) or multiple times (blocked cyclic naming). A computational model suggests that the same learning mechanism responsible for facilitation in repetition creates semantic interference in categorical naming (Oppenheim, Dell, & Schwartz, 2010). Accordingly, we tested the predictions that variability in semantic interference is correlated across categorical naming tasks and is caused by learning, as measured by two repetition priming tasks (picture-picture repetition priming, Exp. 1; definition-picture repetition priming, Exp. 2, e.g., Wheeldon & Monsell, 1992). In Experiment 1 (77 subjects) semantic interference and repetition priming effects were robust, but the results revealed no relationship between semantic interference effects across contexts. Critically, learning (picture-picture repetition priming) did not predict semantic interference effects in either task. We replicated these results in Experiment 2 (81 subjects), finding no relationship between semantic interference effects across tasks or between semantic interference effects and learning (definition-picture repetition priming). We conclude that the changes underlying facilitatory and interfering effects inherent to lexical access are the result of distinct learning processes where multiple mechanisms contribute to semantic interference in naming. Copyright © 2017 Elsevier B.V. All rights reserved.

Semantic memory is impaired in patients with unilateral anterior temporal lobe resection for temporal lobe epilepsy.

PubMed

Lambon Ralph, Matthew A; Ehsan, Sheeba; Baker, Gus A; Rogers, Timothy T

2012-01-01

Contemporary clinical and basic neuroscience studies have increasingly implicated the anterior temporal lobe regions, bilaterally, in the formation of coherent concepts. Mounting convergent evidence for the importance of the anterior temporal lobe in semantic memory is found in patients with bilateral anterior temporal lobe damage (e.g. semantic dementia), functional neuroimaging and repetitive transcranial magnetic stimulation studies. If this proposal is correct, then one might expect patients with anterior temporal lobe resection for long-standing temporal lobe epilepsy to be semantically impaired. Such patients, however, do not present clinically with striking comprehension deficits but with amnesia and variable anomia, leading some to conclude that semantic memory is intact in resection for temporal lobe epilepsy and thus casting doubt over the conclusions drawn from semantic dementia and linked basic neuroscience studies. Whilst there is a considerable neuropsychological literature on temporal lobe epilepsy, few studies have probed semantic memory directly, with mixed results, and none have undertaken the same type of systematic investigation of semantic processing that has been conducted with other patient groups. In this study, therefore, we investigated the semantic performance of 20 patients with resection for chronic temporal lobe epilepsy with a full battery of semantic assessments, including more sensitive measures of semantic processing. The results provide a bridge between the current clinical observations about resection for temporal lobe epilepsy and the expectations from semantic dementia and other neuroscience findings. Specifically, we found that on simple semantic tasks, the patients' accuracy fell in the normal range, with the exception that some patients with left resection for temporal lobe epilepsy had measurable anomia. Once the semantic assessments were made more challenging, by probing specific-level concepts, lower frequency/more abstract items or measuring reaction times on semantic tasks versus those on difficulty-matched non-semantic assessments, evidence of a semantic impairment was found in all individuals. We conclude by describing a unified, computationally inspired framework for capturing the variable degrees of semantic impairment found across different patient groups (semantic dementia, temporal lobe epilepsy, glioma and stroke) as well as semantic processing in neurologically intact participants.

Semantic memory is impaired in patients with unilateral anterior temporal lobe resection for temporal lobe epilepsy

PubMed Central

Ehsan, Sheeba; Baker, Gus A.; Rogers, Timothy T.

2012-01-01

Contemporary clinical and basic neuroscience studies have increasingly implicated the anterior temporal lobe regions, bilaterally, in the formation of coherent concepts. Mounting convergent evidence for the importance of the anterior temporal lobe in semantic memory is found in patients with bilateral anterior temporal lobe damage (e.g. semantic dementia), functional neuroimaging and repetitive transcranial magnetic stimulation studies. If this proposal is correct, then one might expect patients with anterior temporal lobe resection for long-standing temporal lobe epilepsy to be semantically impaired. Such patients, however, do not present clinically with striking comprehension deficits but with amnesia and variable anomia, leading some to conclude that semantic memory is intact in resection for temporal lobe epilepsy and thus casting doubt over the conclusions drawn from semantic dementia and linked basic neuroscience studies. Whilst there is a considerable neuropsychological literature on temporal lobe epilepsy, few studies have probed semantic memory directly, with mixed results, and none have undertaken the same type of systematic investigation of semantic processing that has been conducted with other patient groups. In this study, therefore, we investigated the semantic performance of 20 patients with resection for chronic temporal lobe epilepsy with a full battery of semantic assessments, including more sensitive measures of semantic processing. The results provide a bridge between the current clinical observations about resection for temporal lobe epilepsy and the expectations from semantic dementia and other neuroscience findings. Specifically, we found that on simple semantic tasks, the patients’ accuracy fell in the normal range, with the exception that some patients with left resection for temporal lobe epilepsy had measurable anomia. Once the semantic assessments were made more challenging, by probing specific-level concepts, lower frequency/more abstract items or measuring reaction times on semantic tasks versus those on difficulty-matched non-semantic assessments, evidence of a semantic impairment was found in all individuals. We conclude by describing a unified, computationally inspired framework for capturing the variable degrees of semantic impairment found across different patient groups (semantic dementia, temporal lobe epilepsy, glioma and stroke) as well as semantic processing in neurologically intact participants. PMID:22287382

When fruits lose to animals: Disorganized search of semantic memory in Parkinson's disease.

PubMed

Tagini, Sofia; Seyed-Allaei, Shima; Scarpina, Federica; Toraldo, Alessio; Mauro, Alessandro; Cherubini, Paolo; Reverberi, Carlo

2018-04-16

The semantic fluency task is widely used in both clinical and research settings to assess both the integrity of the semantic store and the effectiveness of the search through it. Our aim was to investigate whether nondemented Parkinson's disease (PD) patients show an impairment in the strategic exploration of the semantic store and whether the tested semantic category has an impact on multiple measures of performance. We compared 74 nondemented PD patients with 254 healthy subjects in a semantic fluency test using relatively small (fruits) and large (animals) semantic categories. Number of words produced, number of explored semantic subcategories, and degree of order in the produced sequences were computed as dependent variables. PD patients produced fewer words than healthy subjects did, regardless of the category. Number of subcategories was also lower in PD patients than in healthy subjects, without a significant difference between categories. Critically, PD patients' sequences were less semantically organized than were those of controls, but this effect appeared in only the smaller category (fruits), thus pointing to a lack of strategy in exploring the semantic store. Our results show that the semantic fluency deficit in PD patients has a strategic component, even though that may not be the only cause of the impaired performance. Furthermore, our evidence suggests that the semantic category used in the test influences performance, hence providing an explanation for the failure by previous studies, which often used large categories such as animals, to detect strategy deficits in PD. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

A Model for New Linkages for Prior Learning Assessment

ERIC Educational Resources Information Center

Kalz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob

2008-01-01

Purpose: The purpose of this paper is twofold: first the paper aims to sketch the theoretical basis for the use of electronic portfolios for prior learning assessment; second it endeavours to introduce latent semantic analysis (LSA) as a powerful method for the computation of semantic similarity between texts and a basis for a new observation link…

How Colours Are Semantically Construed in the Arabic and English Culture: A Comparative Study

ERIC Educational Resources Information Center

Hasan, Amna A.; Al-Sammerai, Nabiha S. Mehdi; Kadir, Fakhrul Adabi Bin Abdul

2011-01-01

Most works in cognitive semantics have been focusing on the manner, in which an individual behaves--be it the mind, brain, or even computers, which process various kinds of information. Among humans, in particular, social life is richly cultured. Sociality and culture are made possible by cognitive studies; they provide specific inputs to…

Focused Belief Measures for Uncertainty Quantification in High Performance Semantic Analysis

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

Joslyn, Cliff A.; Weaver, Jesse R.

In web-scale semantic data analytics there is a great need for methods which aggregate uncertainty claims, on the one hand respecting the information provided as accurately as possible, while on the other still being tractable. Traditional statistical methods are more robust, but only represent distributional, additive uncertainty. Generalized information theory methods, including fuzzy systems and Dempster-Shafer (DS) evidence theory, represent multiple forms of uncertainty, but are computationally and methodologically difficult. We require methods which provide an effective balance between the complete representation of the full complexity of uncertainty claims in their interaction, while satisfying the needs of both computational complexitymore » and human cognition. Here we build on J{\\o}sang's subjective logic to posit methods in focused belief measures (FBMs), where a full DS structure is focused to a single event. The resulting ternary logical structure is posited to be able to capture the minimal amount of generalized complexity needed at a maximum of computational efficiency. We demonstrate the efficacy of this approach in a web ingest experiment over the 2012 Billion Triple dataset from the Semantic Web Challenge.« less

Self-Organized Service Negotiation for Collaborative Decision Making

PubMed Central

Zhang, Bo; Zheng, Ziming

2014-01-01

This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM. PMID:25243228

Self-organized service negotiation for collaborative decision making.

PubMed

Zhang, Bo; Huang, Zhenhua; Zheng, Ziming

2014-01-01

This paper proposes a self-organized service negotiation method for CDM in intelligent and automatic manners. It mainly includes three phases: semantic-based capacity evaluation for the CDM sponsor, trust computation of the CDM organization, and negotiation selection of the decision-making service provider (DMSP). In the first phase, the CDM sponsor produces the formal semantic description of the complex decision task for DMSP and computes the capacity evaluation values according to participator instructions from different DMSPs. In the second phase, a novel trust computation approach is presented to compute the subjective belief value, the objective reputation value, and the recommended trust value. And in the third phase, based on the capacity evaluation and trust computation, a negotiation mechanism is given to efficiently implement the service selection. The simulation experiment results show that our self-organized service negotiation method is feasible and effective for CDM.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

Concepts, Control, and Context: A Connectionist Account of Normal and Disordered Semantic Cognition

PubMed Central

2018-01-01

Semantic cognition requires conceptual representations shaped by verbal and nonverbal experience and executive control processes that regulate activation of knowledge to meet current situational demands. A complete model must also account for the representation of concrete and abstract words, of taxonomic and associative relationships, and for the role of context in shaping meaning. We present the first major attempt to assimilate all of these elements within a unified, implemented computational framework. Our model combines a hub-and-spoke architecture with a buffer that allows its state to be influenced by prior context. This hybrid structure integrates the view, from cognitive neuroscience, that concepts are grounded in sensory-motor representation with the view, from computational linguistics, that knowledge is shaped by patterns of lexical co-occurrence. The model successfully codes knowledge for abstract and concrete words, associative and taxonomic relationships, and the multiple meanings of homonyms, within a single representational space. Knowledge of abstract words is acquired through (a) their patterns of co-occurrence with other words and (b) acquired embodiment, whereby they become indirectly associated with the perceptual features of co-occurring concrete words. The model accounts for executive influences on semantics by including a controlled retrieval mechanism that provides top-down input to amplify weak semantic relationships. The representational and control elements of the model can be damaged independently, and the consequences of such damage closely replicate effects seen in neuropsychological patients with loss of semantic representation versus control processes. Thus, the model provides a wide-ranging and neurally plausible account of normal and impaired semantic cognition. PMID:29733663

Semantic representation in the white matter pathway

PubMed Central

Fang, Yuxing; Wang, Xiaosha; Zhong, Suyu; Song, Luping; Han, Zaizhu; Gong, Gaolang

2018-01-01

Object conceptual processing has been localized to distributed cortical regions that represent specific attributes. A challenging question is how object semantic space is formed. We tested a novel framework of representing semantic space in the pattern of white matter (WM) connections by extending the representational similarity analysis (RSA) to structural lesion pattern and behavioral data in 80 brain-damaged patients. For each WM connection, a neural representational dissimilarity matrix (RDM) was computed by first building machine-learning models with the voxel-wise WM lesion patterns as features to predict naming performance of a particular item and then computing the correlation between the predicted naming score and the actual naming score of another item in the testing patients. This correlation was used to build the neural RDM based on the assumption that if the connection pattern contains certain aspects of information shared by the naming processes of these two items, models trained with one item should also predict naming accuracy of the other. Correlating the neural RDM with various cognitive RDMs revealed that neural patterns in several WM connections that connect left occipital/middle temporal regions and anterior temporal regions associated with the object semantic space. Such associations were not attributable to modality-specific attributes (shape, manipulation, color, and motion), to peripheral picture-naming processes (picture visual similarity, phonological similarity), to broad semantic categories, or to the properties of the cortical regions that they connected, which tended to represent multiple modality-specific attributes. That is, the semantic space could be represented through WM connection patterns across cortical regions representing modality-specific attributes. PMID:29624578

A Computational Unification of Scientific Law:. Spelling out a Universal Semantics for Physical Reality

NASA Astrophysics Data System (ADS)

Marcer, Peter J.; Rowlands, Peter

2013-09-01

The principal criteria Cn (n = 1 to 23) and grammatical production rules are set out of a universal computational rewrite language spelling out a semantic description of an emergent, self-organizing architecture for the cosmos. These language productions already predicate: (1) Einstein's conservation law of energy, momentum and mass and, subsequently, (2) with respect to gauge invariant relativistic space time (both Lorentz special & Einstein general); (3) Standard Model elementary particle physics; (4) the periodic table of the elements & chemical valence; and (5) the molecular biological basis of the DNA / RNA genetic code; so enabling the Cybernetic Machine specialist Groups Mission Statement premise;** (6) that natural semantic language thinking at the higher level of the self-organized emergent chemical molecular complexity of the human brain (only surpassed by that of the cosmos itself!) would be realized (7) by this same universal semantic language via (8) an architecture of a conscious human brain/mind and self which, it predicates consists of its neural / glia and microtubule substrates respectively, so as to endow it with; (9) the intelligent semantic capability to be able to specify, symbolize, spell out and understand the cosmos that conceived it; and (10) provide a quantum physical explanation of consciousness and of how (11) the dichotomy between first person subjectivity and third person objectivity or `hard problem' is resolved.

On the Equivalence of Formal Grammars and Machines.

ERIC Educational Resources Information Center

Lund, Bruce

1991-01-01

Explores concepts of formal language and automata theory underlying computational linguistics. A computational formalism is described known as a "logic grammar," with which computational systems process linguistic data, with examples in declarative and procedural semantics and definite clause grammars. (13 references) (CB)

CHAMPION: Intelligent Hierarchical Reasoning Agents for Enhanced Decision Support

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

Hohimer, Ryan E.; Greitzer, Frank L.; Noonan, Christine F.

2011-11-15

We describe the design and development of an advanced reasoning framework employing semantic technologies, organized within a hierarchy of computational reasoning agents that interpret domain specific information. Designed based on an inspirational metaphor of the pattern recognition functions performed by the human neocortex, the CHAMPION reasoning framework represents a new computational modeling approach that derives invariant knowledge representations through memory-prediction belief propagation processes that are driven by formal ontological language specification and semantic technologies. The CHAMPION framework shows promise for enhancing complex decision making in diverse problem domains including cyber security, nonproliferation and energy consumption analysis.

Computational Modeling of Reading in Semantic Dementia: Comment on Woollams, Lambon Ralph, Plaut, and Patterson (2007)

ERIC Educational Resources Information Center

Coltheart, Max; Tree, Jeremy J.; Saunders, Steven J.

2010-01-01

Woollams, Lambon Ralph, Plaut, and Patterson (see record 2007-05396-004) reported detailed data on reading in 51 cases of semantic dementia. They simulated some aspects of these data using a connectionist parallel distributed processing (PDP) triangle model of reading. We argue here that a different model of reading, the dual route cascaded (DRC)…

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

PubMed

Couto, Francisco M; Pinto, H Sofia

2013-10-01

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

The MMI Semantic Framework: Rosetta Stones for Earth Sciences

NASA Astrophysics Data System (ADS)

Rueda, C.; Bermudez, L. E.; Graybeal, J.; Alexander, P.

2009-12-01

Semantic interoperability—the exchange of meaning among computer systems—is needed to successfully share data in Ocean Science and across all Earth sciences. The best approach toward semantic interoperability requires a designed framework, and operationally tested tools and infrastructure within that framework. Currently available technologies make a scientific semantic framework feasible, but its development requires sustainable architectural vision and development processes. This presentation outlines the MMI Semantic Framework, including recent progress on it and its client applications. The MMI Semantic Framework consists of tools, infrastructure, and operational and community procedures and best practices, to meet short-term and long-term semantic interoperability goals. The design and prioritization of the semantic framework capabilities are based on real-world scenarios in Earth observation systems. We describe some key uses cases, as well as the associated requirements for building the overall infrastructure, which is realized through the MMI Ontology Registry and Repository. This system includes support for community creation and sharing of semantic content, ontology registration, version management, and seamless integration of user-friendly tools and application programming interfaces. The presentation describes the architectural components for semantic mediation, registry and repository for vocabularies, ontology, and term mappings. We show how the technologies and approaches in the framework can address community needs for managing and exchanging semantic information. We will demonstrate how different types of users and client applications exploit the tools and services for data aggregation, visualization, archiving, and integration. Specific examples from OOSTethys (http://www.oostethys.org) and the Ocean Observatories Initiative Cyberinfrastructure (http://www.oceanobservatories.org) will be cited. Finally, we show how semantic augmentation of web services standards could be performed using framework tools.

Constructing a Geology Ontology Using a Relational Database

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Cognitive search model and a new query paradigm

NASA Astrophysics Data System (ADS)

Xu, Zhonghui

2001-06-01

This paper proposes a cognitive model in which people begin to search pictures by using semantic content and find a right picture by judging whether its visual content is a proper visualization of the semantics desired. It is essential that human search is not just a process of matching computation on visual feature but rather a process of visualization of the semantic content known. For people to search electronic images in the way as they manually do in the model, we suggest that querying be a semantic-driven process like design. A query-by-design paradigm is prosed in the sense that what you design is what you find. Unlike query-by-example, query-by-design allows users to specify the semantic content through an iterative and incremental interaction process so that a retrieval can start with association and identification of the given semantic content and get refined while further visual cues are available. An experimental image retrieval system, Kuafu, has been under development using the query-by-design paradigm and an iconic language is adopted.

Decoding semantic information from human electrocorticographic (ECoG) signals.

PubMed

Wang, Wei; Degenhart, Alan D; Sudre, Gustavo P; Pomerleau, Dean A; Tyler-Kabara, Elizabeth C

2011-01-01

This study examined the feasibility of decoding semantic information from human cortical activity. Four human subjects undergoing presurgical brain mapping and seizure foci localization participated in this study. Electrocorticographic (ECoG) signals were recorded while the subjects performed simple language tasks involving semantic information processing, such as a picture naming task where subjects named pictures of objects belonging to different semantic categories. Robust high-gamma band (60-120 Hz) activation was observed at the left inferior frontal gyrus (LIFG) and the posterior portion of the superior temporal gyrus (pSTG) with a temporal sequence corresponding to speech production and perception. Furthermore, Gaussian Naïve Bayes and Support Vector Machine classifiers, two commonly used machine learning algorithms for pattern recognition, were able to predict the semantic category of an object using cortical activity captured by ECoG electrodes covering the frontal, temporal and parietal cortices. These findings have implications for both basic neuroscience research and development of semantic-based brain-computer interface systems (BCI) that can help individuals with severe motor or communication disorders to express their intention and thoughts.

Using RDF to Model the Structure and Process of Systems

NASA Astrophysics Data System (ADS)

Rodriguez, Marko A.; Watkins, Jennifer H.; Bollen, Johan; Gershenson, Carlos

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of 109 edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.

A novel co-occurrence-based approach to predict pure associative and semantic priming.

PubMed

Roelke, Andre; Franke, Nicole; Biemann, Chris; Radach, Ralph; Jacobs, Arthur M; Hofmann, Markus J

2018-03-15

The theoretical "difficulty in separating association strength from [semantic] feature overlap" has resulted in inconsistent findings of either the presence or absence of "pure" associative priming in recent literature (Hutchison, 2003, Psychonomic Bulletin & Review, 10(4), p. 787). The present study used co-occurrence statistics of words in sentences to provide a full factorial manipulation of direct association (strong/no) and the number of common associates (many/no) of the prime and target words. These common associates were proposed to serve as semantic features for a recent interactive activation model of semantic processing (i.e., the associative read-out model; Hofmann & Jacobs, 2014). With stimulus onset asynchrony (SOA) as an additional factor, our findings indicate that associative and semantic priming are indeed dissociable. Moreover, the effect of direct association was strongest at a long SOA (1,000 ms), while many common associates facilitated lexical decisions primarily at a short SOA (200 ms). This response pattern is consistent with previous performance-based accounts and suggests that associative and semantic priming can be evoked by computationally determined direct and common associations.

Computer Processing of Esperanto Text.

ERIC Educational Resources Information Center

Sherwood, Bruce

1981-01-01

Basic aspects of computer processing of Esperanto are considered in relation to orthography and computer representation, phonetics, morphology, one-syllable and multisyllable words, lexicon, semantics, and syntax. There are 28 phonemes in Esperanto, each represented in orthography by a single letter. The PLATO system handles diacritics by using a…

Predicting Visual Semantic Descriptive Terms from Radiological Image Data: Preliminary Results with Liver Lesions in CT

PubMed Central

Depeursinge, Adrien; Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.

2014-01-01

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using high–order steerable Riesz wavelets and support vector machines (SVM). The organization of scales and directions that are specific to every VST are modeled as linear combinations of directional Riesz wavelets. The models obtained are steerable, which means that any orientation of the model can be synthesized from linear combinations of the basis filters. The latter property is leveraged to model VST independently from their local orientation. In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a non–hierarchical computationally–derived ontology of VST containing inter–term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave–one–patient–out cross–validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST when using SVMs in a feature space combining the magnitudes of the steered models with CT intensities. Likelihood maps are created for each VST, which enables high transparency of the information modeled. The computationally–derived ontology obtained from the VST models was found to be consistent with the underlying semantics of the visual terms. It was found to be complementary to the RadLex ontology, and constitutes a potential method to link the image content to visual semantics. The proposed framework is expected to foster human–computer synergies for the interpretation of radiological images while using rotation–covariant computational models of VSTs to (1) quantify their local likelihood and (2) explicitly link them with pixel–based image content in the context of a given imaging domain. PMID:24808406

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

Understanding human activity patterns based on space-time-semantics

NASA Astrophysics Data System (ADS)

Huang, Wei; Li, Songnian

2016-11-01

Understanding human activity patterns plays a key role in various applications in an urban environment, such as transportation planning and traffic forecasting, urban planning, public health and safety, and emergency response. Most existing studies in modeling human activity patterns mainly focus on spatiotemporal dimensions, which lacks consideration of underlying semantic context. In fact, what people do and discuss at some places, inferring what is happening at the places, cannot be simple neglected because it is the root of human mobility patterns. We believe that the geo-tagged semantic context, representing what individuals do and discuss at a place and a specific time, drives a formation of specific human activity pattern. In this paper, we aim to model human activity patterns not only based on space and time but also with consideration of associated semantics, and attempt to prove a hypothesis that similar mobility patterns may have different motivations. We develop a spatiotemporal-semantic model to quantitatively express human activity patterns based on topic models, leading to an analysis of space, time and semantics. A case study is conducted using Twitter data in Toronto based on our model. Through computing the similarities between users in terms of spatiotemporal pattern, semantic pattern and spatiotemporal-semantic pattern, we find that only a small number of users (2.72%) have very similar activity patterns, while the majority (87.14%) show different activity patterns (i.e., similar spatiotemporal patterns and different semantic patterns, similar semantic patterns and different spatiotemporal patterns, or different in both). The population of users that has very similar activity patterns is decreased by 56.41% after incorporating semantic information in the corresponding spatiotemporal patterns, which can quantitatively prove the hypothesis.

Surface errors without semantic impairment in acquired dyslexia: a voxel-based lesion–symptom mapping study

PubMed Central

Pillay, Sara B.; Humphries, Colin J.; Gross, William L.; Graves, William W.; Book, Diane S.

2016-01-01

Patients with surface dyslexia have disproportionate difficulty pronouncing irregularly spelled words (e.g. pint), suggesting impaired use of lexical-semantic information to mediate phonological retrieval. Patients with this deficit also make characteristic ‘regularization’ errors, in which an irregularly spelled word is mispronounced by incorrect application of regular spelling-sound correspondences (e.g. reading plaid as ‘played’), indicating over-reliance on sublexical grapheme–phoneme correspondences. We examined the neuroanatomical correlates of this specific error type in 45 patients with left hemisphere chronic stroke. Voxel-based lesion–symptom mapping showed a strong positive relationship between the rate of regularization errors and damage to the posterior half of the left middle temporal gyrus. Semantic deficits on tests of single-word comprehension were generally mild, and these deficits were not correlated with the rate of regularization errors. Furthermore, the deep occipital-temporal white matter locus associated with these mild semantic deficits was distinct from the lesion site associated with regularization errors. Thus, in contrast to patients with surface dyslexia and semantic impairment from anterior temporal lobe degeneration, surface errors in our patients were not related to a semantic deficit. We propose that these patients have an inability to link intact semantic representations with phonological representations. The data provide novel evidence for a post-semantic mechanism mediating the production of surface errors, and suggest that the posterior middle temporal gyrus may compute an intermediate representation linking semantics with phonology. PMID:26966139

Reading visually embodied meaning from the brain: Visually grounded computational models decode visual-object mental imagery induced by written text.

PubMed

Anderson, Andrew James; Bruni, Elia; Lopopolo, Alessandro; Poesio, Massimo; Baroni, Marco

2015-10-15

Embodiment theory predicts that mental imagery of object words recruits neural circuits involved in object perception. The degree of visual imagery present in routine thought and how it is encoded in the brain is largely unknown. We test whether fMRI activity patterns elicited by participants reading objects' names include embodied visual-object representations, and whether we can decode the representations using novel computational image-based semantic models. We first apply the image models in conjunction with text-based semantic models to test predictions of visual-specificity of semantic representations in different brain regions. Representational similarity analysis confirms that fMRI structure within ventral-temporal and lateral-occipital regions correlates most strongly with the image models and conversely text models correlate better with posterior-parietal/lateral-temporal/inferior-frontal regions. We use an unsupervised decoding algorithm that exploits commonalities in representational similarity structure found within both image model and brain data sets to classify embodied visual representations with high accuracy (8/10) and then extend it to exploit model combinations to robustly decode different brain regions in parallel. By capturing latent visual-semantic structure our models provide a route into analyzing neural representations derived from past perceptual experience rather than stimulus-driven brain activity. Our results also verify the benefit of combining multimodal data to model human-like semantic representations. Copyright © 2015 Elsevier Inc. All rights reserved.

On a categorial aspect of knowledge representation

NASA Astrophysics Data System (ADS)

Tataj, Emanuel; Mulawka, Jan; Nieznański, Edward

Adequate representation of data is crucial for modeling any type of data. To faithfully present and describe the relevant section of the world it is necessary to select the method that can easily be implemented on a computer system which will help in further description allowing reasoning. The main objective of this contribution is to present methods of knowledge representation using categorial approach. Next to identify the main advantages for computer implementation. Categorical aspect of knowledge representation is considered in semantic networks realisation. Such method borrows already known metaphysics properties for data modeling process. The potential topics of further development of categorical semantic networks implementations are also underlined.

CAD system for automatic analysis of CT perfusion maps

NASA Astrophysics Data System (ADS)

Hachaj, T.; Ogiela, M. R.

2011-03-01

In this article, authors present novel algorithms developed for the computer-assisted diagnosis (CAD) system for analysis of dynamic brain perfusion, computer tomography (CT) maps, cerebral blood flow (CBF), and cerebral blood volume (CBV). Those methods perform both quantitative analysis [detection and measurement and description with brain anatomy atlas (AA) of potential asymmetries/lesions] and qualitative analysis (semantic interpretation of visualized symptoms). The semantic interpretation (decision about type of lesion: ischemic/hemorrhagic, is the brain tissue at risk of infraction or not) of visualized symptoms is done by, so-called, cognitive inference processes allowing for reasoning on character of pathological regions based on specialist image knowledge. The whole system is implemented in.NET platform (C# programming language) and can be used on any standard PC computer with.NET framework installed.

Computational approaches for predicting biomedical research collaborations.

PubMed

Zhang, Qing; Yu, Hong

2014-01-01

Biomedical research is increasingly collaborative, and successful collaborations often produce high impact work. Computational approaches can be developed for automatically predicting biomedical research collaborations. Previous works of collaboration prediction mainly explored the topological structures of research collaboration networks, leaving out rich semantic information from the publications themselves. In this paper, we propose supervised machine learning approaches to predict research collaborations in the biomedical field. We explored both the semantic features extracted from author research interest profile and the author network topological features. We found that the most informative semantic features for author collaborations are related to research interest, including similarity of out-citing citations, similarity of abstracts. Of the four supervised machine learning models (naïve Bayes, naïve Bayes multinomial, SVMs, and logistic regression), the best performing model is logistic regression with an ROC ranging from 0.766 to 0.980 on different datasets. To our knowledge we are the first to study in depth how research interest and productivities can be used for collaboration prediction. Our approach is computationally efficient, scalable and yet simple to implement. The datasets of this study are available at https://github.com/qingzhanggithub/medline-collaboration-datasets.

Computer-Based Mapping for Curriculum Development.

ERIC Educational Resources Information Center

Allen, Brockenbrough S.; And Others

This article describes the results of a three-month experiment in the use of computer-based semantic networks for curriculum development. A team of doctoral and master's degree students developed a 1200-item computer database representing a tentative "domain of competency" for a proposed MA degree in Workforce Education and Lifelong…

Semantic querying of relational data for clinical intelligence: a semantic web services-based approach

PubMed Central

2013-01-01

Background Clinical Intelligence, as a research and engineering discipline, is dedicated to the development of tools for data analysis for the purposes of clinical research, surveillance, and effective health care management. Self-service ad hoc querying of clinical data is one desirable type of functionality. Since most of the data are currently stored in relational or similar form, ad hoc querying is problematic as it requires specialised technical skills and the knowledge of particular data schemas. Results A possible solution is semantic querying where the user formulates queries in terms of domain ontologies that are much easier to navigate and comprehend than data schemas. In this article, we are exploring the possibility of using SADI Semantic Web services for semantic querying of clinical data. We have developed a prototype of a semantic querying infrastructure for the surveillance of, and research on, hospital-acquired infections. Conclusions Our results suggest that SADI can support ad-hoc, self-service, semantic queries of relational data in a Clinical Intelligence context. The use of SADI compares favourably with approaches based on declarative semantic mappings from data schemas to ontologies, such as query rewriting and RDFizing by materialisation, because it can easily cope with situations when (i) some computation is required to turn relational data into RDF or OWL, e.g., to implement temporal reasoning, or (ii) integration with external data sources is necessary. PMID:23497556

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed-representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In the present study we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assumes a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations we demonstrated how findings that challenged attractor-based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments. PMID:23094718

Relative category-specific preservation in semantic dementia? Evidence from 35 cases.

PubMed

Merck, Catherine; Jonin, Pierre-Yves; Vichard, Hélène; Boursiquot, Sandrine Le Moal; Leblay, Virginie; Belliard, Serge

2013-03-01

Category-specific deficits have rarely been reported in semantic dementia (SD). To our knowledge, only four previous studies have documented category-specific deficits, and these have focused on the living versus non-living things contrast rather than on more fine-grained semantic categories. This study aimed to determine whether a category-specific effect could be highlighted by a semantic sorting task administered to 35 SD patients once at baseline and again after 2 years and to 10 Alzheimer's disease patients (AD). We found a relative preservation of fruit and vegetables only in SD. This relative preservation of fruit and vegetables could be considered with regard to the importance of color knowledge in their discrimination. Indeed, color knowledge retrieval is known to depend on the left posterior fusiform gyrus which is relatively spared in SD. Finally, according to predictions of semantic memory models, our findings best fitted the Devlin and Gonnerman's computational account. Copyright © 2013 Elsevier Inc. All rights reserved.

Taxonomic and Thematic Semantic Systems

PubMed Central

Mirman, Daniel; Landrigan, Jon-Frederick; Britt, Allison E.

2017-01-01

Object concepts are critical for nearly all aspects of human cognition, from perception tasks like object recognition, to understanding and producing language, to making meaningful actions. Concepts can have two very different kinds of relations: similarity relations based on shared features (e.g., dog – bear), which are called “taxonomic” relations, and contiguity relations based on co-occurrence in events or scenarios (e.g., dog – leash), which are called “thematic” relations. Here we report a systematic review of experimental psychology and cognitive neuroscience evidence of this distinction in the structure of semantic memory. We propose two principles that may drive the development of distinct taxonomic and thematic semantic systems: (1) differences between which features determine taxonomic vs. thematic relations and (2) differences in the processing required to extract taxonomic vs. thematic relations. This review brings together distinct threads of behavioral, computational, and neuroscience research on semantic memory in support of a functional and neural dissociation, and defines a framework for future studies of semantic memory. PMID:28333494

An Ethnographic Study of the Computational Strategies of a Group of Young Street Vendors in Beirut.

ERIC Educational Resources Information Center

Jurdak, Murad; Shahin, Iman

1999-01-01

Examines the computational strategies of 10 young street vendors in Beirut by describing, comparing, and analyzing computational strategies used in solving three types of problems: (1) transactions in the workplace; (2) word problems; and (3) computation exercises in a school-like setting. Indicates that vendors' use of semantically-based mental…

Stimulus Value Signals in Ventromedial PFC Reflect the Integration of Attribute Value Signals Computed in Fusiform Gyrus and Posterior Superior Temporal Gyrus

PubMed Central

Lim, Seung-Lark; O'Doherty, John P.

2013-01-01

We often have to make choices among multiattribute stimuli (e.g., a food that differs on its taste and health). Behavioral data suggest that choices are made by computing the value of the different attributes and then integrating them into an overall stimulus value signal. However, it is not known whether this theory describes the way the brain computes the stimulus value signals, or how the underlying computations might be implemented. We investigated these questions using a human fMRI task in which individuals had to evaluate T-shirts that varied in their visual esthetic (e.g., color) and semantic (e.g., meaning of logo printed in T-shirt) components. We found that activity in the fusiform gyrus, an area associated with the processing of visual features, correlated with the value of the visual esthetic attributes, but not with the value of the semantic attributes. In contrast, activity in posterior superior temporal gyrus, an area associated with the processing of semantic meaning, exhibited the opposite pattern. Furthermore, both areas exhibited functional connectivity with an area of ventromedial prefrontal cortex that reflects the computation of overall stimulus values at the time of decision. The results provide supporting evidence for the hypothesis that some attribute values are computed in cortical areas specialized in the processing of such features, and that those attribute-specific values are then passed to the vmPFC to be integrated into an overall stimulus value signal to guide the decision. PMID:23678116

Stimulus value signals in ventromedial PFC reflect the integration of attribute value signals computed in fusiform gyrus and posterior superior temporal gyrus.

PubMed

Lim, Seung-Lark; O'Doherty, John P; Rangel, Antonio

2013-05-15

We often have to make choices among multiattribute stimuli (e.g., a food that differs on its taste and health). Behavioral data suggest that choices are made by computing the value of the different attributes and then integrating them into an overall stimulus value signal. However, it is not known whether this theory describes the way the brain computes the stimulus value signals, or how the underlying computations might be implemented. We investigated these questions using a human fMRI task in which individuals had to evaluate T-shirts that varied in their visual esthetic (e.g., color) and semantic (e.g., meaning of logo printed in T-shirt) components. We found that activity in the fusiform gyrus, an area associated with the processing of visual features, correlated with the value of the visual esthetic attributes, but not with the value of the semantic attributes. In contrast, activity in posterior superior temporal gyrus, an area associated with the processing of semantic meaning, exhibited the opposite pattern. Furthermore, both areas exhibited functional connectivity with an area of ventromedial prefrontal cortex that reflects the computation of overall stimulus values at the time of decision. The results provide supporting evidence for the hypothesis that some attribute values are computed in cortical areas specialized in the processing of such features, and that those attribute-specific values are then passed to the vmPFC to be integrated into an overall stimulus value signal to guide the decision.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications

PubMed Central

Kurtz, Camille; Depeursinge, Adrien; Napel, Sandy; Beaulieu, Christopher F.; Rubin, Daniel L.

2014-01-01

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic “soft” prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies. PMID:25036769

Semantics-based distributed I/O with the ParaMEDIC framework.

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

Balaji, P.; Feng, W.; Lin, H.

2008-01-01

Many large-scale applications simultaneously rely on multiple resources for efficient execution. For example, such applications may require both large compute and storage resources; however, very few supercomputing centers can provide large quantities of both. Thus, data generated at the compute site oftentimes has to be moved to a remote storage site for either storage or visualization and analysis. Clearly, this is not an efficient model, especially when the two sites are distributed over a wide-area network. Thus, we present a framework called 'ParaMEDIC: Parallel Metadata Environment for Distributed I/O and Computing' which uses application-specific semantic information to convert the generatedmore » data to orders-of-magnitude smaller metadata at the compute site, transfer the metadata to the storage site, and re-process the metadata at the storage site to regenerate the output. Specifically, ParaMEDIC trades a small amount of additional computation (in the form of data post-processing) for a potentially significant reduction in data that needs to be transferred in distributed environments.« less

Practical Semantic Astronomy

NASA Astrophysics Data System (ADS)

Graham, Matthew; Gray, N.; Burke, D.

2010-01-01

Many activities in the era of data-intensive astronomy are predicated upon some transference of domain knowledge and expertise from human to machine. The semantic infrastructure required to support this is no longer a pipe dream of computer science but a set of practical engineering challenges, more concerned with deployment and performance details than AI abstractions. The application of such ideas promises to help in such areas as contextual data access, exploiting distributed annotation and heterogeneous sources, and intelligent data dissemination and discovery. In this talk, we will review the status and use of semantic technologies in astronomy, particularly to address current problems in astroinformatics, with such projects as SKUA and AstroCollation.

Neural bases of event knowledge and syntax integration in comprehension of complex sentences.

PubMed

Malaia, Evie; Newman, Sharlene

2015-01-01

Comprehension of complex sentences is necessarily supported by both syntactic and semantic knowledge, but what linguistic factors trigger a readers' reliance on a specific system? This functional neuroimaging study orthogonally manipulated argument plausibility and verb event type to investigate cortical bases of the semantic effect on argument comprehension during reading. The data suggest that telic verbs facilitate online processing by means of consolidating the event schemas in episodic memory and by easing the computation of syntactico-thematic hierarchies in the left inferior frontal gyrus. The results demonstrate that syntax-semantics integration relies on trade-offs among a distributed network of regions for maximum comprehension efficiency.

Empirical Distributional Semantics: Methods and Biomedical Applications

PubMed Central

Cohen, Trevor; Widdows, Dominic

2009-01-01

Over the past fifteen years, a range of methods have been developed that are able to learn human-like estimates of the semantic relatedness between terms from the way in which these terms are distributed in a corpus of unannotated natural language text. These methods have also been evaluated in a number of applications in the cognitive science, computational linguistics and the information retrieval literatures. In this paper, we review the available methodologies for derivation of semantic relatedness from free text, as well as their evaluation in a variety of biomedical and other applications. Recent methodological developments, and their applicability to several existing applications are also discussed. PMID:19232399

Social and Personal Factors in Semantic Infusion Projects

NASA Astrophysics Data System (ADS)

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

2009-12-01

As part of our semantic data framework activities across multiple, diverse disciplines we required the involvement of domain scientists, computer scientists, software engineers, data managers, and often, social scientists. This involvement from a cross-section of disciplines turns out to be a social exercise as much as it is a technical and methodical activity. Each member of the team is used to different modes of working, expectations, vocabularies, levels of participation, and incentive and reward systems. We will examine how both roles and personal responsibilities play in the development of semantic infusion projects, and how an iterative development cycle can contribute to the successful completion of such a project.

The Interaction between Semantic Representation and Episodic Memory.

PubMed

Fang, Jing; Rüther, Naima; Bellebaum, Christian; Wiskott, Laurenz; Cheng, Sen

2018-02-01

The experimental evidence on the interrelation between episodic memory and semantic memory is inconclusive. Are they independent systems, different aspects of a single system, or separate but strongly interacting systems? Here, we propose a computational role for the interaction between the semantic and episodic systems that might help resolve this debate. We hypothesize that episodic memories are represented as sequences of activation patterns. These patterns are the output of a semantic representational network that compresses the high-dimensional sensory input. We show quantitatively that the accuracy of episodic memory crucially depends on the quality of the semantic representation. We compare two types of semantic representations: appropriate representations, which means that the representation is used to store input sequences that are of the same type as those that it was trained on, and inappropriate representations, which means that stored inputs differ from the training data. Retrieval accuracy is higher for appropriate representations because the encoded sequences are less divergent than those encoded with inappropriate representations. Consistent with our model prediction, we found that human subjects remember some aspects of episodes significantly more accurately if they had previously been familiarized with the objects occurring in the episode, as compared to episodes involving unfamiliar objects. We thus conclude that the interaction with the semantic system plays an important role for episodic memory.

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates

PubMed Central

Kuhlmann, Michael; Hofmann, Markus J.; Jacobs, Arthur M.

2017-01-01

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks. PMID:28348538

If You Don't Have Valence, Ask Your Neighbor: Evaluation of Neutral Words as a Function of Affective Semantic Associates.

PubMed

Kuhlmann, Michael; Hofmann, Markus J; Jacobs, Arthur M

2017-01-01

How do humans perform difficult forced-choice evaluations, e.g., of words that have been previously rated as being neutral? Here we tested the hypothesis that in this case, the valence of semantic associates is of significant influence. From corpus based co-occurrence statistics as a measure of association strength we computed individual neighborhoods for single neutral words comprised of the 10 words with the largest association strength. We then selected neutral words according to the valence of the associated words included in the neighborhoods, which were either mostly positive, mostly negative, mostly neutral or mixed positive and negative, and tested them using a valence decision task (VDT). The data showed that the valence of semantic neighbors can predict valence judgments to neutral words. However, all but the positive neighborhood items revealed a high tendency to elicit negative responses. For the positive and negative neighborhood categories responses congruent with the neighborhood's valence were faster than incongruent responses. We interpret this effect as a semantic network process that supports the evaluation of neutral words by assessing the valence of the associative semantic neighborhood. In this perspective, valence is considered a semantic super-feature, at least partially represented in associative activation patterns of semantic networks.

Selective Attention to Semantic and Syntactic Features Modulates Sentence Processing Networks in Anterior Temporal Cortex

PubMed Central

Rogalsky, Corianne

2009-01-01

Numerous studies have identified an anterior temporal lobe (ATL) region that responds preferentially to sentence-level stimuli. It is unclear, however, whether this activity reflects a response to syntactic computations or some form of semantic integration. This distinction is difficult to investigate with the stimulus manipulations and anomaly detection paradigms traditionally implemented. The present functional magnetic resonance imaging study addresses this question via a selective attention paradigm. Subjects monitored for occasional semantic anomalies or occasional syntactic errors, thus directing their attention to semantic integration, or syntactic properties of the sentences. The hemodynamic response in the sentence-selective ATL region (defined with a localizer scan) was examined during anomaly/error-free sentences only, to avoid confounds due to error detection. The majority of the sentence-specific region of interest was equally modulated by attention to syntactic or compositional semantic features, whereas a smaller subregion was only modulated by the semantic task. We suggest that the sentence-specific ATL region is sensitive to both syntactic and integrative semantic functions during sentence processing, with a smaller portion of this area preferentially involved in the later. This study also suggests that selective attention paradigms may be effective tools to investigate the functional diversity of networks involved in sentence processing. PMID:18669589

Semantic Web technologies for the big data in life sciences.

PubMed

Wu, Hongyan; Yamaguchi, Atsuko

2014-08-01

The life sciences field is entering an era of big data with the breakthroughs of science and technology. More and more big data-related projects and activities are being performed in the world. Life sciences data generated by new technologies are continuing to grow in not only size but also variety and complexity, with great speed. To ensure that big data has a major influence in the life sciences, comprehensive data analysis across multiple data sources and even across disciplines is indispensable. The increasing volume of data and the heterogeneous, complex varieties of data are two principal issues mainly discussed in life science informatics. The ever-evolving next-generation Web, characterized as the Semantic Web, is an extension of the current Web, aiming to provide information for not only humans but also computers to semantically process large-scale data. The paper presents a survey of big data in life sciences, big data related projects and Semantic Web technologies. The paper introduces the main Semantic Web technologies and their current situation, and provides a detailed analysis of how Semantic Web technologies address the heterogeneous variety of life sciences big data. The paper helps to understand the role of Semantic Web technologies in the big data era and how they provide a promising solution for the big data in life sciences.

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

PubMed

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

2007-01-01

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

ICCE/ICCAI 2000 Keynote Papers.

ERIC Educational Resources Information Center

2000

This document contains the four keynote papers from ICCE/ICCAI 2000 (International Conference on Computers in Education/International Conference on Computer-Assisted Instruction). "Using Technologies To Model Student Problem Spaces" (David Jonassen) contrasts examples of semantic network, expert system, and systems modeling…

Semantics driven approach for knowledge acquisition from EMRs.

PubMed

Perera, Sujan; Henson, Cory; Thirunarayan, Krishnaprasad; Sheth, Amit; Nair, Suhas

2014-03-01

Semantic computing technologies have matured to be applicable to many critical domains such as national security, life sciences, and health care. However, the key to their success is the availability of a rich domain knowledge base. The creation and refinement of domain knowledge bases pose difficult challenges. The existing knowledge bases in the health care domain are rich in taxonomic relationships, but they lack nontaxonomic (domain) relationships. In this paper, we describe a semiautomatic technique for enriching existing domain knowledge bases with causal relationships gleaned from Electronic Medical Records (EMR) data. We determine missing causal relationships between domain concepts by validating domain knowledge against EMR data sources and leveraging semantic-based techniques to derive plausible relationships that can rectify knowledge gaps. Our evaluation demonstrates that semantic techniques can be employed to improve the efficiency of knowledge acquisition.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia.

PubMed

Evans, William S; Quimby, Megan; Dickey, Michael Walsh; Dickerson, Bradford C

2016-01-01

Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable "practice-based evidence" for motivating further treatment research.

Relearning and Retaining Personally-Relevant Words using Computer-Based Flashcard Software in Primary Progressive Aphasia

PubMed Central

Evans, William S.; Quimby, Megan; Dickey, Michael Walsh; Dickerson, Bradford C.

2016-01-01

Although anomia treatments have often focused on training small sets of words in the hopes of promoting generalization to untrained items, an alternative is to directly train a larger set of words more efficiently. The current case study reports on a novel treatment for a patient with semantic variant Primary Progressive Aphasia (svPPA), in which the patient was taught to make and practice flashcards for personally-relevant words using an open-source computer program (Anki). Results show that the patient was able to relearn and retain a large subset of her studied words for up to 20 months, the full duration of the study period. At the end of treatment, she showed good retention for 139 words. While only a subset of the 591 studied overall, this is still far more words than is typically targeted in svPPA interventions. Furthermore, she showed evidence of generalization to perceptually distinct stimuli during confrontation naming and temporary gains in semantic fluency, suggesting limited gains in semantic knowledge as a result of training. This case represents a successful example of patient-centered treatment, where the patient used a computer-based intervention independently at home. It also illustrates how data captured from computer-based treatments during routine clinical care can provide valuable “practice-based evidence” for motivating further treatment research. PMID:27899886

Single Sided Messaging v. 0.6.6

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

Curry, Matthew Leon; Farmer, Matthew Shane; Hassani, Amin

Single-Sided Messaging (SSM) is a portable, multitransport networking library that enables applications to leverage potential one-sided capabilities of underlying network transports. It also provides desirable semantics that services for highperformance, massively parallel computers can leverage, such as an explicit cancel operation for pending transmissions, as well as enhanced matching semantics favoring large numbers of buffers attached to a single match entry. This release supports TCP/IP, shared memory, and Infiniband.

High-performance analysis of filtered semantic graphs

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

Buluc, Aydin; Fox, Armando; Gilbert, John R.

2012-01-01

High performance is a crucial consideration when executing a complex analytic query on a massive semantic graph. In a semantic graph, vertices and edges carry "attributes" of various types. Analytic queries on semantic graphs typically depend on the values of these attributes; thus, the computation must either view the graph through a filter that passes only those individual vertices and edges of interest, or else must first materialize a subgraph or subgraphs consisting of only the vertices and edges of interest. The filtered approach is superior due to its generality, ease of use, and memory efficiency, but may carry amore » performance cost. In the Knowledge Discovery Toolbox (KDT), a Python library for parallel graph computations, the user writes filters in a high-level language, but those filters result in relatively low performance due to the bottleneck of having to call into the Python interpreter for each edge. In this work, we use the Selective Embedded JIT Specialization (SEJITS) approach to automatically translate filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance C++ /MPI Combinatorial BLAS engine, and show that the productivity gained by using a high-level filtering language comes without sacrificing performance.« less

Exploiting semantics for sensor re-calibration in event detection systems

NASA Astrophysics Data System (ADS)

Vaisenberg, Ronen; Ji, Shengyue; Hore, Bijit; Mehrotra, Sharad; Venkatasubramanian, Nalini

2008-01-01

Event detection from a video stream is becoming an important and challenging task in surveillance and sentient systems. While computer vision has been extensively studied to solve different kinds of detection problems over time, it is still a hard problem and even in a controlled environment only simple events can be detected with a high degree of accuracy. Instead of struggling to improve event detection using image processing only, we bring in semantics to direct traditional image processing. Semantics are the underlying facts that hide beneath video frames, which can not be "seen" directly by image processing. In this work we demonstrate that time sequence semantics can be exploited to guide unsupervised re-calibration of the event detection system. We present an instantiation of our ideas by using an appliance as an example--Coffee Pot level detection based on video data--to show that semantics can guide the re-calibration of the detection model. This work exploits time sequence semantics to detect when re-calibration is required to automatically relearn a new detection model for the newly evolved system state and to resume monitoring with a higher rate of accuracy.

A Semantic Based Policy Management Framework for Cloud Computing Environments

ERIC Educational Resources Information Center

Takabi, Hassan

2013-01-01

Cloud computing paradigm has gained tremendous momentum and generated intensive interest. Although security issues are delaying its fast adoption, cloud computing is an unstoppable force and we need to provide security mechanisms to ensure its secure adoption. In this dissertation, we mainly focus on issues related to policy management and access…

Centrality-based Selection of Semantic Resources for Geosciences

NASA Astrophysics Data System (ADS)

Cerba, Otakar; Jedlicka, Karel

2017-04-01

Semantical questions intervene almost in all disciplines dealing with geographic data and information, because relevant semantics is crucial for any way of communication and interaction among humans as well as among machines. But the existence of such a large number of different semantic resources (such as various thesauri, controlled vocabularies, knowledge bases or ontologies) makes the process of semantics implementation much more difficult and complicates the use of the advantages of semantics. This is because in many cases users are not able to find the most suitable resource for their purposes. The research presented in this paper introduces a methodology consisting of an analysis of identical relations in Linked Data space, which covers a majority of semantic resources, to find a suitable resource of semantic information. Identical links interconnect representations of an object or a concept in various semantic resources. Therefore this type of relations is considered to be crucial from the view of Linked Data, because these links provide new additional information, including various views on one concept based on different cultural or regional aspects (so-called social role of Linked Data). For these reasons it is possible to declare that one reasonable criterion for feasible semantic resources for almost all domains, including geosciences, is their position in a network of interconnected semantic resources and level of linking to other knowledge bases and similar products. The presented methodology is based on searching of mutual connections between various instances of one concept using "follow your nose" approach. The extracted data on interconnections between semantic resources are arranged to directed graphs and processed by various metrics patterned on centrality computing (degree, closeness or betweenness centrality). Semantic resources recommended by the research could be used for providing semantically described keywords for metadata records or as names of items in data models. Such an approach enables much more efficient data harmonization, integration, sharing and exploitation. * * * * This publication was supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports. This publication was supported by project Data-Driven Bioeconomy (DataBio) from the ICT-15-2016-2017, Big Data PPP call.

Revealing the Dynamic Modulations That Underpin a Resilient Neural Network for Semantic Cognition: An fMRI Investigation in Patients With Anterior Temporal Lobe Resection.

PubMed

Rice, Grace E; Caswell, Helen; Moore, Perry; Lambon Ralph, Matthew A; Hoffman, Paul

2018-06-06

One critical feature of any well-engineered system is its resilience to perturbation and minor damage. The purpose of the current study was to investigate how resilience is achieved in higher cognitive systems, which we explored through the domain of semantic cognition. Convergent evidence implicates the bilateral anterior temporal lobes (ATLs) as a conceptual knowledge hub. While bilateral damage to this region produces profound semantic impairment, unilateral atrophy/resection or transient perturbation has a limited effect. Two neural mechanisms might underpin this resilience to unilateral ATL damage: 1) the undamaged ATL upregulates its activation in order to compensate; and/or 2) prefrontal regions involved in control of semantic retrieval upregulate to compensate for the impoverished semantic representations that follow from ATL damage. To test these possibilities, 34 postsurgical temporal lobe epilepsy patients and 20 age-matched controls were scanned whilst completing semantic tasks. Pictorial tasks, which produced bilateral frontal and temporal activation, showed few activation differences between patients and control participants. Written word tasks, however, produced a left-lateralized activation pattern and greater differences between the groups. Patients with right ATL resection increased activation in left inferior frontal gyrus (IFG). Patients with left ATL resection upregulated both the right ATL and right IFG. Consistent with recent computational models, these results indicate that 1) written word semantic processing in patients with ATL resection is supported by upregulation of semantic knowledge and control regions, principally in the undamaged hemisphere, and 2) pictorial semantic processing is less affected, presumably because it draws on a more bilateral network.

The effects of semantic congruency: a research of audiovisual P300-speller.

PubMed

Cao, Yong; An, Xingwei; Ke, Yufeng; Jiang, Jin; Yang, Hanjun; Chen, Yuqian; Jiao, Xuejun; Qi, Hongzhi; Ming, Dong

2017-07-25

Over the past few decades, there have been many studies of aspects of brain-computer interface (BCI). Of particular interests are event-related potential (ERP)-based BCI spellers that aim at helping mental typewriting. Nowadays, audiovisual unimodal stimuli based BCI systems have attracted much attention from researchers, and most of the existing studies of audiovisual BCIs were based on semantic incongruent stimuli paradigm. However, no related studies had reported that whether there is difference of system performance or participant comfort between BCI based on semantic congruent paradigm and that based on semantic incongruent paradigm. The goal of this study was to investigate the effects of semantic congruency in system performance and participant comfort in audiovisual BCI. Two audiovisual paradigms (semantic congruent and incongruent) were adopted, and 11 healthy subjects participated in the experiment. High-density electrical mapping of ERPs and behavioral data were measured for the two stimuli paradigms. The behavioral data indicated no significant difference between congruent and incongruent paradigms for offline classification accuracy. Nevertheless, eight of the 11 participants reported their priority to semantic congruent experiment, two reported no difference between the two conditions, and only one preferred the semantic incongruent paradigm. Besides, the result indicted that higher amplitude of ERP was found in incongruent stimuli based paradigm. In a word, semantic congruent paradigm had a better participant comfort, and maintained the same recognition rate as incongruent paradigm. Furthermore, our study suggested that the paradigm design of spellers must take both system performance and user experience into consideration rather than merely pursuing a larger ERP response.

Representational constraints on children's suggestibility.

PubMed

Ceci, Stephen J; Papierno, Paul B; Kulkofsky, Sarah

2007-06-01

In a multistage experiment, twelve 4- and 9-year-old children participated in a triad rating task. Their ratings were mapped with multidimensional scaling, from which euclidean distances were computed to operationalize semantic distance between items in target pairs. These children and age-mates then participated in an experiment that employed these target pairs in a story, which was followed by a misinformation manipulation. Analyses linked individual and developmental differences in suggestibility to children's representations of the target items. Semantic proximity was a strong predictor of differences in suggestibility: The closer a suggested distractor was to the original item's representation, the greater was the distractor's suggestive influence. The triad participants' semantic proximity subsequently served as the basis for correctly predicting memory performance in the larger group. Semantic proximity enabled a priori counterintuitive predictions of reverse age-related trends to be confirmed whenever the distance between representations of items in a target pair was greater for younger than for older children.

Not all triads are created equal: further support for the importance of visual and semantic proximity in object identification.

PubMed

Schweizer, Tom A; Dixon, Mike J; Desmarais, Geneviève; Smith, Stephen D

2002-01-01

Identification deficits were investigated in ELM, a temporal lobe stroke patient with category-specific deficits. We replicated previous work done on FS, a patient with category specific deficits as a result of herpes viral encephalitis. ELM was tested using novel, computer generated shapes that were paired with artifact labels. We paired semantically close or disparate labels to shapes and ELM attempted to learn these pairings. Overall, ELM's shape-label confusions were most detrimentally affected when we used labels that referred to objects that were visually and semantically close. However, as with FS, ELM had as many errors when shapes were paired with the labels "donut," "tire," and "washer" as he did when they were paired with visually and semantically close artifact labels. Two explanations are put forth to account for the anomalous performance by both patients on the triad of donut-tire-washer.

Towards semantically sensitive text clustering: a feature space modeling technology based on dimension extension.

PubMed

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach.

Developing A Web-based User Interface for Semantic Information Retrieval

NASA Technical Reports Server (NTRS)

Berrios, Daniel C.; Keller, Richard M.

2003-01-01

While there are now a number of languages and frameworks that enable computer-based systems to search stored data semantically, the optimal design for effective user interfaces for such systems is still uncle ar. Such interfaces should mask unnecessary query detail from users, yet still allow them to build queries of arbitrary complexity without significant restrictions. We developed a user interface supporting s emantic query generation for Semanticorganizer, a tool used by scient ists and engineers at NASA to construct networks of knowledge and dat a. Through this interface users can select node types, node attribute s and node links to build ad-hoc semantic queries for searching the S emanticOrganizer network.

Towards Semantically Sensitive Text Clustering: A Feature Space Modeling Technology Based on Dimension Extension

PubMed Central

Liu, Yuanchao; Liu, Ming; Wang, Xin

2015-01-01

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and that in extension space, the adverse effects of the complexity and diversity of natural language can be addressed and clustering semantic sensitivity can be improved correspondingly. The generated clusters can be organized using different granularities. The experimental evaluations on well-known clustering algorithms and datasets have verified the effectiveness of our approach. PMID:25794172

CelOWS: an ontology based framework for the provision of semantic web services related to biological models.

PubMed

Matos, Ely Edison; Campos, Fernanda; Braga, Regina; Palazzi, Daniele

2010-02-01

The amount of information generated by biological research has lead to an intensive use of models. Mathematical and computational modeling needs accurate description to share, reuse and simulate models as formulated by original authors. In this paper, we introduce the Cell Component Ontology (CelO), expressed in OWL-DL. This ontology captures both the structure of a cell model and the properties of functional components. We use this ontology in a Web project (CelOWS) to describe, query and compose CellML models, using semantic web services. It aims to improve reuse and composition of existent components and allow semantic validation of new models.

Parametric effects of syntactic-semantic conflict in Broca's area during sentence processing.

PubMed

Thothathiri, Malathi; Kim, Albert; Trueswell, John C; Thompson-Schill, Sharon L

2012-03-01

The hypothesized role of Broca's area in sentence processing ranges from domain-general executive function to domain-specific computation that is specific to certain syntactic structures. We examined this issue by manipulating syntactic structure and conflict between syntactic and semantic cues in a sentence processing task. Functional neuroimaging revealed that activation within several Broca's area regions of interest reflected the parametric variation in syntactic-semantic conflict. These results suggest that Broca's area supports sentence processing by mediating between multiple incompatible constraints on sentence interpretation, consistent with this area's well-known role in conflict resolution in other linguistic and non-linguistic tasks. Copyright © 2011 Elsevier Inc. All rights reserved.

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

PubMed Central

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language. PMID:22615554

The layer-oriented approach to declarative languages for biological modeling.

PubMed

Raikov, Ivan; De Schutter, Erik

2012-01-01

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

COEUS: “semantic web in a box” for biomedical applications

PubMed Central

2012-01-01

Background As the “omics” revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter’s complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. Results COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a “semantic web in a box” approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. Conclusions The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/. PMID:23244467

Convergence of Health Level Seven Version 2 Messages to Semantic Web Technologies for Software-Intensive Systems in Telemedicine Trauma Care.

PubMed

Menezes, Pedro Monteiro; Cook, Timothy Wayne; Cavalini, Luciana Tricai

2016-01-01

To present the technical background and the development of a procedure that enriches the semantics of Health Level Seven version 2 (HL7v2) messages for software-intensive systems in telemedicine trauma care. This study followed a multilevel model-driven approach for the development of semantically interoperable health information systems. The Pre-Hospital Trauma Life Support (PHTLS) ABCDE protocol was adopted as the use case. A prototype application embedded the semantics into an HL7v2 message as an eXtensible Markup Language (XML) file, which was validated against an XML schema that defines constraints on a common reference model. This message was exchanged with a second prototype application, developed on the Mirth middleware, which was also used to parse and validate both the original and the hybrid messages. Both versions of the data instance (one pure XML, one embedded in the HL7v2 message) were equally validated and the RDF-based semantics recovered by the receiving side of the prototype from the shared XML schema. This study demonstrated the semantic enrichment of HL7v2 messages for intensive-software telemedicine systems for trauma care, by validating components of extracts generated in various computing environments. The adoption of the method proposed in this study ensures the compliance of the HL7v2 standard in Semantic Web technologies.

COEUS: "semantic web in a box" for biomedical applications.

PubMed

Lopes, Pedro; Oliveira, José Luís

2012-12-17

As the "omics" revolution unfolds, the growth in data quantity and diversity is bringing about the need for pioneering bioinformatics software, capable of significantly improving the research workflow. To cope with these computer science demands, biomedical software engineers are adopting emerging semantic web technologies that better suit the life sciences domain. The latter's complex relationships are easily mapped into semantic web graphs, enabling a superior understanding of collected knowledge. Despite increased awareness of semantic web technologies in bioinformatics, their use is still limited. COEUS is a new semantic web framework, aiming at a streamlined application development cycle and following a "semantic web in a box" approach. The framework provides a single package including advanced data integration and triplification tools, base ontologies, a web-oriented engine and a flexible exploration API. Resources can be integrated from heterogeneous sources, including CSV and XML files or SQL and SPARQL query results, and mapped directly to one or more ontologies. Advanced interoperability features include REST services, a SPARQL endpoint and LinkedData publication. These enable the creation of multiple applications for web, desktop or mobile environments, and empower a new knowledge federation layer. The platform, targeted at biomedical application developers, provides a complete skeleton ready for rapid application deployment, enhancing the creation of new semantic information systems. COEUS is available as open source at http://bioinformatics.ua.pt/coeus/.

A DNA-based semantic fusion model for remote sensing data.

PubMed

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology.

A DNA-Based Semantic Fusion Model for Remote Sensing Data

PubMed Central

Sun, Heng; Weng, Jian; Yu, Guangchuang; Massawe, Richard H.

2013-01-01

Semantic technology plays a key role in various domains, from conversation understanding to algorithm analysis. As the most efficient semantic tool, ontology can represent, process and manage the widespread knowledge. Nowadays, many researchers use ontology to collect and organize data's semantic information in order to maximize research productivity. In this paper, we firstly describe our work on the development of a remote sensing data ontology, with a primary focus on semantic fusion-driven research for big data. Our ontology is made up of 1,264 concepts and 2,030 semantic relationships. However, the growth of big data is straining the capacities of current semantic fusion and reasoning practices. Considering the massive parallelism of DNA strands, we propose a novel DNA-based semantic fusion model. In this model, a parallel strategy is developed to encode the semantic information in DNA for a large volume of remote sensing data. The semantic information is read in a parallel and bit-wise manner and an individual bit is converted to a base. By doing so, a considerable amount of conversion time can be saved, i.e., the cluster-based multi-processes program can reduce the conversion time from 81,536 seconds to 4,937 seconds for 4.34 GB source data files. Moreover, the size of result file recording DNA sequences is 54.51 GB for parallel C program compared with 57.89 GB for sequential Perl. This shows that our parallel method can also reduce the DNA synthesis cost. In addition, data types are encoded in our model, which is a basis for building type system in our future DNA computer. Finally, we describe theoretically an algorithm for DNA-based semantic fusion. This algorithm enables the process of integration of the knowledge from disparate remote sensing data sources into a consistent, accurate, and complete representation. This process depends solely on ligation reaction and screening operations instead of the ontology. PMID:24116207

WebGIS based on semantic grid model and web services

NASA Astrophysics Data System (ADS)

Zhang, WangFei; Yue, CaiRong; Gao, JianGuo

2009-10-01

As the combination point of the network technology and GIS technology, WebGIS has got the fast development in recent years. With the restriction of Web and the characteristics of GIS, traditional WebGIS has some prominent problems existing in development. For example, it can't accomplish the interoperability of heterogeneous spatial databases; it can't accomplish the data access of cross-platform. With the appearance of Web Service and Grid technology, there appeared great change in field of WebGIS. Web Service provided an interface which can give information of different site the ability of data sharing and inter communication. The goal of Grid technology was to make the internet to a large and super computer, with this computer we can efficiently implement the overall sharing of computing resources, storage resource, data resource, information resource, knowledge resources and experts resources. But to WebGIS, we only implement the physically connection of data and information and these is far from the enough. Because of the different understanding of the world, following different professional regulations, different policies and different habits, the experts in different field will get different end when they observed the same geographic phenomenon and the semantic heterogeneity produced. Since these there are large differences to the same concept in different field. If we use the WebGIS without considering of the semantic heterogeneity, we will answer the questions users proposed wrongly or we can't answer the questions users proposed. To solve this problem, this paper put forward and experienced an effective method of combing semantic grid and Web Services technology to develop WebGIS. In this paper, we studied the method to construct ontology and the method to combine Grid technology and Web Services and with the detailed analysis of computing characteristics and application model in the distribution of data, we designed the WebGIS query system driven by ontology based on Grid technology and Web Services.

Knowledge acquisition, semantic text mining, and security risks in health and biomedical informatics

PubMed Central

Huang, Jingshan; Dou, Dejing; Dang, Jiangbo; Pardue, J Harold; Qin, Xiao; Huan, Jun; Gerthoffer, William T; Tan, Ming

2012-01-01

Computational techniques have been adopted in medical and biological systems for a long time. There is no doubt that the development and application of computational methods will render great help in better understanding biomedical and biological functions. Large amounts of datasets have been produced by biomedical and biological experiments and simulations. In order for researchers to gain knowledge from original data, nontrivial transformation is necessary, which is regarded as a critical link in the chain of knowledge acquisition, sharing, and reuse. Challenges that have been encountered include: how to efficiently and effectively represent human knowledge in formal computing models, how to take advantage of semantic text mining techniques rather than traditional syntactic text mining, and how to handle security issues during the knowledge sharing and reuse. This paper summarizes the state-of-the-art in these research directions. We aim to provide readers with an introduction of major computing themes to be applied to the medical and biological research. PMID:22371823

Fast Semantic Segmentation of 3d Point Clouds with Strongly Varying Density

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2016-06-01

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point's (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

Relating UMLS semantic types and task-based ontology to computer-interpretable clinical practice guidelines.

PubMed

Kumar, Anand; Ciccarese, Paolo; Quaglini, Silvana; Stefanelli, Mario; Caffi, Ezio; Boiocchi, Lorenzo

2003-01-01

Medical knowledge in clinical practice guideline (GL) texts is the source of task-based computer-interpretable clinical guideline models (CIGMs). We have used Unified Medical Language System (UMLS) semantic types (STs) to understand the percentage of GL text which belongs to a particular ST. We also use UMLS semantic network together with the CIGM-specific ontology to derive a semantic meaning behind the GL text. In order to achieve this objective, we took nine GL texts from the National Guideline Clearinghouse (NGC) and marked up the text dealing with a particular ST. The STs we took into consideration were restricted taking into account the requirements of a task-based CIGM. We used DARPA Agent Markup Language and Ontology Inference Layer (DAML + OIL) to create the UMLS and CIGM specific semantic network. For the latter, as a bench test, we used the 1999 WHO-International Society of Hypertension Guidelines for the Management of Hypertension. We took into consideration the UMLS STs closest to the clinical tasks. The percentage of the GL text dealing with the ST "Health Care Activity" and subtypes "Laboratory Procedure", "Diagnostic Procedure" and "Therapeutic or Preventive Procedure" were measured. The parts of text belonging to other STs or comments were separated. A mapping of terms belonging to other STs was done to the STs under "HCA" for representation in DAML + OIL. As a result, we found that the three STs under "HCA" were the predominant STs present in the GL text. In cases where the terms of related STs existed, they were mapped into one of the three STs. The DAML + OIL representation was able to describe the hierarchy in task-based CIGMs. To conclude, we understood that the three STs could be used to represent the semantic network of the task-bases CIGMs. We identified some mapping operators which could be used for the mapping of other STs into these.

BioPortal: An Open-Source Community-Based Ontology Repository

NASA Astrophysics Data System (ADS)

Noy, N.; NCBO Team

2011-12-01

Advances in computing power and new computational techniques have changed the way researchers approach science. In many fields, one of the most fruitful approaches has been to use semantically aware software to break down the barriers among disparate domains, systems, data sources, and technologies. Such software facilitates data aggregation, improves search, and ultimately allows the detection of new associations that were previously not detectable. Achieving these analyses requires software systems that take advantage of the semantics and that can intelligently negotiate domains and knowledge sources, identifying commonality across systems that use different and conflicting vocabularies, while understanding apparent differences that may be concealed by the use of superficially similar terms. An ontology, a semantically rich vocabulary for a domain of interest, is the cornerstone of software for bridging systems, domains, and resources. However, as ontologies become the foundation of all semantic technologies in e-science, we must develop an infrastructure for sharing ontologies, finding and evaluating them, integrating and mapping among them, and using ontologies in applications that help scientists process their data. BioPortal [1] is an open-source on-line community-based ontology repository that has been used as a critical component of semantic infrastructure in several domains, including biomedicine and bio-geochemical data. BioPortal, uses the social approaches in the Web 2.0 style to bring structure and order to the collection of biomedical ontologies. It enables users to provide and discuss a wide array of knowledge components, from submitting the ontologies themselves, to commenting on and discussing classes in the ontologies, to reviewing ontologies in the context of their own ontology-based projects, to creating mappings between overlapping ontologies and discussing and critiquing the mappings. Critically, it provides web-service access to all its content, enabling its integration in semantically enriched applications. [1] Noy, N.F., Shah, N.H., et al., BioPortal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Res, 2009. 37(Web Server issue): p. W170-3.

Drug knowledge expressed as computable semantic triples.

PubMed

Elkin, Peter L; Carter, John S; Nabar, Manasi; Tuttle, Mark; Lincoln, Michael; Brown, Steven H

2011-01-01

The majority of questions that arise in the practice of medicine relate to drug information. Additionally, adverse reactions account for as many as 98,000 deaths per year in the United States. Adverse drug reactions account for a significant portion of those errors. Many authors believe that clinical decision support associated with computerized physician order entry has the potential to decrease this adverse drug event rate. This decision support requires knowledge to drive the process. One important and rich source of drug knowledge is the DailyMed product labels. In this project we used computationally extracted SNOMED CT™ codified data associated with each section of each product label as input to a rules engine that created computable assertional knowledge in the form of semantic triples. These are expressed in the form of "Drug" HasIndication "SNOMED CT™". The information density of drug labels is deep, broad and quite substantial. By providing a computable form of this information content from drug labels we make these important axioms (facts) more accessible to computer programs designed to support improved care.

Computationally Efficient Clustering of Audio-Visual Meeting Data

NASA Astrophysics Data System (ADS)

Hung, Hayley; Friedland, Gerald; Yeo, Chuohao

This chapter presents novel computationally efficient algorithms to extract semantically meaningful acoustic and visual events related to each of the participants in a group discussion using the example of business meeting recordings. The recording setup involves relatively few audio-visual sensors, comprising a limited number of cameras and microphones. We first demonstrate computationally efficient algorithms that can identify who spoke and when, a problem in speech processing known as speaker diarization. We also extract visual activity features efficiently from MPEG4 video by taking advantage of the processing that was already done for video compression. Then, we present a method of associating the audio-visual data together so that the content of each participant can be managed individually. The methods presented in this article can be used as a principal component that enables many higher-level semantic analysis tasks needed in search, retrieval, and navigation.

Computational methods to extract meaning from text and advance theories of human cognition.

PubMed

McNamara, Danielle S

2011-01-01

Over the past two decades, researchers have made great advances in the area of computational methods for extracting meaning from text. This research has to a large extent been spurred by the development of latent semantic analysis (LSA), a method for extracting and representing the meaning of words using statistical computations applied to large corpora of text. Since the advent of LSA, researchers have developed and tested alternative statistical methods designed to detect and analyze meaning in text corpora. This research exemplifies how statistical models of semantics play an important role in our understanding of cognition and contribute to the field of cognitive science. Importantly, these models afford large-scale representations of human knowledge and allow researchers to explore various questions regarding knowledge, discourse processing, text comprehension, and language. This topic includes the latest progress by the leading researchers in the endeavor to go beyond LSA. Copyright © 2010 Cognitive Science Society, Inc.

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques

PubMed Central

2014-01-01

Background Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. Results We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. Conclusions SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats. PMID:25093070

SEE: structured representation of scientific evidence in the biomedical domain using Semantic Web techniques.

PubMed

Bölling, Christian; Weidlich, Michael; Holzhütter, Hermann-Georg

2014-01-01

Accounts of evidence are vital to evaluate and reproduce scientific findings and integrate data on an informed basis. Currently, such accounts are often inadequate, unstandardized and inaccessible for computational knowledge engineering even though computational technologies, among them those of the semantic web, are ever more employed to represent, disseminate and integrate biomedical data and knowledge. We present SEE (Semantic EvidencE), an RDF/OWL based approach for detailed representation of evidence in terms of the argumentative structure of the supporting background for claims even in complex settings. We derive design principles and identify minimal components for the representation of evidence. We specify the Reasoning and Discourse Ontology (RDO), an OWL representation of the model of scientific claims, their subjects, their provenance and their argumentative relations underlying the SEE approach. We demonstrate the application of SEE and illustrate its design patterns in a case study by providing an expressive account of the evidence for certain claims regarding the isolation of the enzyme glutamine synthetase. SEE is suited to provide coherent and computationally accessible representations of evidence-related information such as the materials, methods, assumptions, reasoning and information sources used to establish a scientific finding by adopting a consistently claim-based perspective on scientific results and their evidence. SEE allows for extensible evidence representations, in which the level of detail can be adjusted and which can be extended as needed. It supports representation of arbitrary many consecutive layers of interpretation and attribution and different evaluations of the same data. SEE and its underlying model could be a valuable component in a variety of use cases that require careful representation or examination of evidence for data presented on the semantic web or in other formats.

When Singular and Plural are Both Grammatical: Semantic and Morphophonological Effects in Agreement

PubMed Central

Mirković, Jelena; MacDonald, Maryellen C.

2013-01-01

The utterance planning processes allowing speakers to produce agreement between subjects and verbs (the catspl arepl asleep) have been the topic of extensive study as a window into language production mechanisms. A key question has been the extent to which agreement processing is influenced by semantic and phonological factors. Most prior studies have found limited effects of non-syntactic, particularly phonological factors, leading to conclusions that agreement is computed by a process influenced strongly by syntactic factors and with only a minor contribution of semantics. This conclusion may have been influenced by use of agreement error data as the main dependent variable, because errors are rare, potentially reducing sensitivity to the interaction of several factors. Two studies investigate agreement processing in Serbian, which allows both singular and plural verb forms to agree with plural nouns in some constructions. We use these constructions to further investigate the contribution of semantic factors to agreement, by manipulating levels of individuation of the members of a set. In addition, we investigate the effect of morphophonological homophony onto the participants’ productions of agreeing forms. The findings are discussed in the context of three models of agreement (Marking & Morphing, competition and controller misidentification), which differ in the extent to which they allow the influence of non-syntactic factors on agreement. We also compare the behavioral findings with the predictions of four computational implementations of the Marking & Morphing account. We discuss the implications of the behavioral and computational findings for models of agreement and the language production more broadly. Rosemary: Some biscuits or a piece of cake… ‘goes’ or ‘go’ better with an afternoon tea? PMID:24039340

On Propagating Interpersonal Trust in Social Networks

NASA Astrophysics Data System (ADS)

Ziegler, Cai-Nicolas

The age of information glut has fostered the proliferation of data and documents on the Web, created by man and machine alike. Hence, there is an enormous wealth of minable knowledge that is yet to be extracted, in particular, on the Semantic Web. However, besides understanding information stated by subjects, knowing about their credibility becomes equally crucial. Hence, trust and trust metrics, conceived as computational means to evaluate trust relationships between individuals, come into play. Our major contribution to Semantic Web trust management through this work is twofold. First, we introduce a classification scheme for trust metrics along various axes and discuss advantages and drawbacks of existing approaches for Semantic Web scenarios. Hereby, we devise an advocacy for local group trust metrics, guiding us to the second part, which presents Appleseed, our novel proposal for local group trust computation. Compelling in its simplicity, Appleseed borrows many ideas from spreading activation models in psychology and relates their concepts to trust evaluation in an intuitive fashion. Moreover, we provide extensions for the Appleseed nucleus that make our trust metric handle distrust statements.

Hybrid ontology for semantic information retrieval model using keyword matching indexing system.

PubMed

Uthayan, K R; Mala, G S Anandha

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology.

Organizing Diverse, Distributed Project Information

NASA Technical Reports Server (NTRS)

Keller, Richard M.

2003-01-01

SemanticOrganizer is a software application designed to organize and integrate information generated within a distributed organization or as part of a project that involves multiple, geographically dispersed collaborators. SemanticOrganizer incorporates the capabilities of database storage, document sharing, hypermedia navigation, and semantic-interlinking into a system that can be customized to satisfy the specific information-management needs of different user communities. The program provides a centralized repository of information that is both secure and accessible to project collaborators via the World Wide Web. SemanticOrganizer's repository can be used to collect diverse information (including forms, documents, notes, data, spreadsheets, images, and sounds) from computers at collaborators work sites. The program organizes the information using a unique network-structured conceptual framework, wherein each node represents a data record that contains not only the original information but also metadata (in effect, standardized data that characterize the information). Links among nodes express semantic relationships among the data records. The program features a Web interface through which users enter, interlink, and/or search for information in the repository. By use of this repository, the collaborators have immediate access to the most recent project information, as well as to archived information. A key advantage to SemanticOrganizer is its ability to interlink information together in a natural fashion using customized terminology and concepts that are familiar to a user community.

Semantic Entity-Component State Management Techniques to Enhance Software Quality for Multimodal VR-Systems.

PubMed

Fischbach, Martin; Wiebusch, Dennis; Latoschik, Marc Erich

2017-04-01

Modularity, modifiability, reusability, and API usability are important software qualities that determine the maintainability of software architectures. Virtual, Augmented, and Mixed Reality (VR, AR, MR) systems, modern computer games, as well as interactive human-robot systems often include various dedicated input-, output-, and processing subsystems. These subsystems collectively maintain a real-time simulation of a coherent application state. The resulting interdependencies between individual state representations, mutual state access, overall synchronization, and flow of control implies a conceptual close coupling whereas software quality asks for a decoupling to develop maintainable solutions. This article presents five semantics-based software techniques that address this contradiction: Semantic grounding, code from semantics, grounded actions, semantic queries, and decoupling by semantics. These techniques are applied to extend the well-established entity-component-system (ECS) pattern to overcome some of this pattern's deficits with respect to the implied state access. A walk-through of central implementation aspects of a multimodal (speech and gesture) VR-interface is used to highlight the techniques' benefits. This use-case is chosen as a prototypical example of complex architectures with multiple interacting subsystems found in many VR, AR and MR architectures. Finally, implementation hints are given, lessons learned regarding maintainability pointed-out, and performance implications discussed.

Hybrid Ontology for Semantic Information Retrieval Model Using Keyword Matching Indexing System

PubMed Central

Uthayan, K. R.; Anandha Mala, G. S.

2015-01-01

Ontology is the process of growth and elucidation of concepts of an information domain being common for a group of users. Establishing ontology into information retrieval is a normal method to develop searching effects of relevant information users require. Keywords matching process with historical or information domain is significant in recent calculations for assisting the best match for specific input queries. This research presents a better querying mechanism for information retrieval which integrates the ontology queries with keyword search. The ontology-based query is changed into a primary order to predicate logic uncertainty which is used for routing the query to the appropriate servers. Matching algorithms characterize warm area of researches in computer science and artificial intelligence. In text matching, it is more dependable to study semantics model and query for conditions of semantic matching. This research develops the semantic matching results between input queries and information in ontology field. The contributed algorithm is a hybrid method that is based on matching extracted instances from the queries and information field. The queries and information domain is focused on semantic matching, to discover the best match and to progress the executive process. In conclusion, the hybrid ontology in semantic web is sufficient to retrieve the documents when compared to standard ontology. PMID:25922851

Semantic diversity: a measure of semantic ambiguity based on variability in the contextual usage of words.

PubMed

Hoffman, Paul; Lambon Ralph, Matthew A; Rogers, Timothy T

2013-09-01

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word's semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

Measuring content overlap during handoff communication using distributional semantics: An exploratory study.

PubMed

Abraham, Joanna; Kannampallil, Thomas G; Srinivasan, Vignesh; Galanter, William L; Tagney, Gail; Cohen, Trevor

2017-01-01

We develop and evaluate a methodological approach to measure the degree and nature of overlap in handoff communication content within and across clinical professions. This extensible, exploratory approach relies on combining techniques from conversational analysis and distributional semantics. We audio-recorded handoff communication of residents and nurses on the General Medicine floor of a large academic hospital (n=120 resident and n=120 nurse handoffs). We measured semantic similarity, a proxy for content overlap, between resident-resident and nurse-nurse communication using multiple steps: a qualitative conversational content analysis; an automated semantic similarity analysis using Reflective Random Indexing (RRI); and comparing semantic similarity generated by RRI analysis with human ratings of semantic similarity. There was significant association between the semantic similarity as computed by the RRI method and human rating (ρ=0.88). Based on the semantic similarity scores, content overlap was relatively higher for content related to patient active problems, assessment of active problems, patient-identifying information, past medical history, and medications/treatments. In contrast, content overlap was limited on content related to allergies, family-related information, code status, and anticipatory guidance. Our approach using RRI analysis provides new opportunities for characterizing the nature and degree of overlap in handoff communication. Although exploratory, this method provides a basis for identifying content that can be used for determining shared understanding across clinical professions. Additionally, this approach can inform the development of flexibly standardized handoff tools that reflect clinical content that are most appropriate for fostering shared understanding during transitions of care. Copyright © 2016 Elsevier Inc. All rights reserved.

Toward Agent Programs with Circuit Semantics

NASA Technical Reports Server (NTRS)

Nilsson, Nils J.

1992-01-01

New ideas are presented for computing and organizing actions for autonomous agents in dynamic environments-environments in which the agent's current situation cannot always be accurately discerned and in which the effects of actions cannot always be reliably predicted. The notion of 'circuit semantics' for programs based on 'teleo-reactive trees' is introduced. Program execution builds a combinational circuit which receives sensory inputs and controls actions. These formalisms embody a high degree of inherent conditionality and thus yield programs that are suitably reactive to their environments. At the same time, the actions computed by the programs are guided by the overall goals of the agent. The paper also speculates about how programs using these ideas could be automatically generated by artificial intelligence planning systems and adapted by learning methods.

Fully convolutional network with cluster for semantic segmentation

NASA Astrophysics Data System (ADS)

Ma, Xiao; Chen, Zhongbi; Zhang, Jianlin

2018-04-01

At present, image semantic segmentation technology has been an active research topic for scientists in the field of computer vision and artificial intelligence. Especially, the extensive research of deep neural network in image recognition greatly promotes the development of semantic segmentation. This paper puts forward a method based on fully convolutional network, by cluster algorithm k-means. The cluster algorithm using the image's low-level features and initializing the cluster centers by the super-pixel segmentation is proposed to correct the set of points with low reliability, which are mistakenly classified in great probability, by the set of points with high reliability in each clustering regions. This method refines the segmentation of the target contour and improves the accuracy of the image segmentation.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing.

PubMed

Grisoni, Luigi; Miller, Tally McCormick; Pulvermüller, Friedemann

2017-05-03

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system-in dorsolateral hand motor areas for expected hand-related words (e.g., "write"), but in ventral motor cortex for face-related words ("talk"). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words "lick" or "pick") and between affirmative and negated sentence meanings. Copyright © 2017 Grisoni et al.

Neural Correlates of Semantic Prediction and Resolution in Sentence Processing

PubMed Central

2017-01-01

Most brain-imaging studies of language comprehension focus on activity following meaningful stimuli. Testing adult human participants with high-density EEG, we show that, already before the presentation of a critical word, context-induced semantic predictions are reflected by a neurophysiological index, which we therefore call the semantic readiness potential (SRP). The SRP precedes critical words if a previous sentence context constrains the upcoming semantic content (high-constraint contexts), but not in unpredictable (low-constraint) contexts. Specific semantic predictions were indexed by SRP sources within the motor system—in dorsolateral hand motor areas for expected hand-related words (e.g., “write”), but in ventral motor cortex for face-related words (“talk”). Compared with affirmative sentences, negated ones led to medial prefrontal and more widespread motor source activation, the latter being consistent with predictive semantic computation of alternatives to the negated expected concept. Predictive processing of semantic alternatives in negated sentences is further supported by a negative-going event-related potential at ∼400 ms (N400), which showed the typical enhancement to semantically incongruent sentence endings only in high-constraint affirmative contexts, but not to high-constraint negated ones. These brain dynamics reveal the interplay between semantic prediction and resolution (match vs error) processing in sentence understanding. SIGNIFICANCE STATEMENT Most neuroscientists agree on the eminent importance of predictive mechanisms for understanding basic as well as higher brain functions. This contrasts with a sparseness of brain measures that directly reflects specific aspects of prediction, as they are relevant in the processing of language and thought. Here we show that when critical words are strongly expected in their sentence context, a predictive brain response reflects meaning features of these anticipated symbols already before they appear. The granularity of the semantic predictions was so fine grained that the cortical sources in sensorimotor and medial prefrontal cortex even distinguished between predicted face- or hand-related action words (e.g., the words “lick” or “pick”) and between affirmative and negated sentence meanings. PMID:28411271

Computer-Based and Paper-Based Measurement of Semantic Knowledge

DTIC Science & Technology

1989-01-01

of Personality Assessment , 34, 353-361. McArthur, D. L., & Choppin, B. H. (1984). Computerized diagnostic testing. Journal 15 of Educational...Computers in Human Behavior, 1, 49-58. Lushene, R. E., O’Neii, H. F., & Dunn, T. (1974). Equivalent validity of a completely computerized MMPI. Journal

Developing Expert Systems for the Analysis of Syntactic and Semantic Patterns.

ERIC Educational Resources Information Center

Hellwig, Harold H.

Noting that expert computer systems respond to various contexts in terms of knowledge representation, this paper explains that heuristic rules of production, procedural representation, and frame representation have been adapted to such areas as medical diagnosis, signal interpretation, design and planning of electrical circuits and computer system…

A Framework for the Specification of the Semantics and the Dynamics of Instructional Applications

ERIC Educational Resources Information Center

Buendia-Garcia, Felix; Diaz, Paloma

2003-01-01

An instructional application consists of a set of resources and activities to implement interacting, interrelated, and structured experiences oriented towards achieving specific educational objectives. The development of computer-based instructional applications has to follow a well defined process, so models for computer-based instructional…

Semantic relatedness for evaluation of course equivalencies

NASA Astrophysics Data System (ADS)

Yang, Beibei

Semantic relatedness, or its inverse, semantic distance, measures the degree of closeness between two pieces of text determined by their meaning. Related work typically measures semantics based on a sparse knowledge base such as WordNet or Cyc that requires intensive manual efforts to build and maintain. Other work is based on a corpus such as the Brown corpus, or more recently, Wikipedia. This dissertation proposes two approaches to applying semantic relatedness to the problem of suggesting transfer course equivalencies. Two course descriptions are given as input to feed the proposed algorithms, which output a value that can be used to help determine if the courses are equivalent. The first proposed approach uses traditional knowledge sources such as WordNet and corpora for courses from multiple fields of study. The second approach uses Wikipedia, the openly-editable encyclopedia, and it focuses on courses from a technical field such as Computer Science. This work shows that it is promising to adapt semantic relatedness to the education field for matching equivalencies between transfer courses. A semantic relatedness measure using traditional knowledge sources such as WordNet performs relatively well on non-technical courses. However, due to the "knowledge acquisition bottleneck," such a resource is not ideal for technical courses, which use an extensive and growing set of technical terms. To address the problem, this work proposes a Wikipedia-based approach which is later shown to be more correlated to human judgment compared to previous work.

Convergence of Health Level Seven Version 2 Messages to Semantic Web Technologies for Software-Intensive Systems in Telemedicine Trauma Care

PubMed Central

Cook, Timothy Wayne; Cavalini, Luciana Tricai

2016-01-01

Objectives To present the technical background and the development of a procedure that enriches the semantics of Health Level Seven version 2 (HL7v2) messages for software-intensive systems in telemedicine trauma care. Methods This study followed a multilevel model-driven approach for the development of semantically interoperable health information systems. The Pre-Hospital Trauma Life Support (PHTLS) ABCDE protocol was adopted as the use case. A prototype application embedded the semantics into an HL7v2 message as an eXtensible Markup Language (XML) file, which was validated against an XML schema that defines constraints on a common reference model. This message was exchanged with a second prototype application, developed on the Mirth middleware, which was also used to parse and validate both the original and the hybrid messages. Results Both versions of the data instance (one pure XML, one embedded in the HL7v2 message) were equally validated and the RDF-based semantics recovered by the receiving side of the prototype from the shared XML schema. Conclusions This study demonstrated the semantic enrichment of HL7v2 messages for intensive-software telemedicine systems for trauma care, by validating components of extracts generated in various computing environments. The adoption of the method proposed in this study ensures the compliance of the HL7v2 standard in Semantic Web technologies. PMID:26893947

Towards a Framework for Developing Semantic Relatedness Reference Standards

PubMed Central

Pakhomov, Serguei V.S.; Pedersen, Ted; McInnes, Bridget; Melton, Genevieve B.; Ruggieri, Alexander; Chute, Christopher G.

2010-01-01

Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the “moderate” range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development. PMID:21044697

Laboratory for Computer Science Progress Report 16, 1 July 1978 - 30 June 1979,

DTIC Science & Technology

1980-08-01

name strongly distinguishes the XLMS node from ordinary nameless semantic network nodes. The name of a node has two parts: the " genus ", itself a node...and the "specializer", a node or an atomic symbol. The genus and specializer of a node are almost always semantically meaningful, though their...meaning is almost never suppliec by XLMS, but rather by some system built on top of XLMS. The genus of a node almost always plays a crucial role in its

A data base processor semantics specification package

NASA Technical Reports Server (NTRS)

Fishwick, P. A.

1983-01-01

A Semantics Specification Package (DBPSSP) for the Intel Data Base Processor (DBP) is defined. DBPSSP serves as a collection of cross assembly tools that allow the analyst to assemble request blocks on the host computer for passage to the DBP. The assembly tools discussed in this report may be effectively used in conjunction with a DBP compatible data communications protocol to form a query processor, precompiler, or file management system for the database processor. The source modules representing the components of DBPSSP are fully commented and included.

Simplifying the Reuse and Interoperability of Geoscience Data Sets and Models with Semantic Metadata that is Human-Readable and Machine-actionable

NASA Astrophysics Data System (ADS)

Peckham, S. D.

2017-12-01

Standardized, deep descriptions of digital resources (e.g. data sets, computational models, software tools and publications) make it possible to develop user-friendly software systems that assist scientists with the discovery and appropriate use of these resources. Semantic metadata makes it possible for machines to take actions on behalf of humans, such as automatically identifying the resources needed to solve a given problem, retrieving them and then automatically connecting them (despite their heterogeneity) into a functioning workflow. Standardized model metadata also helps model users to understand the important details that underpin computational models and to compare the capabilities of different models. These details include simplifying assumptions on the physics, governing equations and the numerical methods used to solve them, discretization of space (the grid) and time (the time-stepping scheme), state variables (input or output), model configuration parameters. This kind of metadata provides a "deep description" of a computational model that goes well beyond other types of metadata (e.g. author, purpose, scientific domain, programming language, digital rights, provenance, execution) and captures the science that underpins a model. A carefully constructed, unambiguous and rules-based schema to address this problem, called the Geoscience Standard Names ontology will be presented that utilizes Semantic Web best practices and technologies. It has also been designed to work across science domains and to be readable by both humans and machines.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

The interpretation of dream meaning: Resolving ambiguity using Latent Semantic Analysis in a small corpus of text.

PubMed

Altszyler, Edgar; Ribeiro, Sidarta; Sigman, Mariano; Fernández Slezak, Diego

2017-11-01

Computer-based dreams content analysis relies on word frequencies within predefined categories in order to identify different elements in text. As a complementary approach, we explored the capabilities and limitations of word-embedding techniques to identify word usage patterns among dream reports. These tools allow us to quantify words associations in text and to identify the meaning of target words. Word-embeddings have been extensively studied in large datasets, but only a few studies analyze semantic representations in small corpora. To fill this gap, we compared Skip-gram and Latent Semantic Analysis (LSA) capabilities to extract semantic associations from dream reports. LSA showed better performance than Skip-gram in small size corpora in two tests. Furthermore, LSA captured relevant word associations in dream collection, even in cases with low-frequency words or small numbers of dreams. Word associations in dreams reports can thus be quantified by LSA, which opens new avenues for dream interpretation and decoding. Copyright © 2017 Elsevier Inc. All rights reserved.

CASAS: A tool for composing automatically and semantically astrophysical services

NASA Astrophysics Data System (ADS)

Louge, T.; Karray, M. H.; Archimède, B.; Knödlseder, J.

2017-07-01

Multiple astronomical datasets are available through internet and the astrophysical Distributed Computing Infrastructure (DCI) called Virtual Observatory (VO). Some scientific workflow technologies exist for retrieving and combining data from those sources. However selection of relevant services, automation of the workflows composition and the lack of user-friendly platforms remain a concern. This paper presents CASAS, a tool for semantic web services composition in astrophysics. This tool proposes automatic composition of astrophysical web services and brings a semantics-based, automatic composition of workflows. It widens the services choice and eases the use of heterogeneous services. Semantic web services composition relies on ontologies for elaborating the services composition; this work is based on Astrophysical Services ONtology (ASON). ASON had its structure mostly inherited from the VO services capacities. Nevertheless, our approach is not limited to the VO and brings VO plus non-VO services together without the need for premade recipes. CASAS is available for use through a simple web interface.

Towards a semantic PACS: Using Semantic Web technology to represent imaging data.

PubMed

Van Soest, Johan; Lustberg, Tim; Grittner, Detlef; Marshall, M Scott; Persoon, Lucas; Nijsten, Bas; Feltens, Peter; Dekker, Andre

2014-01-01

The DICOM standard is ubiquitous within medicine. However, improved DICOM semantics would significantly enhance search operations. Furthermore, databases of current PACS systems are not flexible enough for the demands within image analysis research. In this paper, we investigated if we can use Semantic Web technology, to store and represent metadata of DICOM image files, as well as linking additional computational results to image metadata. Therefore, we developed a proof of concept containing two applications: one to store commonly used DICOM metadata in an RDF repository, and one to calculate imaging biomarkers based on DICOM images, and store the biomarker values in an RDF repository. This enabled us to search for all patients with a gross tumor volume calculated to be larger than 50 cc. We have shown that we can successfully store the DICOM metadata in an RDF repository and are refining our proof of concept with regards to volume naming, value representation, and the applications themselves.

Using semantic technologies and the OSU ontology for modelling context and activities in multi-sensory surveillance systems

NASA Astrophysics Data System (ADS)

Gómez A, Héctor F.; Martínez-Tomás, Rafael; Arias Tapia, Susana A.; Rincón Zamorano, Mariano

2014-04-01

Automatic systems that monitor human behaviour for detecting security problems are a challenge today. Previously, our group defined the Horus framework, which is a modular architecture for the integration of multi-sensor monitoring stages. In this work, structure and technologies required for high-level semantic stages of Horus are proposed, and the associated methodological principles established with the aim of recognising specific behaviours and situations. Our methodology distinguishes three semantic levels of events: low level (compromised with sensors), medium level (compromised with context), and high level (target behaviours). The ontology for surveillance and ubiquitous computing has been used to integrate ontologies from specific domains and together with semantic technologies have facilitated the modelling and implementation of scenes and situations by reusing components. A home context and a supermarket context were modelled following this approach, where three suspicious activities were monitored via different virtual sensors. The experiments demonstrate that our proposals facilitate the rapid prototyping of this kind of systems.

Working memory training and semantic structuring improves remembering future events, not past events.

PubMed

Richter, Kim Merle; Mödden, Claudia; Eling, Paul; Hildebrandt, Helmut

2015-01-01

Objectives. Memory training in combination with practice in semantic structuring and word fluency has been shown to improve memory performance. This study investigated the efficacy of a working memory training combined with exercises in semantic structuring and word fluency and examined whether training effects generalize to other cognitive tasks. Methods. In this double-blind randomized control study, 36 patients with memory impairments following brain damage were allocated to either the experimental or the active control condition, with both groups receiving 9 hours of therapy. The experimental group received a computer-based working memory training and exercises in word fluency and semantic structuring. The control group received the standard memory therapy provided in the rehabilitation center. Patients were tested on a neuropsychological test battery before and after therapy, resulting in composite scores for working memory; immediate, delayed, and prospective memory; word fluency; and attention. Results. The experimental group improved significantly in working memory and word fluency. The training effects also generalized to prospective memory tasks. No specific effect on episodic memory could be demonstrated. Conclusion. Combined treatment of working memory training with exercises in semantic structuring is an effective method for cognitive rehabilitation of organic memory impairment. © The Author(s) 2014.

A-DaGO-Fun: an adaptable Gene Ontology semantic similarity-based functional analysis tool.

PubMed

Mazandu, Gaston K; Chimusa, Emile R; Mbiyavanga, Mamana; Mulder, Nicola J

2016-02-01

Gene Ontology (GO) semantic similarity measures are being used for biological knowledge discovery based on GO annotations by integrating biological information contained in the GO structure into data analyses. To empower users to quickly compute, manipulate and explore these measures, we introduce A-DaGO-Fun (ADaptable Gene Ontology semantic similarity-based Functional analysis). It is a portable software package integrating all known GO information content-based semantic similarity measures and relevant biological applications associated with these measures. A-DaGO-Fun has the advantage not only of handling datasets from the current high-throughput genome-wide applications, but also allowing users to choose the most relevant semantic similarity approach for their biological applications and to adapt a given module to their needs. A-DaGO-Fun is freely available to the research community at http://web.cbio.uct.ac.za/ITGOM/adagofun. It is implemented in Linux using Python under free software (GNU General Public Licence). gmazandu@cbio.uct.ac.za or Nicola.Mulder@uct.ac.za Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

A Drone Remote Sensing for Virtual Reality Simulation System for Forest Fires: Semantic Neural Network Approach

NASA Astrophysics Data System (ADS)

Narasimha Rao, Gudikandhula; Jagadeeswara Rao, Peddada; Duvvuru, Rajesh

2016-09-01

Wild fires have significant impact on atmosphere and lives. The demand of predicting exact fire area in forest may help fire management team by using drone as a robot. These are flexible, inexpensive and elevated-motion remote sensing systems that use drones as platforms are important for substantial data gaps and supplementing the capabilities of manned aircraft and satellite remote sensing systems. In addition, powerful computational tools are essential for predicting certain burned area in the duration of a forest fire. The reason of this study is to built up a smart system based on semantic neural networking for the forecast of burned areas. The usage of virtual reality simulator is used to support the instruction process of fire fighters and all users for saving of surrounded wild lives by using a naive method Semantic Neural Network System (SNNS). Semantics are valuable initially to have a enhanced representation of the burned area prediction and better alteration of simulation situation to the users. In meticulous, consequences obtained with geometric semantic neural networking is extensively superior to other methods. This learning suggests that deeper investigation of neural networking in the field of forest fires prediction could be productive.

Knowledge Provenance in Semantic Wikis

NASA Astrophysics Data System (ADS)

Ding, L.; Bao, J.; McGuinness, D. L.

2008-12-01

Collaborative online environments with a technical Wiki infrastructure are becoming more widespread. One of the strengths of a Wiki environment is that it is relatively easy for numerous users to contribute original content and modify existing content (potentially originally generated by others). As more users begin to depend on informational content that is evolving by Wiki communities, it becomes more important to track the provenance of the information. Semantic Wikis expand upon traditional Wiki environments by adding some computationally understandable encodings of some of the terms and relationships in Wikis. We have developed a semantic Wiki environment that expands a semantic Wiki with provenance markup. Provenance of original contributions as well as modifications is encoded using the provenance markup component of the Proof Markup Language. The Wiki environment provides the provenance markup automatically, thus users are not required to make specific encodings of author, contribution date, and modification trail. Further, our Wiki environment includes a search component that understands the provenance primitives and thus can be used to provide a provenance-aware search facility. We will describe the knowledge provenance infrastructure of our Semantic Wiki and show how it is being used as the foundation of our group web site as well as a number of project web sites.

The role of lexical variables in the visual recognition of Chinese characters: A megastudy analysis.

PubMed

Sze, Wei Ping; Yap, Melvin J; Rickard Liow, Susan J

2015-01-01

Logographic Chinese orthography partially represents both phonology and semantics. By capturing the online processing of a large pool of Chinese characters, we were able to examine the relative salience of specific lexical variables when this nonalphabetic script is read. Using a sample of native mainland Chinese speakers (N = 35), lexical decision latencies for 1560 single characters were collated into a database, before the effects of a comprehensive range of variables were explored. Hierarchical regression analyses determined the unique item-level variance explained by orthographic (frequency, stroke count), semantic (age of learning, imageability, number of meanings), and phonological (consistency, phonological frequency) factors. Orthographic and semantic variables, respectively, accounted for more collective variance than the phonological variables. Significant main effects were further observed for the individual orthographic and semantic predictors. These results are consistent with the idea that skilled readers tend to rely on orthographic and semantic information when processing visually presented characters. This megastudy approach marks an important extension to existing work on Chinese character recognition, which hitherto has relied on factorial designs. Collectively, the findings reported here represent a useful set of empirical constraints for future computational models of character recognition.

Optics and Symbolic Computing

DTIC Science & Technology

1987-03-01

applicatior for AI are in variation of classification parameters for knowledge acquisition ( changing of classes into which objects are placed), and...computation. The well-structured data formats of vectors, matrices, etc. used in numeric computing give way to data structures that can change their shapes...by "flexible data structures. The semantic meanings of objects are readily changed by adding and deleting the variable lists of attributes. Another

Template construction grammar: from visual scene description to language comprehension and agrammatism.

PubMed

Barrès, Victor; Lee, Jinyong

2014-01-01

How does the language system coordinate with our visual system to yield flexible integration of linguistic, perceptual, and world-knowledge information when we communicate about the world we perceive? Schema theory is a computational framework that allows the simulation of perceptuo-motor coordination programs on the basis of known brain operating principles such as cooperative computation and distributed processing. We present first its application to a model of language production, SemRep/TCG, which combines a semantic representation of visual scenes (SemRep) with Template Construction Grammar (TCG) as a means to generate verbal descriptions of a scene from its associated SemRep graph. SemRep/TCG combines the neurocomputational framework of schema theory with the representational format of construction grammar in a model linking eye-tracking data to visual scene descriptions. We then offer a conceptual extension of TCG to include language comprehension and address data on the role of both world knowledge and grammatical semantics in the comprehension performances of agrammatic aphasic patients. This extension introduces a distinction between heavy and light semantics. The TCG model of language comprehension offers a computational framework to quantitatively analyze the distributed dynamics of language processes, focusing on the interactions between grammatical, world knowledge, and visual information. In particular, it reveals interesting implications for the understanding of the various patterns of comprehension performances of agrammatic aphasics measured using sentence-picture matching tasks. This new step in the life cycle of the model serves as a basis for exploring the specific challenges that neurolinguistic computational modeling poses to the neuroinformatics community.

Automated Inspection of Power Line Corridors to Measure Vegetation Undercut Using Uav-Based Images

NASA Astrophysics Data System (ADS)

Maurer, M.; Hofer, M.; Fraundorfer, F.; Bischof, H.

2017-08-01

Power line corridor inspection is a time consuming task that is performed mostly manually. As the development of UAVs made huge progress in recent years, and photogrammetric computer vision systems became well established, it is time to further automate inspection tasks. In this paper we present an automated processing pipeline to inspect vegetation undercuts of power line corridors. For this, the area of inspection is reconstructed, geo-referenced, semantically segmented and inter class distance measurements are calculated. The presented pipeline performs an automated selection of the proper 3D reconstruction method for on the one hand wiry (power line), and on the other hand solid objects (surrounding). The automated selection is realized by performing pixel-wise semantic segmentation of the input images using a Fully Convolutional Neural Network. Due to the geo-referenced semantic 3D reconstructions a documentation of areas where maintenance work has to be performed is inherently included in the distance measurements and can be extracted easily. We evaluate the influence of the semantic segmentation according to the 3D reconstruction and show that the automated semantic separation in wiry and dense objects of the 3D reconstruction routine improves the quality of the vegetation undercut inspection. We show the generalization of the semantic segmentation to datasets acquired using different acquisition routines and to varied seasons in time.

HyQue: evaluating hypotheses using Semantic Web technologies.

PubMed

Callahan, Alison; Dumontier, Michel; Shah, Nigam H

2011-05-17

Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks. We present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF. HyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque.

Developing Online Learning Resources: Big Data, Social Networks, and Cloud Computing to Support Pervasive Knowledge

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2016-01-01

Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of…

Computational Psycholinguistic Analysis and Its Application in Psychological Assessment of College Students

ERIC Educational Resources Information Center

Kucera, Dalibor; Havigerová, Jana M.

2015-01-01

The paper deals with the issue of computational psycholinguistic analysis (CPA) and its experimental application in basic psychological and pedagogical assessment. CPA is a new method which may potentially provide interesting, psychologically relevant information about the author of a particular text, regardless of the text's factual (semantic)…

The large-scale structure of software-intensive systems

PubMed Central

Booch, Grady

2012-01-01

The computer metaphor is dominant in most discussions of neuroscience, but the semantics attached to that metaphor are often quite naive. Herein, we examine the ontology of software-intensive systems, the nature of their structure and the application of the computer metaphor to the metaphysical questions of self and causation. PMID:23386964

Global Journal of Computer Science and Technology. Volume 1.2

ERIC Educational Resources Information Center

Dixit, R. K.

2009-01-01

Articles in this issue of "Global Journal of Computer Science and Technology" include: (1) Input Data Processing Techniques in Intrusion Detection Systems--Short Review (Suhair H. Amer and John A. Hamilton, Jr.); (2) Semantic Annotation of Stock Photography for CBIR Using MPEG-7 standards (R. Balasubramani and V. Kannan); (3) An Experimental Study…

Gestural cue analysis in automated semantic miscommunication annotation

PubMed Central

Inoue, Masashi; Ogihara, Mitsunori; Hanada, Ryoko; Furuyama, Nobuhiro

2011-01-01

The automated annotation of conversational video by semantic miscommunication labels is a challenging topic. Although miscommunications are often obvious to the speakers as well as the observers, it is difficult for machines to detect them from the low-level features. We investigate the utility of gestural cues in this paper among various non-verbal features. Compared with gesture recognition tasks in human-computer interaction, this process is difficult due to the lack of understanding on which cues contribute to miscommunications and the implicitness of gestures. Nine simple gestural features are taken from gesture data, and both simple and complex classifiers are constructed using machine learning. The experimental results suggest that there is no single gestural feature that can predict or explain the occurrence of semantic miscommunication in our setting. PMID:23585724

Synergetic computer and holonics - information dynamics of a semantic computer

NASA Astrophysics Data System (ADS)

Shimizu, H.; Yamaguchi, Y.

1987-12-01

The dynamics of semantic information in biosystem is studied based on holons, generators of mutual relations. Any biosystem has an internal world, a so-called "self", which has an intrinsic purpose rendering the system continuously alive and developed as much as possible against a fluctuating external world. External signals to the system through sensory organs are classified by the self into two basic categories, semantic information with some meaning and value for the purpose and inputs from background and noise sources. Due to this breaking of semantic symmetry, any input signals are transformed into a figure and background, respectively. As a typical example, the visual perception of vertebrates is studied. For such semantic transformation the external signal is first decomposed and converted into a number of elementary signs named "syntons" which are then transmitted into a sensory area of cortex corresponding to an image synthesizer. The synthesizer is a sort of autonomic parallel processor composed of autonomic units, "holons", which are characterized by many internal modes. Syntons are fed into the holons one by one. A set of the elementary meanings, the so-called "semons", provided to the synton are encoded in the internal modes of the holon; that is, each internal mode encodes a semon. A dynamic information theory for the transformation of external signals to semantic information is developed based on our model which we call holovision. Holovision is a dynamic model of visual perception that processes an autonomic ability to self-organize visual images. Autonomous oscillators are utilized as the line processors to encode line elements with specific orientations in their phases as semons. An information space is defined according to the assembly of holons; the spatial plane on which holons are arranged is a syntactic subspace while the internal modes of the holons span a semantic subspace in the orthogonal direction. In this information space, the image of a figure is self-organized - as a sort of spatiotemporal pattern - by autonomic coordinations of the holons that select relevant internal modes, accompanied with compression of irrelevant syntons that correspond to the background. Holons coded by a synton are relevantly connected by means of coherent relations, i.e., dynamic connections with time-coherence, in order to represent the image that varies in time depending on the instantaneous state of the external object. These connections depend on the internal modes that are cooperatively selectively selected by the holons. The image is regarded as a bridge between the external and internal world that has both external and internal consistency. The meaning of the image, i.e., transformed semantic information, is spontaneously transferred from semantic items that have a coherent relation with the image, and the external signal is perceived by the self through the image. We demonstrate that images are indeed self-organized in holovision in the previously described sense. Simulated processes of the creation of semantic information in holovision are shown to display typical features of the forgoing steps of information compression. Based on these results, we propose quantitative indices that represent the value of semantic information in the image processor as well as in the memory.

A method of extracting ontology module using concept relations for sharing knowledge in mobile cloud computing environment.

PubMed

Lee, Keonsoo; Rho, Seungmin; Lee, Seok-Won

2014-01-01

In mobile cloud computing environment, the cooperation of distributed computing objects is one of the most important requirements for providing successful cloud services. To satisfy this requirement, all the members, who are employed in the cooperation group, need to share the knowledge for mutual understanding. Even if ontology can be the right tool for this goal, there are several issues to make a right ontology. As the cost and complexity of managing knowledge increase according to the scale of the knowledge, reducing the size of ontology is one of the critical issues. In this paper, we propose a method of extracting ontology module to increase the utility of knowledge. For the given signature, this method extracts the ontology module, which is semantically self-contained to fulfill the needs of the service, by considering the syntactic structure and semantic relation of concepts. By employing this module, instead of the original ontology, the cooperation of computing objects can be performed with less computing load and complexity. In particular, when multiple external ontologies need to be combined for more complex services, this method can be used to optimize the size of shared knowledge.

A Semantic Transformation Methodology for the Secondary Use of Observational Healthcare Data in Postmarketing Safety Studies.

PubMed

Pacaci, Anil; Gonul, Suat; Sinaci, A Anil; Yuksel, Mustafa; Laleci Erturkmen, Gokce B

2018-01-01

Background: Utilization of the available observational healthcare datasets is key to complement and strengthen the postmarketing safety studies. Use of common data models (CDM) is the predominant approach in order to enable large scale systematic analyses on disparate data models and vocabularies. Current CDM transformation practices depend on proprietarily developed Extract-Transform-Load (ETL) procedures, which require knowledge both on the semantics and technical characteristics of the source datasets and target CDM. Purpose: In this study, our aim is to develop a modular but coordinated transformation approach in order to separate semantic and technical steps of transformation processes, which do not have a strict separation in traditional ETL approaches. Such an approach would discretize the operations to extract data from source electronic health record systems, alignment of the source, and target models on the semantic level and the operations to populate target common data repositories. Approach: In order to separate the activities that are required to transform heterogeneous data sources to a target CDM, we introduce a semantic transformation approach composed of three steps: (1) transformation of source datasets to Resource Description Framework (RDF) format, (2) application of semantic conversion rules to get the data as instances of ontological model of the target CDM, and (3) population of repositories, which comply with the specifications of the CDM, by processing the RDF instances from step 2. The proposed approach has been implemented on real healthcare settings where Observational Medical Outcomes Partnership (OMOP) CDM has been chosen as the common data model and a comprehensive comparative analysis between the native and transformed data has been conducted. Results: Health records of ~1 million patients have been successfully transformed to an OMOP CDM based database from the source database. Descriptive statistics obtained from the source and target databases present analogous and consistent results. Discussion and Conclusion: Our method goes beyond the traditional ETL approaches by being more declarative and rigorous. Declarative because the use of RDF based mapping rules makes each mapping more transparent and understandable to humans while retaining logic-based computability. Rigorous because the mappings would be based on computer readable semantics which are amenable to validation through logic-based inference methods.

From ontology selection and semantic web to an integrated information system for food-borne diseases and food safety.

PubMed

Yan, Xianghe; Peng, Yun; Meng, Jianghong; Ruzante, Juliana; Fratamico, Pina M; Huang, Lihan; Juneja, Vijay; Needleman, David S

2011-01-01

Several factors have hindered effective use of information and resources related to food safety due to inconsistency among semantically heterogeneous data resources, lack of knowledge on profiling of food-borne pathogens, and knowledge gaps among research communities, government risk assessors/managers, and end-users of the information. This paper discusses technical aspects in the establishment of a comprehensive food safety information system consisting of the following steps: (a) computational collection and compiling publicly available information, including published pathogen genomic, proteomic, and metabolomic data; (b) development of ontology libraries on food-borne pathogens and design automatic algorithms with formal inference and fuzzy and probabilistic reasoning to address the consistency and accuracy of distributed information resources (e.g., PulseNet, FoodNet, OutbreakNet, PubMed, NCBI, EMBL, and other online genetic databases and information); (c) integration of collected pathogen profiling data, Foodrisk.org ( http://www.foodrisk.org ), PMP, Combase, and other relevant information into a user-friendly, searchable, "homogeneous" information system available to scientists in academia, the food industry, and government agencies; and (d) development of a computational model in semantic web for greater adaptability and robustness.

Towards a framework for developing semantic relatedness reference standards.

PubMed

Pakhomov, Serguei V S; Pedersen, Ted; McInnes, Bridget; Melton, Genevieve B; Ruggieri, Alexander; Chute, Christopher G

2011-04-01

Our objective is to develop a framework for creating reference standards for functional testing of computerized measures of semantic relatedness. Currently, research on computerized approaches to semantic relatedness between biomedical concepts relies on reference standards created for specific purposes using a variety of methods for their analysis. In most cases, these reference standards are not publicly available and the published information provided in manuscripts that evaluate computerized semantic relatedness measurement approaches is not sufficient to reproduce the results. Our proposed framework is based on the experiences of medical informatics and computational linguistics communities and addresses practical and theoretical issues with creating reference standards for semantic relatedness. We demonstrate the use of the framework on a pilot set of 101 medical term pairs rated for semantic relatedness by 13 medical coding experts. While the reliability of this particular reference standard is in the "moderate" range; we show that using clustering and factor analyses offers a data-driven approach to finding systematic differences among raters and identifying groups of potential outliers. We test two ontology-based measures of relatedness and provide both the reference standard containing individual ratings and the R program used to analyze the ratings as open-source. Currently, these resources are intended to be used to reproduce and compare results of studies involving computerized measures of semantic relatedness. Our framework may be extended to the development of reference standards in other research areas in medical informatics including automatic classification, information retrieval from medical records and vocabulary/ontology development. Copyright © 2010 Elsevier Inc. All rights reserved.

Fuzzy Versions of Epistemic and Deontic Logic

NASA Technical Reports Server (NTRS)

Gounder, Ramasamy S.; Esterline, Albert C.

1998-01-01

Epistemic and deontic logics are modal logics, respectively, of knowledge and of the normative concepts of obligation, permission, and prohibition. Epistemic logic is useful in formalizing systems of communicating processes and knowledge and belief in AI (Artificial Intelligence). Deontic logic is useful in computer science wherever we must distinguish between actual and ideal behavior, as in fault tolerance and database integrity constraints. We here discuss fuzzy versions of these logics. In the crisp versions, various axioms correspond to various properties of the structures used in defining the semantics of the logics. Thus, any axiomatic theory will be characterized not only by its axioms but also by the set of properties holding of the corresponding semantic structures. Fuzzy logic does not proceed with axiomatic systems, but fuzzy versions of the semantic properties exist and can be shown to correspond to some of the axioms for the crisp systems in special ways that support dependency networks among assertions in a modal domain. This in turn allows one to implement truth maintenance systems. For the technical development of epistemic logic, and for that of deontic logic. To our knowledge, we are the first to address fuzzy epistemic and fuzzy deontic logic explicitly and to consider the different systems and semantic properties available. We give the syntax and semantics of epistemic logic and discuss the correspondence between axioms of epistemic logic and properties of semantic structures. The same topics are covered for deontic logic. Fuzzy epistemic and fuzzy deontic logic discusses the relationship between axioms and semantic properties for these logics. Our results can be exploited in truth maintenance systems.

Characterizing Cognitive Performance in a Large Longitudinal Study of Aging with Computerized Semantic Indices of Verbal Fluency

PubMed Central

Pakhomov, Serguei VS; Eberly, Lynn; Knopman, David

2016-01-01

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia. PMID:27245645

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms.

PubMed

Papež, Václav; Denaxas, Spiros; Hemingway, Harry

2017-01-01

Electronic Health Records are electronic data generated during or as a byproduct of routine patient care. Structured, semi-structured and unstructured EHR offer researchers unprecedented phenotypic breadth and depth and have the potential to accelerate the development of precision medicine approaches at scale. A main EHR use-case is defining phenotyping algorithms that identify disease status, onset and severity. Phenotyping algorithms utilize diagnoses, prescriptions, laboratory tests, symptoms and other elements in order to identify patients with or without a specific trait. No common standardized, structured, computable format exists for storing phenotyping algorithms. The majority of algorithms are stored as human-readable descriptive text documents making their translation to code challenging due to their inherent complexity and hinders their sharing and re-use across the community. In this paper, we evaluate the two key Semantic Web Technologies, the Web Ontology Language and the Resource Description Framework, for enabling computable representations of EHR-driven phenotyping algorithms.

Introspections on the Semantic Gap

DTIC Science & Technology

2015-04-14

cloud comput - ing. Zhang received an MS in computer science from Stony Brook University. Contact him at dozhang@ cs.stonybrook.edu. Donald E. Porter...designated by other documentation. ... 2 March/April 2015 Copublished by the IEEE Computer and Reliability Societies 1540-7993/15/$31.00 © 2015 IEEE IEEE S...pauses the VM, and the VMI tool introspects the process descriptor list. In contrast, an asynchronous mechanism would intro - spect memory

CityGML - Interoperable semantic 3D city models

NASA Astrophysics Data System (ADS)

Gröger, Gerhard; Plümer, Lutz

2012-07-01

CityGML is the international standard of the Open Geospatial Consortium (OGC) for the representation and exchange of 3D city models. It defines the three-dimensional geometry, topology, semantics and appearance of the most relevant topographic objects in urban or regional contexts. These definitions are provided in different, well-defined Levels-of-Detail (multiresolution model). The focus of CityGML is on the semantical aspects of 3D city models, its structures, taxonomies and aggregations, allowing users to employ virtual 3D city models for advanced analysis and visualization tasks in a variety of application domains such as urban planning, indoor/outdoor pedestrian navigation, environmental simulations, cultural heritage, or facility management. This is in contrast to purely geometrical/graphical models such as KML, VRML, or X3D, which do not provide sufficient semantics. CityGML is based on the Geography Markup Language (GML), which provides a standardized geometry model. Due to this model and its well-defined semantics and structures, CityGML facilitates interoperable data exchange in the context of geo web services and spatial data infrastructures. Since its standardization in 2008, CityGML has become used on a worldwide scale: tools from notable companies in the geospatial field provide CityGML interfaces. Many applications and projects use this standard. CityGML is also having a strong impact on science: numerous approaches use CityGML, particularly its semantics, for disaster management, emergency responses, or energy-related applications as well as for visualizations, or they contribute to CityGML, improving its consistency and validity, or use CityGML, particularly its different Levels-of-Detail, as a source or target for generalizations. This paper gives an overview of CityGML, its underlying concepts, its Levels-of-Detail, how to extend it, its applications, its likely future development, and the role it plays in scientific research. Furthermore, its relationship to other standards from the fields of computer graphics and computer-aided architectural design and to the prospective INSPIRE model are discussed, as well as the impact CityGML has and is having on the software industry, on applications of 3D city models, and on science generally.

Using the Unified Modelling Language (UML) to guide the systemic description of biological processes and systems.

PubMed

Roux-Rouquié, Magali; Caritey, Nicolas; Gaubert, Laurent; Rosenthal-Sabroux, Camille

2004-07-01

One of the main issues in Systems Biology is to deal with semantic data integration. Previously, we examined the requirements for a reference conceptual model to guide semantic integration based on the systemic principles. In the present paper, we examine the usefulness of the Unified Modelling Language (UML) to describe and specify biological systems and processes. This makes unambiguous representations of biological systems, which would be suitable for translation into mathematical and computational formalisms, enabling analysis, simulation and prediction of these systems behaviours.

Programming with process groups: Group and multicast semantics

NASA Technical Reports Server (NTRS)

Birman, Kenneth P.; Cooper, Robert; Gleeson, Barry

1991-01-01

Process groups are a natural tool for distributed programming and are increasingly important in distributed computing environments. Discussed here is a new architecture that arose from an effort to simplify Isis process group semantics. The findings include a refined notion of how the clients of a group should be treated, what the properties of a multicast primitive should be when systems contain large numbers of overlapping groups, and a new construct called the causality domain. A system based on this architecture is now being implemented in collaboration with the Chorus and Mach projects.

Practical solutions to implementing "Born Semantic" data systems

NASA Astrophysics Data System (ADS)

Leadbetter, A.; Buck, J. J. H.; Stacey, P.

2015-12-01

The concept of data being "Born Semantic" has been proposed in recent years as a Semantic Web analogue to the idea of data being "born digital"[1], [2]. Within the "Born Semantic" concept, data are captured digitally and at a point close to the time of creation are annotated with markup terms from semantic web resources (controlled vocabularies, thesauri or ontologies). This allows heterogeneous data to be more easily ingested and amalgamated in near real-time due to the standards compliant annotation of the data. In taking the "Born Semantic" proposal from concept to operation, a number of difficulties have been encountered. For example, although there are recognised methods such as Header, Dictionary, Triples [3] for the compression, publication and dissemination of large volumes of triples these systems are not practical to deploy in the field on low-powered (both electrically and computationally) devices. Similarly, it is not practical for instruments to output fully formed semantically annotated data files if they are designed to be plugged into a modular system and the data to be centrally logged in the field as is the case on Argo floats and oceanographic gliders where internal bandwidth becomes an issue [2]. In light of these issues, this presentation will concentrate on pragmatic solutions being developed to the problem of generating Linked Data in near real-time systems. Specific examples from the European Commission SenseOCEAN project where Linked Data systems are being developed for autonomous underwater platforms, and from work being undertaken in the streaming of data from the Irish Galway Bay Cable Observatory initiative will be highlighted. Further, developments of a set of tools for the LogStash-ElasticSearch software ecosystem to allow the storing and retrieval of Linked Data will be introduced. References[1] A. Leadbetter & J. Fredericks, We have "born digital" - now what about "born semantic"?, European Geophysical Union General Assembly, 2014.[2] J. Buck & A. Leadbetter, Born semantic: linking data from sensors to users and balancing hardware limitations with data standards, European Geophysical Union General Assembly, 2015.[3] J. Fernandez et al., Binary RDF Representation for Publication and Exchange (HDT), Web Semantics 19:22-41, 2013.

Evaluation of Mathematical Self-Explanations with LSA in a Counterintuitive Problem of Probabilities

ERIC Educational Resources Information Center

Guiu, Jordi Maja

2012-01-01

In this paper different type of mathematical explanations are presented in relation to the mathematical problem of probabilities Monty Hall (card version) and the computational tool Latent Semantic Analyses (LSA) is used. At the moment the results in the literature about this computational tool to study texts show that this technique is…

The Effects of Learning a Computer Programming Language on the Logical Reasoning of School Children.

ERIC Educational Resources Information Center

Seidman, Robert H.

The research reported in this paper explores the syntactical and semantic link between computer programming statements and logical principles, and addresses the effects of learning a programming language on logical reasoning ability. Fifth grade students in a public school in Syracuse, New York, were randomly selected as subjects, and then…

Impaired Oral Reading in Two Atypical Dyslexics: A Comparison with a Computational Lexical-Analogy Model

ERIC Educational Resources Information Center

Marchand, Y.; Friedman, R.B.

2005-01-01

A computational model of reading was developed based upon the notion that the structural relationship between orthography and phonology is of greater importance than the dimension of semantics for the reading aloud of single words. Degradation of this model successfully simulated the reading performance of two patients with atypical acquired…

Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

PubMed Central

2012-01-01

Background The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. Results The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Conclusions Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net. PMID:22889332

Avogadro: an advanced semantic chemical editor, visualization, and analysis platform.

PubMed

Hanwell, Marcus D; Curtis, Donald E; Lonie, David C; Vandermeersch, Tim; Zurek, Eva; Hutchison, Geoffrey R

2012-08-13

The Avogadro project has developed an advanced molecule editor and visualizer designed for cross-platform use in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas. It offers flexible, high quality rendering, and a powerful plugin architecture. Typical uses include building molecular structures, formatting input files, and analyzing output of a wide variety of computational chemistry packages. By using the CML file format as its native document type, Avogadro seeks to enhance the semantic accessibility of chemical data types. The work presented here details the Avogadro library, which is a framework providing a code library and application programming interface (API) with three-dimensional visualization capabilities; and has direct applications to research and education in the fields of chemistry, physics, materials science, and biology. The Avogadro application provides a rich graphical interface using dynamically loaded plugins through the library itself. The application and library can each be extended by implementing a plugin module in C++ or Python to explore different visualization techniques, build/manipulate molecular structures, and interact with other programs. We describe some example extensions, one which uses a genetic algorithm to find stable crystal structures, and one which interfaces with the PackMol program to create packed, solvated structures for molecular dynamics simulations. The 1.0 release series of Avogadro is the main focus of the results discussed here. Avogadro offers a semantic chemical builder and platform for visualization and analysis. For users, it offers an easy-to-use builder, integrated support for downloading from common databases such as PubChem and the Protein Data Bank, extracting chemical data from a wide variety of formats, including computational chemistry output, and native, semantic support for the CML file format. For developers, it can be easily extended via a powerful plugin mechanism to support new features in organic chemistry, inorganic complexes, drug design, materials, biomolecules, and simulations. Avogadro is freely available under an open-source license from http://avogadro.openmolecules.net.

Computer Language For Optimization Of Design

NASA Technical Reports Server (NTRS)

Scotti, Stephen J.; Lucas, Stephen H.

1991-01-01

SOL is computer language geared to solution of design problems. Includes mathematical modeling and logical capabilities of computer language like FORTRAN; also includes additional power of nonlinear mathematical programming methods at language level. SOL compiler takes SOL-language statements and generates equivalent FORTRAN code and system calls. Provides syntactic and semantic checking for recovery from errors and provides detailed reports containing cross-references to show where each variable used. Implemented on VAX/VMS computer systems. Requires VAX FORTRAN compiler to produce executable program.

Context in Generalized Conversational Implicatures: The Case of Some

PubMed Central

Dupuy, Ludivine E.; Van der Henst, Jean-Baptiste; Cheylus, Anne; Reboul, Anne C.

2016-01-01

There is now general agreement about the optionality of scalar implicatures: the pragmatic interpretation will be accessed depending on the context relative to which the utterance is interpreted. The question, then, is what makes a context upper- (vs. lower-) bounding. Neo-Gricean accounts should predict that contexts including factual information will enhance the rate of pragmatic interpretations. Post-Gricean accounts should predict that contexts including psychological attributions will enhance the rate of pragmatic interpretations. We tested two factors using the quantifier scale : (1) the existence of factual information that facilitates the computation of pragmatic interpretations in the context (here, the cardinality of the domain of quantification) and (2) the fact that the context makes the difference between the semantic and the pragmatic interpretations of the target sentence relevant, involving psychological attributions to the speaker (here a question using all). We did three experiments, all of which suggest that while cardinality information may be necessary to the computation of the pragmatic interpretation, it plays a minor role in triggering it; highlighting the contrast between the pragmatic and the semantic interpretations, while it is not necessary to the computation of the pragmatic interpretation, strongly mandates a pragmatic interpretation. These results favor Sperber and Wilson's (1995) post-Gricean account over Chierchia's (2013) neo-Gricean account. Overall, this suggests that highlighting the relevance of the pragmatic vs. semantic interpretations of the target sentence makes a context upper-bounding. Additionally, the results give a small advantage to the post-Gricean account. PMID:27047413

What we talk about when we talk about access deficits

PubMed Central

Mirman, Daniel; Britt, Allison E.

2014-01-01

Semantic impairments have been divided into storage deficits, in which the semantic representations themselves are damaged, and access deficits, in which the representations are intact but access to them is impaired. The behavioural phenomena that have been associated with access deficits include sensitivity to cueing, sensitivity to presentation rate, performance inconsistency, negative serial position effects, sensitivity to number and strength of competitors, semantic blocking effects, disordered selection between strong and weak competitors, correlation between semantic deficits and executive function deficits and reduced word frequency effects. Four general accounts have been proposed for different subsets of these phenomena: abnormal refractoriness, too much activation, impaired competitive selection and deficits of semantic control. A combination of abnormal refractoriness and impaired competitive selection can account for most of the behavioural phenomena, but there remain several open questions. In particular, it remains unclear whether access deficits represent a single syndrome, a syndrome with multiple subtypes or a variable collection of phenomena, whether the underlying deficit is domain-general or domain-specific, whether it is owing to disorders of inhibition, activation or selection, and the nature of the connection (if any) between access phenomena in aphasia and in neurologically intact controls. Computational models offer a promising approach to answering these questions. PMID:24324232

Spatiotemporal integration of molecular and anatomical data in virtual reality using semantic mapping.

PubMed

Soh, Jung; Turinsky, Andrei L; Trinh, Quang M; Chang, Jasmine; Sabhaney, Ajay; Dong, Xiaoli; Gordon, Paul Mk; Janzen, Ryan Pw; Hau, David; Xia, Jianguo; Wishart, David S; Sensen, Christoph W

2009-01-01

We have developed a computational framework for spatiotemporal integration of molecular and anatomical datasets in a virtual reality environment. Using two case studies involving gene expression data and pharmacokinetic data, respectively, we demonstrate how existing knowledge bases for molecular data can be semantically mapped onto a standardized anatomical context of human body. Our data mapping methodology uses ontological representations of heterogeneous biomedical datasets and an ontology reasoner to create complex semantic descriptions of biomedical processes. This framework provides a means to systematically combine an increasing amount of biomedical imaging and numerical data into spatiotemporally coherent graphical representations. Our work enables medical researchers with different expertise to simulate complex phenomena visually and to develop insights through the use of shared data, thus paving the way for pathological inference, developmental pattern discovery and biomedical hypothesis testing.

Content relatedness in the social web based on social explicit semantic analysis

NASA Astrophysics Data System (ADS)

Ntalianis, Klimis; Otterbacher, Jahna; Mastorakis, Nikolaos

2017-06-01

In this paper a novel content relatedness algorithm for social media content is proposed, based on the Explicit Semantic Analysis (ESA) technique. The proposed scheme takes into consideration social interactions. In particular starting from the vector space representation model, similarity is expressed by a summation of term weight products. In this paper, term weights are estimated by a social computing method, where the strength of each term is calculated by the attention the terms receives. For this reason each post is split into two parts, title and comments area, while attention is defined by the number of social interactions such as likes and shares. The overall approach is named Social Explicit Semantic Analysis. Experimental results on real data show the advantages and limitations of the proposed approach, while an initial comparison between ESA and S-ESA is very promising.

Semantic information extracting system for classification of radiological reports in radiology information system (RIS)

NASA Astrophysics Data System (ADS)

Shi, Liehang; Ling, Tonghui; Zhang, Jianguo

2016-03-01

Radiologists currently use a variety of terminologies and standards in most hospitals in China, and even there are multiple terminologies being used for different sections in one department. In this presentation, we introduce a medical semantic comprehension system (MedSCS) to extract semantic information about clinical findings and conclusion from free text radiology reports so that the reports can be classified correctly based on medical terms indexing standards such as Radlex or SONMED-CT. Our system (MedSCS) is based on both rule-based methods and statistics-based methods which improve the performance and the scalability of MedSCS. In order to evaluate the over all of the system and measure the accuracy of the outcomes, we developed computation methods to calculate the parameters of precision rate, recall rate, F-score and exact confidence interval.

Minimizing the semantic gap in biomedical content-based image retrieval

NASA Astrophysics Data System (ADS)

Guan, Haiying; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2010-03-01

A major challenge in biomedical Content-Based Image Retrieval (CBIR) is to achieve meaningful mappings that minimize the semantic gap between the high-level biomedical semantic concepts and the low-level visual features in images. This paper presents a comprehensive learning-based scheme toward meeting this challenge and improving retrieval quality. The article presents two algorithms: a learning-based feature selection and fusion algorithm and the Ranking Support Vector Machine (Ranking SVM) algorithm. The feature selection algorithm aims to select 'good' features and fuse them using different similarity measurements to provide a better representation of the high-level concepts with the low-level image features. Ranking SVM is applied to learn the retrieval rank function and associate the selected low-level features with query concepts, given the ground-truth ranking of the training samples. The proposed scheme addresses four major issues in CBIR to improve the retrieval accuracy: image feature extraction, selection and fusion, similarity measurements, the association of the low-level features with high-level concepts, and the generation of the rank function to support high-level semantic image retrieval. It models the relationship between semantic concepts and image features, and enables retrieval at the semantic level. We apply it to the problem of vertebra shape retrieval from a digitized spine x-ray image set collected by the second National Health and Nutrition Examination Survey (NHANES II). The experimental results show an improvement of up to 41.92% in the mean average precision (MAP) over conventional image similarity computation methods.

Using Semantic Web technologies for the generation of domain-specific templates to support clinical study metadata standards.

PubMed

Jiang, Guoqian; Evans, Julie; Endle, Cory M; Solbrig, Harold R; Chute, Christopher G

2016-01-01

The Biomedical Research Integrated Domain Group (BRIDG) model is a formal domain analysis model for protocol-driven biomedical research, and serves as a semantic foundation for application and message development in the standards developing organizations (SDOs). The increasing sophistication and complexity of the BRIDG model requires new approaches to the management and utilization of the underlying semantics to harmonize domain-specific standards. The objective of this study is to develop and evaluate a Semantic Web-based approach that integrates the BRIDG model with ISO 21090 data types to generate domain-specific templates to support clinical study metadata standards development. We developed a template generation and visualization system based on an open source Resource Description Framework (RDF) store backend, a SmartGWT-based web user interface, and a "mind map" based tool for the visualization of generated domain-specific templates. We also developed a RESTful Web Service informed by the Clinical Information Modeling Initiative (CIMI) reference model for access to the generated domain-specific templates. A preliminary usability study is performed and all reviewers (n = 3) had very positive responses for the evaluation questions in terms of the usability and the capability of meeting the system requirements (with the average score of 4.6). Semantic Web technologies provide a scalable infrastructure and have great potential to enable computable semantic interoperability of models in the intersection of health care and clinical research.

Triangulation of the neurocomputational architecture underpinning reading aloud

PubMed Central

Hoffman, Paul; Lambon Ralph, Matthew A.; Woollams, Anna M.

2015-01-01

The goal of cognitive neuroscience is to integrate cognitive models with knowledge about underlying neural machinery. This significant challenge was explored in relation to word reading, where sophisticated computational-cognitive models exist but have made limited contact with neural data. Using distortion-corrected functional MRI and dynamic causal modeling, we investigated the interactions between brain regions dedicated to orthographic, semantic, and phonological processing while participants read words aloud. We found that the lateral anterior temporal lobe exhibited increased activation when participants read words with irregular spellings. This area is implicated in semantic processing but has not previously been considered part of the reading network. We also found meaningful individual differences in the activation of this region: Activity was predicted by an independent measure of the degree to which participants use semantic knowledge to read. These characteristics are predicted by the connectionist Triangle Model of reading and indicate a key role for semantic knowledge in reading aloud. Premotor regions associated with phonological processing displayed the reverse characteristics. Changes in the functional connectivity of the reading network during irregular word reading also were consistent with semantic recruitment. These data support the view that reading aloud is underpinned by the joint operation of two neural pathways. They reveal that (i) the ATL is an important element of the ventral semantic pathway and (ii) the division of labor between the two routes varies according to both the properties of the words being read and individual differences in the degree to which participants rely on each route. PMID:26124121

Standard biological parts knowledgebase.

PubMed

Galdzicki, Michal; Rodriguez, Cesar; Chandran, Deepak; Sauro, Herbert M; Gennari, John H

2011-02-24

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate "promoter" parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible.

MENTOR: an enabler for interoperable intelligent systems

NASA Astrophysics Data System (ADS)

Sarraipa, João; Jardim-Goncalves, Ricardo; Steiger-Garcao, Adolfo

2010-07-01

A community with knowledge organisation based on ontologies will enable an increase in the computational intelligence of its information systems. However, due to the worldwide diversity of communities, a high number of knowledge representation elements, which are not semantically coincident, have appeared representing the same segment of reality, becoming a barrier to business communications. Even if a domain community uses the same kind of technologies in its information systems, such as ontologies, it doesn't solve its semantics differences. In order to solve this interoperability problem, a solution is to use a reference ontology as an intermediary in the communications between the community enterprises and the outside, while allowing the enterprises to keep their own ontology and semantics unchanged internally. This work proposes MENTOR, a methodology to support the development of a common reference ontology for a group of organisations sharing the same business domain. This methodology is based on the mediator ontology (MO) concept, which assists the semantic transformations among each enterprise's ontology and the referential one. The MO enables each organisation to keep its own terminology, glossary and ontological structures, while providing seamless communication and interaction with the others.

Filtering Gene Ontology semantic similarity for identifying protein complexes in large protein interaction networks.

PubMed

Wang, Jian; Xie, Dong; Lin, Hongfei; Yang, Zhihao; Zhang, Yijia

2012-06-21

Many biological processes recognize in particular the importance of protein complexes, and various computational approaches have been developed to identify complexes from protein-protein interaction (PPI) networks. However, high false-positive rate of PPIs leads to challenging identification. A protein semantic similarity measure is proposed in this study, based on the ontology structure of Gene Ontology (GO) terms and GO annotations to estimate the reliability of interactions in PPI networks. Interaction pairs with low GO semantic similarity are removed from the network as unreliable interactions. Then, a cluster-expanding algorithm is used to detect complexes with core-attachment structure on filtered network. Our method is applied to three different yeast PPI networks. The effectiveness of our method is examined on two benchmark complex datasets. Experimental results show that our method performed better than other state-of-the-art approaches in most evaluation metrics. The method detects protein complexes from large scale PPI networks by filtering GO semantic similarity. Removing interactions with low GO similarity significantly improves the performance of complex identification. The expanding strategy is also effective to identify attachment proteins of complexes.

SoFoCles: feature filtering for microarray classification based on gene ontology.

PubMed

Papachristoudis, Georgios; Diplaris, Sotiris; Mitkas, Pericles A

2010-02-01

Marker gene selection has been an important research topic in the classification analysis of gene expression data. Current methods try to reduce the "curse of dimensionality" by using statistical intra-feature set calculations, or classifiers that are based on the given dataset. In this paper, we present SoFoCles, an interactive tool that enables semantic feature filtering in microarray classification problems with the use of external, well-defined knowledge retrieved from the Gene Ontology. The notion of semantic similarity is used to derive genes that are involved in the same biological path during the microarray experiment, by enriching a feature set that has been initially produced with legacy methods. Among its other functionalities, SoFoCles offers a large repository of semantic similarity methods that are used in order to derive feature sets and marker genes. The structure and functionality of the tool are discussed in detail, as well as its ability to improve classification accuracy. Through experimental evaluation, SoFoCles is shown to outperform other classification schemes in terms of classification accuracy in two real datasets using different semantic similarity computation approaches.

Integration of Neuroimaging and Microarray Datasets through Mapping and Model-Theoretic Semantic Decomposition of Unstructured Phenotypes

PubMed Central

Pantazatos, Spiro P.; Li, Jianrong; Pavlidis, Paul; Lussier, Yves A.

2009-01-01

An approach towards heterogeneous neuroscience dataset integration is proposed that uses Natural Language Processing (NLP) and a knowledge-based phenotype organizer system (PhenOS) to link ontology-anchored terms to underlying data from each database, and then maps these terms based on a computable model of disease (SNOMED CT®). The approach was implemented using sample datasets from fMRIDC, GEO, The Whole Brain Atlas and Neuronames, and allowed for complex queries such as “List all disorders with a finding site of brain region X, and then find the semantically related references in all participating databases based on the ontological model of the disease or its anatomical and morphological attributes”. Precision of the NLP-derived coding of the unstructured phenotypes in each dataset was 88% (n = 50), and precision of the semantic mapping between these terms across datasets was 98% (n = 100). To our knowledge, this is the first example of the use of both semantic decomposition of disease relationships and hierarchical information found in ontologies to integrate heterogeneous phenotypes across clinical and molecular datasets. PMID:20495688

iPad: Semantic annotation and markup of radiological images.

PubMed

Rubin, Daniel L; Rodriguez, Cesar; Shah, Priyanka; Beaulieu, Chris

2008-11-06

Radiological images contain a wealth of information,such as anatomy and pathology, which is often not explicit and computationally accessible. Information schemes are being developed to describe the semantic content of images, but such schemes can be unwieldy to operationalize because there are few tools to enable users to capture structured information easily as part of the routine research workflow. We have created iPad, an open source tool enabling researchers and clinicians to create semantic annotations on radiological images. iPad hides the complexity of the underlying image annotation information model from users, permitting them to describe images and image regions using a graphical interface that maps their descriptions to structured ontologies semi-automatically. Image annotations are saved in a variety of formats,enabling interoperability among medical records systems, image archives in hospitals, and the Semantic Web. Tools such as iPad can help reduce the burden of collecting structured information from images, and it could ultimately enable researchers and physicians to exploit images on a very large scale and glean the biological and physiological significance of image content.

Predicting Raters’ Transparency Judgments of English and Chinese Morphological Constituents using Latent Semantic Analysis

PubMed Central

Wang, Hsueh-Cheng; Hsu, Li-Chuan; Tien, Yi-Min; Pomplun, Marc

2013-01-01

The morphological constituents of English compounds (e.g., “butter” and “fly” for “butterfly”) and two-character Chinese compounds may differ in meaning from the whole word. Subjective differences and ambiguity of transparency make the judgments difficult, and a computational alternative based on a general model may be a way to average across subjective differences. The current study proposes two approaches based on Latent Semantic Analysis (Landauer & Dumais, 1997): Model 1 compares the semantic similarity between a compound word and each of its constituents, and Model 2 derives the dominant meaning of a constituent based on a clustering analysis of morphological family members (e.g., “butterfingers” or “buttermilk” for “butter”). The proposed models successfully predicted participants’ transparency ratings, and we recommend that experimenters use Model 1 for English compounds and Model 2 for Chinese compounds, due to raters’ morphological processing in different writing systems. The dominance of lexical meaning, semantic transparency, and the average similarity between all pairs within a morphological family are provided, and practical applications for future studies are discussed. PMID:23784009

HyQue: evaluating hypotheses using Semantic Web technologies

PubMed Central

2011-01-01

Background Key to the success of e-Science is the ability to computationally evaluate expert-composed hypotheses for validity against experimental data. Researchers face the challenge of collecting, evaluating and integrating large amounts of diverse information to compose and evaluate a hypothesis. Confronted with rapidly accumulating data, researchers currently do not have the software tools to undertake the required information integration tasks. Results We present HyQue, a Semantic Web tool for querying scientific knowledge bases with the purpose of evaluating user submitted hypotheses. HyQue features a knowledge model to accommodate diverse hypotheses structured as events and represented using Semantic Web languages (RDF/OWL). Hypothesis validity is evaluated against experimental and literature-sourced evidence through a combination of SPARQL queries and evaluation rules. Inference over OWL ontologies (for type specifications, subclass assertions and parthood relations) and retrieval of facts stored as Bio2RDF linked data provide support for a given hypothesis. We evaluate hypotheses of varying levels of detail about the genetic network controlling galactose metabolism in Saccharomyces cerevisiae to demonstrate the feasibility of deploying such semantic computing tools over a growing body of structured knowledge in Bio2RDF. Conclusions HyQue is a query-based hypothesis evaluation system that can currently evaluate hypotheses about the galactose metabolism in S. cerevisiae. Hypotheses as well as the supporting or refuting data are represented in RDF and directly linked to one another allowing scientists to browse from data to hypothesis and vice versa. HyQue hypotheses and data are available at http://semanticscience.org/projects/hyque. PMID:21624158

Opposing Effects of Semantic Diversity in Lexical and Semantic Relatedness Decisions

PubMed Central

2015-01-01

Semantic ambiguity has often been divided into 2 forms: homonymy, referring to words with 2 unrelated interpretations (e.g., bark), and polysemy, referring to words associated with a number of varying but semantically linked uses (e.g., twist). Typically, polysemous words are thought of as having a fixed number of discrete definitions, or “senses,” with each use of the word corresponding to one of its senses. In this study, we investigated an alternative conception of polysemy, based on the idea that polysemous variation in meaning is a continuous, graded phenomenon that occurs as a function of contextual variation in word usage. We quantified this contextual variation using semantic diversity (SemD), a corpus-based measure of the degree to which a particular word is used in a diverse set of linguistic contexts. In line with other approaches to polysemy, we found a reaction time (RT) advantage for high SemD words in lexical decision, which occurred for words of both high and low imageability. When participants made semantic relatedness decisions to word pairs, however, responses were slower to high SemD pairs, irrespective of whether these were related or unrelated. Again, this result emerged irrespective of the imageability of the word. The latter result diverges from previous findings using homonyms, in which ambiguity effects have only been found for related word pairs. We argue that participants were slower to respond to high SemD words because their high contextual variability resulted in noisy, underspecified semantic representations that were more difficult to compare with one another. We demonstrated this principle in a connectionist computational model that was trained to activate distributed semantic representations from orthographic inputs. Greater variability in the orthography-to-semantic mappings of high SemD words resulted in a lower degree of similarity for related pairs of this type. At the same time, the representations of high SemD unrelated pairs were less distinct from one another. In addition, the model demonstrated more rapid semantic activation for high SemD words, thought to underpin the processing advantage in lexical decision. These results support the view that polysemous variation in word meaning can be conceptualized in terms of graded variation in distributed semantic representations. PMID:25751041

Verification and Planning Based on Coinductive Logic Programming

NASA Technical Reports Server (NTRS)

Bansal, Ajay; Min, Richard; Simon, Luke; Mallya, Ajay; Gupta, Gopal

2008-01-01

Coinduction is a powerful technique for reasoning about unfounded sets, unbounded structures, infinite automata, and interactive computations [6]. Where induction corresponds to least fixed point's semantics, coinduction corresponds to greatest fixed point semantics. Recently coinduction has been incorporated into logic programming and an elegant operational semantics developed for it [11, 12]. This operational semantics is the greatest fix point counterpart of SLD resolution (SLD resolution imparts operational semantics to least fix point based computations) and is termed co- SLD resolution. In co-SLD resolution, a predicate goal p( t) succeeds if it unifies with one of its ancestor calls. In addition, rational infinite terms are allowed as arguments of predicates. Infinite terms are represented as solutions to unification equations and the occurs check is omitted during the unification process. Coinductive Logic Programming (Co-LP) and Co-SLD resolution can be used to elegantly perform model checking and planning. A combined SLD and Co-SLD resolution based LP system forms the common basis for planning, scheduling, verification, model checking, and constraint solving [9, 4]. This is achieved by amalgamating SLD resolution, co-SLD resolution, and constraint logic programming [13] in a single logic programming system. Given that parallelism in logic programs can be implicitly exploited [8], complex, compute-intensive applications (planning, scheduling, model checking, etc.) can be executed in parallel on multi-core machines. Parallel execution can result in speed-ups as well as in larger instances of the problems being solved. In the remainder we elaborate on (i) how planning can be elegantly and efficiently performed under real-time constraints, (ii) how real-time systems can be elegantly and efficiently model- checked, as well as (iii) how hybrid systems can be verified in a combined system with both co-SLD and SLD resolution. Implementations of co-SLD resolution as well as preliminary implementations of the planning and verification applications have been developed [4]. Co-LP and Model Checking: The vast majority of properties that are to be verified can be classified into safety properties and liveness properties. It is well known within model checking that safety properties can be verified by reachability analysis, i.e, if a counter-example to the property exists, it can be finitely determined by enumerating all the reachable states of the Kripke structure.

Characterizing cognitive performance in a large longitudinal study of aging with computerized semantic indices of verbal fluency.

PubMed

Pakhomov, Serguei V S; Eberly, Lynn; Knopman, David

2016-08-01

A computational approach for estimating several indices of performance on the animal category verbal fluency task was validated, and examined in a large longitudinal study of aging. The performance indices included the traditional verbal fluency score, size of semantic clusters, density of repeated words, as well as measures of semantic and lexical diversity. Change over time in these measures was modeled using mixed effects regression in several groups of participants, including those that remained cognitively normal throughout the study (CN) and those that were diagnosed with mild cognitive impairment (MCI) or Alzheimer's disease (AD) dementia at some point subsequent to the baseline visit. The results of the study show that, with the exception of mean cluster size, the indices showed significantly greater declines in the MCI and AD dementia groups as compared to CN participants. Examination of associations between the indices and cognitive domains of memory, attention and visuospatial functioning showed that the traditional verbal fluency scores were associated with declines in all three domains, whereas semantic and lexical diversity measures were associated with declines only in the visuospatial domain. Baseline repetition density was associated with declines in memory and visuospatial domains. Examination of lexical and semantic diversity measures in subgroups with high vs. low attention scores (but normal functioning in other domains) showed that the performance of individuals with low attention was influenced more by word frequency rather than strength of semantic relatedness between words. These findings suggest that various automatically semantic indices may be used to examine various aspects of cognitive performance affected by dementia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Semantic similarity between ontologies at different scales

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

Zhang, Qingpeng; Haglin, David J.

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

Towards Automatic Semantic Labelling of 3D City Models

NASA Astrophysics Data System (ADS)

Rook, M.; Biljecki, F.; Diakité, A. A.

2016-10-01

The lack of semantic information in many 3D city models is a considerable limiting factor in their use, as a lot of applications rely on semantics. Such information is not always available, since it is not collected at all times, it might be lost due to data transformation, or its lack may be caused by non-interoperability in data integration from other sources. This research is a first step in creating an automatic workflow that semantically labels plain 3D city model represented by a soup of polygons, with semantic and thematic information, as defined in the CityGML standard. The first step involves the reconstruction of the topology, which is used in a region growing algorithm that clusters upward facing adjacent triangles. Heuristic rules, embedded in a decision tree, are used to compute a likeliness score for these regions that either represent the ground (terrain) or a RoofSurface. Regions with a high likeliness score, to one of the two classes, are used to create a decision space, which is used in a support vector machine (SVM). Next, topological relations are utilised to select seeds that function as a start in a region growing algorithm, to create regions of triangles of other semantic classes. The topological relationships of the regions are used in the aggregation of the thematic building features. Finally, the level of detail is detected to generate the correct output in CityGML. The results show an accuracy between 85 % and 99 % in the automatic semantic labelling on four different test datasets. The paper is concluded by indicating problems and difficulties implying the next steps in the research.

Comparison between semantic features and lung-RADS in predicting malignancy of screening lung nodule

PubMed Central

Li, Qian; Balagurunathan, Yoganand; Liu, Ying; Qi, Jin; Schabath, Matthew B.; Ye, Zhaoxiang; Gillies, Robert

2017-01-01

Rationale Lung-RADS is proposed for the Low-dose computed tomography (LDCT) interpretation in lung cancer screening, but its performance needs to be further evaluated. Objectives To compare the value of radiological semantic features and lung-RADS in predicting nodule malignancy risk at different screening rounds, and to investigate whether the predictive power of lung-RADS could be improved by incorporating semantic features. Methods A training cohort of 199 patients (139 benign and 60 cancerous nodules diagnosed at the third screening round), and a testing cohort of 80 patients (40 benign and 40 malignant nodules) were obtained from the National Lung Screening Trial dataset. A multivariate linear predictor model was built based on the 24 systematically scored semantic features, and the performances were compared to lung-RADS (scale 3 or above called positive). Measurements and Main Results Among the semantic features, contour and border definition were the top individual predictors. The average area under the receiver-operating characteristic curve (AUC) of border definition at baseline (T0) was 0.724. The average AUC of contour at first (T1) and second follow-up (T2) were 0.843 and 0.878, respectively. Other significant features included size, location, vessel attachment, solidity, focal emphysema and focal fibrosis. In comparison, the average AUC of lung-RADS at T0, T1 and T2 were 0.600, 0.760 and 0.867, respectively, and could be improved to 0.743, 0.887 and 0.968 by adding semantic features. Conclusion The semantic features performed similar to lung-RADS at follow-ups, outperformed lung-RADS at baseline, and could improve the performance of lung-RADS for all screening rounds. PMID:29137847

A Semantically Enriched Context-Aware OER Recommendation Strategy and Its Application to a Computer Science OER Repository

ERIC Educational Resources Information Center

Ruiz-Iniesta, Almudena; Jiménez-Díaz, Guillermo; Gómez-Albarrán, Mercedes

2014-01-01

This paper describes a knowledge-based strategy for recommending educational resources-worked problems, exercises, quiz questions, and lecture notes-to learners in the first two courses in the introductory sequence of a computer science major (CS1 and CS2). The goal of the recommendation strategy is to provide support for personalized access to…

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration

PubMed Central

2011-01-01

Background Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information. Results Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase. Conclusions By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research. PMID:21595881

Chemical Entity Semantic Specification: Knowledge representation for efficient semantic cheminformatics and facile data integration.

PubMed

Chepelev, Leonid L; Dumontier, Michel

2011-05-19

Over the past several centuries, chemistry has permeated virtually every facet of human lifestyle, enriching fields as diverse as medicine, agriculture, manufacturing, warfare, and electronics, among numerous others. Unfortunately, application-specific, incompatible chemical information formats and representation strategies have emerged as a result of such diverse adoption of chemistry. Although a number of efforts have been dedicated to unifying the computational representation of chemical information, disparities between the various chemical databases still persist and stand in the way of cross-domain, interdisciplinary investigations. Through a common syntax and formal semantics, Semantic Web technology offers the ability to accurately represent, integrate, reason about and query across diverse chemical information. Here we specify and implement the Chemical Entity Semantic Specification (CHESS) for the representation of polyatomic chemical entities, their substructures, bonds, atoms, and reactions using Semantic Web technologies. CHESS provides means to capture aspects of their corresponding chemical descriptors, connectivity, functional composition, and geometric structure while specifying mechanisms for data provenance. We demonstrate that using our readily extensible specification, it is possible to efficiently integrate multiple disparate chemical data sources, while retaining appropriate correspondence of chemical descriptors, with very little additional effort. We demonstrate the impact of some of our representational decisions on the performance of chemically-aware knowledgebase searching and rudimentary reaction candidate selection. Finally, we provide access to the tools necessary to carry out chemical entity encoding in CHESS, along with a sample knowledgebase. By harnessing the power of Semantic Web technologies with CHESS, it is possible to provide a means of facile cross-domain chemical knowledge integration with full preservation of data correspondence and provenance. Our representation builds on existing cheminformatics technologies and, by the virtue of RDF specification, remains flexible and amenable to application- and domain-specific annotations without compromising chemical data integration. We conclude that the adoption of a consistent and semantically-enabled chemical specification is imperative for surviving the coming chemical data deluge and supporting systems science research.

Structural Similarities between Brain and Linguistic Data Provide Evidence of Semantic Relations in the Brain

PubMed Central

Crangle, Colleen E.; Perreau-Guimaraes, Marcos; Suppes, Patrick

2013-01-01

This paper presents a new method of analysis by which structural similarities between brain data and linguistic data can be assessed at the semantic level. It shows how to measure the strength of these structural similarities and so determine the relatively better fit of the brain data with one semantic model over another. The first model is derived from WordNet, a lexical database of English compiled by language experts. The second is given by the corpus-based statistical technique of latent semantic analysis (LSA), which detects relations between words that are latent or hidden in text. The brain data are drawn from experiments in which statements about the geography of Europe were presented auditorily to participants who were asked to determine their truth or falsity while electroencephalographic (EEG) recordings were made. The theoretical framework for the analysis of the brain and semantic data derives from axiomatizations of theories such as the theory of differences in utility preference. Using brain-data samples from individual trials time-locked to the presentation of each word, ordinal relations of similarity differences are computed for the brain data and for the linguistic data. In each case those relations that are invariant with respect to the brain and linguistic data, and are correlated with sufficient statistical strength, amount to structural similarities between the brain and linguistic data. Results show that many more statistically significant structural similarities can be found between the brain data and the WordNet-derived data than the LSA-derived data. The work reported here is placed within the context of other recent studies of semantics and the brain. The main contribution of this paper is the new method it presents for the study of semantics and the brain and the focus it permits on networks of relations detected in brain data and represented by a semantic model. PMID:23799009

Computing Information Value from RDF Graph Properties

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

al-Saffar, Sinan; Heileman, Gregory

2010-11-08

Information value has been implicitly utilized and mostly non-subjectively computed in information retrieval (IR) systems. We explicitly define and compute the value of an information piece as a function of two parameters, the first is the potential semantic impact the target information can subjectively have on its recipient's world-knowledge, and the second parameter is trust in the information source. We model these two parameters as properties of RDF graphs. Two graphs are constructed, a target graph representing the semantics of the target body of information and a context graph representing the context of the consumer of that information. We computemore » information value subjectively as a function of both potential change to the context graph (impact) and the overlap between the two graphs (trust). Graph change is computed as a graph edit distance measuring the dissimilarity between the context graph before and after the learning of the target graph. A particular application of this subjective information valuation is in the construction of a personalized ranking component in Web search engines. Based on our method, we construct a Web re-ranking system that personalizes the information experience for the information-consumer.« less

Evolvix BEST Names for semantic reproducibility across code2brain interfaces

PubMed Central

Scheuer, Katherine S.; Keel, Seth A.; Vyas, Vaibhav; Liblit, Ben; Hanlon, Bret; Ferris, Michael C.; Yin, John; Dutra, Inês; Pietsch, Anthony; Javid, Christine G.; Moog, Cecilia L.; Meyer, Jocelyn; Dresel, Jerdon; McLoone, Brian; Loberger, Sonya; Movaghar, Arezoo; Gilchrist‐Scott, Morgaine; Sabri, Yazeed; Sescleifer, Dave; Pereda‐Zorrilla, Ivan; Zietlow, Andrew; Smith, Rodrigo; Pietenpol, Samantha; Goldfinger, Jacob; Atzen, Sarah L.; Freiberg, Erika; Waters, Noah P.; Nusbaum, Claire; Nolan, Erik; Hotz, Alyssa; Kliman, Richard M.; Mentewab, Ayalew; Fregien, Nathan; Loewe, Martha

2016-01-01

Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general‐purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long‐term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder‐brains to reader‐brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core. PMID:27918836

BeeSpace Navigator: exploratory analysis of gene function using semantic indexing of biological literature.

PubMed

Sen Sarma, Moushumi; Arcoleo, David; Khetani, Radhika S; Chee, Brant; Ling, Xu; He, Xin; Jiang, Jing; Mei, Qiaozhu; Zhai, ChengXiang; Schatz, Bruce

2011-07-01

With the rapid decrease in cost of genome sequencing, the classification of gene function is becoming a primary problem. Such classification has been performed by human curators who read biological literature to extract evidence. BeeSpace Navigator is a prototype software for exploratory analysis of gene function using biological literature. The software supports an automatic analogue of the curator process to extract functions, with a simple interface intended for all biologists. Since extraction is done on selected collections that are semantically indexed into conceptual spaces, the curation can be task specific. Biological literature containing references to gene lists from expression experiments can be analyzed to extract concepts that are computational equivalents of a classification such as Gene Ontology, yielding discriminating concepts that differentiate gene mentions from other mentions. The functions of individual genes can be summarized from sentences in biological literature, to produce results resembling a model organism database entry that is automatically computed. Statistical frequency analysis based on literature phrase extraction generates offline semantic indexes to support these gene function services. The website with BeeSpace Navigator is free and open to all; there is no login requirement at www.beespace.illinois.edu for version 4. Materials from the 2010 BeeSpace Software Training Workshop are available at www.beespace.illinois.edu/bstwmaterials.php.

Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services.

PubMed

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Lee, Sungyoung; Chung, Tae Choong

2016-01-01

Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify secure outsourced data which contains semantically equivalent information. In addition, trapdoor-based methodologies are confined to pre-defined trapdoors and prevent subscribers from searching outsourced data with arbitrarily defined search criteria. To solve the problem of relevant data access, we have proposed an index-based privacy-aware search methodology that ensures semantic retrieval of data from an untrusted domain. This method ensures oblivious execution of a search query and leverages authorized subscribers to model conjunctive search queries without relying on predefined trapdoors. A security analysis of our proposed methodology shows that, in a conspired attack, unauthorized subscribers and untrusted cloud service providers cannot deduce any information that can lead to the potential loss of data privacy. A computational time analysis on commodity hardware demonstrates that our proposed methodology requires moderate computational resources to model a privacy-aware search query and for its oblivious evaluation on a cloud service provider.

Privacy-Aware Relevant Data Access with Semantically Enriched Search Queries for Untrusted Cloud Storage Services

PubMed Central

Pervez, Zeeshan; Ahmad, Mahmood; Khattak, Asad Masood; Lee, Sungyoung; Chung, Tae Choong

2016-01-01

Privacy-aware search of outsourced data ensures relevant data access in the untrusted domain of a public cloud service provider. Subscriber of a public cloud storage service can determine the presence or absence of a particular keyword by submitting search query in the form of a trapdoor. However, these trapdoor-based search queries are limited in functionality and cannot be used to identify secure outsourced data which contains semantically equivalent information. In addition, trapdoor-based methodologies are confined to pre-defined trapdoors and prevent subscribers from searching outsourced data with arbitrarily defined search criteria. To solve the problem of relevant data access, we have proposed an index-based privacy-aware search methodology that ensures semantic retrieval of data from an untrusted domain. This method ensures oblivious execution of a search query and leverages authorized subscribers to model conjunctive search queries without relying on predefined trapdoors. A security analysis of our proposed methodology shows that, in a conspired attack, unauthorized subscribers and untrusted cloud service providers cannot deduce any information that can lead to the potential loss of data privacy. A computational time analysis on commodity hardware demonstrates that our proposed methodology requires moderate computational resources to model a privacy-aware search query and for its oblivious evaluation on a cloud service provider. PMID:27571421

Assessing Semantic Knowledge Using Computer-Based and Paper-Based Media

DTIC Science & Technology

1992-01-01

capitalized upon in this research. Computer-Based Assessment A computer-based game or test, FlashCards (Liggett & Federico, 1986), was adopt- ed and adapted...alterative forms did not have to be specifically or previously programmed as such. FlashCards is analogous to using real flash cards. That is, a...reflects their degree of confidence in their answer. Also, for each answer the student’s response latency is recorded and displayed. FlashCards quizzed

Computer assessment of interview data using latent semantic analysis.

PubMed

Dam, Gregory; Kaufmann, Stefan

2008-02-01

Clinical interviews are a powerful method for assessing students' knowledge and conceptualdevelopment. However, the analysis of the resulting data is time-consuming and can create a "bottleneck" in large-scale studies. This article demonstrates the utility of computational methods in supporting such an analysis. Thirty-four 7th-grade student explanations of the causes of Earth's seasons were assessed using latent semantic analysis (LSA). Analyses were performed on transcriptions of student responses during interviews administered, prior to (n = 21) and after (n = 13) receiving earth science instruction. An instrument that uses LSA technology was developed to identify misconceptions and assess conceptual change in students' thinking. Its accuracy, as determined by comparing its classifications to the independent coding performed by four human raters, reached 90%. Techniques for adapting LSA technology to support the analysis of interview data, as well as some limitations, are discussed.

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback

PubMed Central

Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery. PMID:27861505

Content-Based Discovery for Web Map Service using Support Vector Machine and User Relevance Feedback.

PubMed

Hu, Kai; Gui, Zhipeng; Cheng, Xiaoqiang; Qi, Kunlun; Zheng, Jie; You, Lan; Wu, Huayi

2016-01-01

Many discovery methods for geographic information services have been proposed. There are approaches for finding and matching geographic information services, methods for constructing geographic information service classification schemes, and automatic geographic information discovery. Overall, the efficiency of the geographic information discovery keeps improving., There are however, still two problems in Web Map Service (WMS) discovery that must be solved. Mismatches between the graphic contents of a WMS and the semantic descriptions in the metadata make discovery difficult for human users. End-users and computers comprehend WMSs differently creating semantic gaps in human-computer interactions. To address these problems, we propose an improved query process for WMSs based on the graphic contents of WMS layers, combining Support Vector Machine (SVM) and user relevance feedback. Our experiments demonstrate that the proposed method can improve the accuracy and efficiency of WMS discovery.

Conceptual Hierarchies in a Flat Attractor Network

PubMed Central

O’Connor, Christopher M.; Cree, George S.; McRae, Ken

2009-01-01

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. PMID:19543434

Extracting semantics from audio-visual content: the final frontier in multimedia retrieval.

PubMed

Naphade, M R; Huang, T S

2002-01-01

Multimedia understanding is a fast emerging interdisciplinary research area. There is tremendous potential for effective use of multimedia content through intelligent analysis. Diverse application areas are increasingly relying on multimedia understanding systems. Advances in multimedia understanding are related directly to advances in signal processing, computer vision, pattern recognition, multimedia databases, and smart sensors. We review the state-of-the-art techniques in multimedia retrieval. In particular, we discuss how multimedia retrieval can be viewed as a pattern recognition problem. We discuss how reliance on powerful pattern recognition and machine learning techniques is increasing in the field of multimedia retrieval. We review the state-of-the-art multimedia understanding systems with particular emphasis on a system for semantic video indexing centered around multijects and multinets. We discuss how semantic retrieval is centered around concepts and context and the various mechanisms for modeling concepts and context.

OMOGENIA: A Semantically Driven Collaborative Environment

NASA Astrophysics Data System (ADS)

Liapis, Aggelos

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

Sharing Epigraphic Information as Linked Data

NASA Astrophysics Data System (ADS)

Álvarez, Fernando-Luis; García-Barriocanal, Elena; Gómez-Pantoja, Joaquín-L.

The diffusion of epigraphic data has evolved in the last years from printed catalogues to indexed digital databases shared through the Web. Recently, the open EpiDoc specifications have resulted in an XML-based schema for the interchange of ancient texts that uses XSLT to render typographic representations. However, these schemas and representation systems are still not providing a way to encode computational semantics and semantic relations between pieces of epigraphic data. This paper sketches an approach to bring these semantics into an EpiDoc based schema using the Ontology Web Language (OWL) and following the principles and methods of information sharing known as "linked data". The paper describes the general principles of the OWL mapping of the EpiDoc schema and how epigraphic data can be shared in RDF format via dereferenceable URIs that can be used to build advanced search, visualization and analysis systems.

The Semantic Retrieval of Spatial Data Service Based on Ontology in SIG

NASA Astrophysics Data System (ADS)

Sun, S.; Liu, D.; Li, G.; Yu, W.

2011-08-01

The research of SIG (Spatial Information Grid) mainly solves the problem of how to connect different computing resources, so that users can use all the resources in the Grid transparently and seamlessly. In SIG, spatial data service is described in some kinds of specifications, which use different meta-information of each kind of services. This kind of standardization cannot resolve the problem of semantic heterogeneity, which may limit user to obtain the required resources. This paper tries to solve two kinds of semantic heterogeneities (name heterogeneity and structure heterogeneity) in spatial data service retrieval based on ontology, and also, based on the hierarchical subsumption relationship among concept in ontology, the query words can be extended and more resource can be matched and found for user. These applications of ontology in spatial data resource retrieval can help to improve the capability of keyword matching, and find more related resources.

Ontology-aided Data Fusion (Invited)

NASA Astrophysics Data System (ADS)

Raskin, R.

2009-12-01

An ontology provides semantic descriptions that are analogous to those in a dictionary, but are readable by both computers and humans. A data or service is semantically annotated when it is formally associated with elements of an ontology. The ESIP Federation Semantic Web Cluster has developed a set of ontologies to describe datatypes and data services that can be used to support automated data fusion. The service ontology includes descriptors of the service function, its inputs/outputs, and its invocation method. The datatype descriptors resemble typical metadata fields (data format, data model, data structure, originator, etc.) augmented with descriptions of the meaning of the data. These ontologies, in combination with the SWEET science ontology, enable a registered data fusion service to be chained together and implemented that is scientifically meaningful based on machine understanding of the associated data and services. This presentation describes initial results and experiences in automated data fusion.

Hierarchical semantic structures for medical NLP.

PubMed

Taira, Ricky K; Arnold, Corey W

2013-01-01

We present a framework for building a medical natural language processing (NLP) system capable of deep understanding of clinical text reports. The framework helps developers understand how various NLP-related efforts and knowledge sources can be integrated. The aspects considered include: 1) computational issues dealing with defining layers of intermediate semantic structures to reduce the dimensionality of the NLP problem; 2) algorithmic issues in which we survey the NLP literature and discuss state-of-the-art procedures used to map between various levels of the hierarchy; and 3) implementation issues to software developers with available resources. The objective of this poster is to educate readers to the various levels of semantic representation (e.g., word level concepts, ontological concepts, logical relations, logical frames, discourse structures, etc.). The poster presents an architecture for which diverse efforts and resources in medical NLP can be integrated in a principled way.

Exploiting salient semantic analysis for information retrieval

NASA Astrophysics Data System (ADS)

Luo, Jing; Meng, Bo; Quan, Changqin; Tu, Xinhui

2016-11-01

Recently, many Wikipedia-based methods have been proposed to improve the performance of different natural language processing (NLP) tasks, such as semantic relatedness computation, text classification and information retrieval. Among these methods, salient semantic analysis (SSA) has been proven to be an effective way to generate conceptual representation for words or documents. However, its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use SSA to improve the information retrieval performance, and propose a SSA-based retrieval method under the language model framework. First, SSA model is adopted to build conceptual representations for documents and queries. Then, these conceptual representations and the bag-of-words (BOW) representations can be used in combination to estimate the language models of queries and documents. The proposed method is evaluated on several standard text retrieval conference (TREC) collections. Experiment results on standard TREC collections show the proposed models consistently outperform the existing Wikipedia-based retrieval methods.

Network-based approaches to climate knowledge discovery

NASA Astrophysics Data System (ADS)

Budich, Reinhard; Nyberg, Per; Weigel, Tobias

2011-11-01

Climate Knowledge Discovery Workshop; Hamburg, Germany, 30 March to 1 April 2011 Do complex networks combined with semantic Web technologies offer the next generation of solutions in climate science? To address this question, a first Climate Knowledge Discovery (CKD) Workshop, hosted by the German Climate Computing Center (Deutsches Klimarechenzentrum (DKRZ)), brought together climate and computer scientists from major American and European laboratories, data centers, and universities, as well as representatives from industry, the broader academic community, and the semantic Web communities. The participants, representing six countries, were concerned with large-scale Earth system modeling and computational data analysis. The motivation for the meeting was the growing problem that climate scientists generate data faster than it can be interpreted and the need to prepare for further exponential data increases. Current analysis approaches are focused primarily on traditional methods, which are best suited for large-scale phenomena and coarse-resolution data sets. The workshop focused on the open discussion of ideas and technologies to provide the next generation of solutions to cope with the increasing data volumes in climate science.

Combining computational models, semantic annotations and simulation experiments in a graph database

PubMed Central

Henkel, Ron; Wolkenhauer, Olaf; Waltemath, Dagmar

2015-01-01

Model repositories such as the BioModels Database, the CellML Model Repository or JWS Online are frequently accessed to retrieve computational models of biological systems. However, their storage concepts support only restricted types of queries and not all data inside the repositories can be retrieved. In this article we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models’ structure, incorporates semantic annotations and simulation descriptions and ultimately connects different types of model-related data. The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features. The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching and filtering. Furthermore, our work for the first time enables CellML- and Systems Biology Markup Language-encoded models to be effectively maintained in one database. We show how these models can be linked via annotations and queried. Database URL: https://sems.uni-rostock.de/projects/masymos/ PMID:25754863

Multiple Semantic Matching on Augmented N-partite Graph for Object Co-segmentation.

PubMed

Wang, Chuan; Zhang, Hua; Yang, Liang; Cao, Xiaochun; Xiong, Hongkai

2017-09-08

Recent methods for object co-segmentation focus on discovering single co-occurring relation of candidate regions representing the foreground of multiple images. However, region extraction based only on low and middle level information often occupies a large area of background without the help of semantic context. In addition, seeking single matching solution very likely leads to discover local parts of common objects. To cope with these deficiencies, we present a new object cosegmentation framework, which takes advantages of semantic information and globally explores multiple co-occurring matching cliques based on an N-partite graph structure. To this end, we first propose to incorporate candidate generation with semantic context. Based on the regions extracted from semantic segmentation of each image, we design a merging mechanism to hierarchically generate candidates with high semantic responses. Secondly, all candidates are taken into consideration to globally formulate multiple maximum weighted matching cliques, which complements the discovery of part of the common objects induced by a single clique. To facilitate the discovery of multiple matching cliques, an N-partite graph, which inherently excludes intralinks between candidates from the same image, is constructed to separate multiple cliques without additional constraints. Further, we augment the graph with an additional virtual node in each part to handle irrelevant matches when the similarity between two candidates is too small. Finally, with the explored multiple cliques, we statistically compute pixel-wise co-occurrence map for each image. Experimental results on two benchmark datasets, i.e., iCoseg and MSRC datasets, achieve desirable performance and demonstrate the effectiveness of our proposed framework.

Oxytocin Modulates Semantic Integration in Speech Comprehension.

PubMed

Ye, Zheng; Stolk, Arjen; Toni, Ivan; Hagoort, Peter

2017-02-01

Listeners interpret utterances by integrating information from multiple sources including word level semantics and world knowledge. When the semantics of an expression is inconsistent with their knowledge about the world, the listener may have to search through the conceptual space for alternative possible world scenarios that can make the expression more acceptable. Such cognitive exploration requires considerable computational resources and might depend on motivational factors. This study explores whether and how oxytocin, a neuropeptide known to influence social motivation by reducing social anxiety and enhancing affiliative tendencies, can modulate the integration of world knowledge and sentence meanings. The study used a between-participant double-blind randomized placebo-controlled design. Semantic integration, indexed with magnetoencephalography through the N400m marker, was quantified while 45 healthy male participants listened to sentences that were either congruent or incongruent with facts of the world, after receiving intranasally delivered oxytocin or placebo. Compared with congruent sentences, world knowledge incongruent sentences elicited a stronger N400m signal from the left inferior frontal and anterior temporal regions and medial pFC (the N400m effect) in the placebo group. Oxytocin administration significantly attenuated the N400m effect at both sensor and cortical source levels throughout the experiment, in a state-like manner. Additional electrophysiological markers suggest that the absence of the N400m effect in the oxytocin group is unlikely due to the lack of early sensory or semantic processing or a general downregulation of attention. These findings suggest that oxytocin drives listeners to resolve challenges of semantic integration, possibly by promoting the cognitive exploration of alternative possible world scenarios.

Apples are not the only fruit: the effects of concept typicality on semantic representation in the anterior temporal lobe

PubMed Central

Woollams, Anna M.

2012-01-01

Intuitively, an apple seems a fairly good example of a fruit, whereas an avocado seems less so. The extent to which an exemplar is representative of its category, referred to here as concept typicality, has long been thought to be a key dimension determining semantic representation. Concept typicality is, however, correlated with a number of other variables, in particular age of acquisition (AoA) and name frequency. Consideration of picture naming accuracy from a large case-series of semantic dementia (SD) patients demonstrated strong effects of concept typicality that were maximal in the moderately impaired patients, over and above the impact of AoA and name frequency. Induction of a temporary virtual lesion to the left anterior temporal lobe, the region most commonly affected in SD, via repetitive Transcranial Magnetic Stimulation produced an enhanced effect of concept typicality in the picture naming of normal participants, but did not affect the magnitude of the AoA or name frequency effects. These results indicate that concept typicality exerts its influence on semantic representations themselves, as opposed to the strength of connections outside the semantic system. To date, there has been little direct exploration of the dimension of concept typicality within connectionist models of intact and impaired conceptual representation, and these findings provide a target for future computational simulation. PMID:22529789

Students' Beliefs about Mobile Devices vs. Desktop Computers in South Korea and the United States

ERIC Educational Resources Information Center

Sung, Eunmo; Mayer, Richard E.

2012-01-01

College students in the United States and in South Korea completed a 28-item multidimensional scaling (MDS) questionnaire in which they rated the similarity of 28 pairs of multimedia learning materials on a 10-point scale (e.g., narrated animation on a mobile device Vs. movie clip on a desktop computer) and a 56-item semantic differential…

Quantum information, cognition, and music.

PubMed

Dalla Chiara, Maria L; Giuntini, Roberto; Leporini, Roberto; Negri, Eleonora; Sergioli, Giuseppe

2015-01-01

Parallelism represents an essential aspect of human mind/brain activities. One can recognize some common features between psychological parallelism and the characteristic parallel structures that arise in quantum theory and in quantum computation. The article is devoted to a discussion of the following questions: a comparison between classical probabilistic Turing machines and quantum Turing machines.possible applications of the quantum computational semantics to cognitive problems.parallelism in music.

Quantum information, cognition, and music

PubMed Central

Dalla Chiara, Maria L.; Giuntini, Roberto; Leporini, Roberto; Negri, Eleonora; Sergioli, Giuseppe

2015-01-01

Parallelism represents an essential aspect of human mind/brain activities. One can recognize some common features between psychological parallelism and the characteristic parallel structures that arise in quantum theory and in quantum computation. The article is devoted to a discussion of the following questions: a comparison between classical probabilistic Turing machines and quantum Turing machines.possible applications of the quantum computational semantics to cognitive problems.parallelism in music. PMID:26539139

A bibliometric and visual analysis of global geo-ontology research

NASA Astrophysics Data System (ADS)

Li, Lin; Liu, Yu; Zhu, Haihong; Ying, Shen; Luo, Qinyao; Luo, Heng; Kuai, Xi; Xia, Hui; Shen, Hang

2017-02-01

In this paper, the results of a bibliometric and visual analysis of geo-ontology research articles collected from the Web of Science (WOS) database between 1999 and 2014 are presented. The numbers of national institutions and published papers are visualized and a global research heat map is drawn, illustrating an overview of global geo-ontology research. In addition, we present a chord diagram of countries and perform a visual cluster analysis of a knowledge co-citation network of references, disclosing potential academic communities and identifying key points, main research areas, and future research trends. The International Journal of Geographical Information Science, Progress in Human Geography, and Computers & Geosciences are the most active journals. The USA makes the largest contributions to geo-ontology research by virtue of its highest numbers of independent and collaborative papers, and its dominance was also confirmed in the country chord diagram. The majority of institutions are in the USA, Western Europe, and Eastern Asia. Wuhan University, University of Munster, and the Chinese Academy of Sciences are notable geo-ontology institutions. Keywords such as "Semantic Web," "GIS," and "space" have attracted a great deal of attention. "Semantic granularity in ontology-driven geographic information systems, "Ontologies in support of activities in geographical space" and "A translation approach to portable ontology specifications" have the highest cited centrality. Geographical space, computer-human interaction, and ontology cognition are the three main research areas of geo-ontology. The semantic mismatch between the producers and users of ontology data as well as error propagation in interdisciplinary and cross-linguistic data reuse needs to be solved. In addition, the development of geo-ontology modeling primitives based on OWL (Web Ontology Language）and finding methods to automatically rework data in Semantic Web are needed. Furthermore, the topological relations between geographical entities still require further study.

Standard Biological Parts Knowledgebase

PubMed Central

Galdzicki, Michal; Rodriguez, Cesar; Chandran, Deepak; Sauro, Herbert M.; Gennari, John H.

2011-01-01

We have created the Knowledgebase of Standard Biological Parts (SBPkb) as a publically accessible Semantic Web resource for synthetic biology (sbolstandard.org). The SBPkb allows researchers to query and retrieve standard biological parts for research and use in synthetic biology. Its initial version includes all of the information about parts stored in the Registry of Standard Biological Parts (partsregistry.org). SBPkb transforms this information so that it is computable, using our semantic framework for synthetic biology parts. This framework, known as SBOL-semantic, was built as part of the Synthetic Biology Open Language (SBOL), a project of the Synthetic Biology Data Exchange Group. SBOL-semantic represents commonly used synthetic biology entities, and its purpose is to improve the distribution and exchange of descriptions of biological parts. In this paper, we describe the data, our methods for transformation to SBPkb, and finally, we demonstrate the value of our knowledgebase with a set of sample queries. We use RDF technology and SPARQL queries to retrieve candidate “promoter” parts that are known to be both negatively and positively regulated. This method provides new web based data access to perform searches for parts that are not currently possible. PMID:21390321

Translation-aware semantic segmentation via conditional least-square generative adversarial networks

NASA Astrophysics Data System (ADS)

Zhang, Mi; Hu, Xiangyun; Zhao, Like; Pang, Shiyan; Gong, Jinqi; Luo, Min

2017-10-01

Semantic segmentation has recently made rapid progress in the field of remote sensing and computer vision. However, many leading approaches cannot simultaneously translate label maps to possible source images with a limited number of training images. The core issue is insufficient adversarial information to interpret the inverse process and proper objective loss function to overcome the vanishing gradient problem. We propose the use of conditional least squares generative adversarial networks (CLS-GAN) to delineate visual objects and solve these problems. We trained the CLS-GAN network for semantic segmentation to discriminate dense prediction information either from training images or generative networks. We show that the optimal objective function of CLS-GAN is a special class of f-divergence and yields a generator that lies on the decision boundary of discriminator that reduces possible vanished gradient. We also demonstrate the effectiveness of the proposed architecture at translating images from label maps in the learning process. Experiments on a limited number of high resolution images, including close-range and remote sensing datasets, indicate that the proposed method leads to the improved semantic segmentation accuracy and can simultaneously generate high quality images from label maps.

Joint classification and contour extraction of large 3D point clouds

NASA Astrophysics Data System (ADS)

Hackel, Timo; Wegner, Jan D.; Schindler, Konrad

2017-08-01

We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours. Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with >109 points.

S3DB core: a framework for RDF generation and management in bioinformatics infrastructures

PubMed Central

2010-01-01

Background Biomedical research is set to greatly benefit from the use of semantic web technologies in the design of computational infrastructure. However, beyond well defined research initiatives, substantial issues of data heterogeneity, source distribution, and privacy currently stand in the way towards the personalization of Medicine. Results A computational framework for bioinformatic infrastructure was designed to deal with the heterogeneous data sources and the sensitive mixture of public and private data that characterizes the biomedical domain. This framework consists of a logical model build with semantic web tools, coupled with a Markov process that propagates user operator states. An accompanying open source prototype was developed to meet a series of applications that range from collaborative multi-institution data acquisition efforts to data analysis applications that need to quickly traverse complex data structures. This report describes the two abstractions underlying the S3DB-based infrastructure, logical and numerical, and discusses its generality beyond the immediate confines of existing implementations. Conclusions The emergence of the "web as a computer" requires a formal model for the different functionalities involved in reading and writing to it. The S3DB core model proposed was found to address the design criteria of biomedical computational infrastructure, such as those supporting large scale multi-investigator research, clinical trials, and molecular epidemiology. PMID:20646315

OlyMPUS - The Ontology-based Metadata Portal for Unified Semantics

NASA Astrophysics Data System (ADS)

Huffer, E.; Gleason, J. L.

2015-12-01

The Ontology-based Metadata Portal for Unified Semantics (OlyMPUS), funded by the NASA Earth Science Technology Office Advanced Information Systems Technology program, is an end-to-end system designed to support data consumers and data providers, enabling the latter to register their data sets and provision them with the semantically rich metadata that drives the Ontology-Driven Interactive Search Environment for Earth Sciences (ODISEES). OlyMPUS leverages the semantics and reasoning capabilities of ODISEES to provide data producers with a semi-automated interface for producing the semantically rich metadata needed to support ODISEES' data discovery and access services. It integrates the ODISEES metadata search system with multiple NASA data delivery tools to enable data consumers to create customized data sets for download to their computers, or for NASA Advanced Supercomputing (NAS) facility registered users, directly to NAS storage resources for access by applications running on NAS supercomputers. A core function of NASA's Earth Science Division is research and analysis that uses the full spectrum of data products available in NASA archives. Scientists need to perform complex analyses that identify correlations and non-obvious relationships across all types of Earth System phenomena. Comprehensive analytics are hindered, however, by the fact that many Earth science data products are disparate and hard to synthesize. Variations in how data are collected, processed, gridded, and stored, create challenges for data interoperability and synthesis, which are exacerbated by the sheer volume of available data. Robust, semantically rich metadata can support tools for data discovery and facilitate machine-to-machine transactions with services such as data subsetting, regridding, and reformatting. Such capabilities are critical to enabling the research activities integral to NASA's strategic plans. However, as metadata requirements increase and competing standards emerge, metadata provisioning becomes increasingly burdensome to data producers. The OlyMPUS system helps data providers produce semantically rich metadata, making their data more accessible to data consumers, and helps data consumers quickly discover and download the right data for their research.

A Window into the Intoxicated Mind? Speech as an Index of Psychoactive Drug Effects

PubMed Central

Bedi, Gillinder; Cecchi, Guillermo A; Slezak, Diego F; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet

2014-01-01

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique ‘window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; ‘ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness. PMID:24694926

A window into the intoxicated mind? Speech as an index of psychoactive drug effects.

PubMed

Bedi, Gillinder; Cecchi, Guillermo A; Slezak, Diego F; Carrillo, Facundo; Sigman, Mariano; de Wit, Harriet

2014-09-01

Abused drugs can profoundly alter mental states in ways that may motivate drug use. These effects are usually assessed with self-report, an approach that is vulnerable to biases. Analyzing speech during intoxication may present a more direct, objective measure, offering a unique 'window' into the mind. Here, we employed computational analyses of speech semantic and topological structure after ±3,4-methylenedioxymethamphetamine (MDMA; 'ecstasy') and methamphetamine in 13 ecstasy users. In 4 sessions, participants completed a 10-min speech task after MDMA (0.75 and 1.5 mg/kg), methamphetamine (20 mg), or placebo. Latent Semantic Analyses identified the semantic proximity between speech content and concepts relevant to drug effects. Graph-based analyses identified topological speech characteristics. Group-level drug effects on semantic distances and topology were assessed. Machine-learning analyses (with leave-one-out cross-validation) assessed whether speech characteristics could predict drug condition in the individual subject. Speech after MDMA (1.5 mg/kg) had greater semantic proximity than placebo to the concepts friend, support, intimacy, and rapport. Speech on MDMA (0.75 mg/kg) had greater proximity to empathy than placebo. Conversely, speech on methamphetamine was further from compassion than placebo. Classifiers discriminated between MDMA (1.5 mg/kg) and placebo with 88% accuracy, and MDMA (1.5 mg/kg) and methamphetamine with 84% accuracy. For the two MDMA doses, the classifier performed at chance. These data suggest that automated semantic speech analyses can capture subtle alterations in mental state, accurately discriminating between drugs. The findings also illustrate the potential for automated speech-based approaches to characterize clinically relevant alterations to mental state, including those occurring in psychiatric illness.

Towards a semantic medical Web: HealthCyberMap's tool for building an RDF metadata base of health information resources based on the Qualified Dublin Core Metadata Set.

PubMed

Boulos, Maged N; Roudsari, Abdul V; Carson, Ewart R

2002-07-01

HealthCyberMap (http://healthcybermap.semanticweb.org/) aims at mapping Internet health information resources in novel ways for enhanced retrieval and navigation. This is achieved by collecting appropriate resource metadata in an unambiguous form that preserves semantics. We modelled a qualified Dublin Core (DC) metadata set ontology with extra elements for resource quality and geographical provenance in Prot g -2000. A metadata collection form helps acquiring resource instance data within Prot g . The DC subject field is populated with UMLS terms directly imported from UMLS Knowledge Source Server using UMLS tab, a Prot g -2000 plug-in. The project is saved in RDFS/RDF. The ontology and associated form serve as a free tool for building and maintaining an RDF medical resource metadata base. The UMLS tab enables browsing and searching for concepts that best describe a resource, and importing them to DC subject fields. The resultant metadata base can be used with a search and inference engine, and have textual and/or visual navigation interface(s) applied to it, to ultimately build a medical Semantic Web portal. Different ways of exploiting Prot g -2000 RDF output are discussed. By making the context and semantics of resources, not merely their raw text and formatting, amenable to computer 'understanding,' we can build a Semantic Web that is more useful to humans than the current Web. This requires proper use of metadata and ontologies. Clinical codes can reliably describe the subjects of medical resources, establish the semantic relationships (as defined by underlying coding scheme) between related resources, and automate their topical categorisation.

EXACT2: the semantics of biomedical protocols

PubMed Central

2014-01-01

Background The reliability and reproducibility of experimental procedures is a cornerstone of scientific practice. There is a pressing technological need for the better representation of biomedical protocols to enable other agents (human or machine) to better reproduce results. A framework that ensures that all information required for the replication of experimental protocols is essential to achieve reproducibility. Methods We have developed the ontology EXACT2 (EXperimental ACTions) that is designed to capture the full semantics of biomedical protocols required for their reproducibility. To construct EXACT2 we manually inspected hundreds of published and commercial biomedical protocols from several areas of biomedicine. After establishing a clear pattern for extracting the required information we utilized text-mining tools to translate the protocols into a machine amenable format. We have verified the utility of EXACT2 through the successful processing of previously 'unseen' (not used for the construction of EXACT2) protocols. Results The paper reports on a fundamentally new version EXACT2 that supports the semantically-defined representation of biomedical protocols. The ability of EXACT2 to capture the semantics of biomedical procedures was verified through a text mining use case. In this EXACT2 is used as a reference model for text mining tools to identify terms pertinent to experimental actions, and their properties, in biomedical protocols expressed in natural language. An EXACT2-based framework for the translation of biomedical protocols to a machine amenable format is proposed. Conclusions The EXACT2 ontology is sufficient to record, in a machine processable form, the essential information about biomedical protocols. EXACT2 defines explicit semantics of experimental actions, and can be used by various computer applications. It can serve as a reference model for for the translation of biomedical protocols in natural language into a semantically-defined format. PMID:25472549

Using Graph Components Derived from an Associative Concept Dictionary to Predict fMRI Neural Activation Patterns that Represent the Meaning of Nouns.

PubMed

Akama, Hiroyuki; Miyake, Maki; Jung, Jaeyoung; Murphy, Brian

2015-01-01

In this study, we introduce an original distance definition for graphs, called the Markov-inverse-F measure (MiF). This measure enables the integration of classical graph theory indices with new knowledge pertaining to structural feature extraction from semantic networks. MiF improves the conventional Jaccard and/or Simpson indices, and reconciles both the geodesic information (random walk) and co-occurrence adjustment (degree balance and distribution). We measure the effectiveness of graph-based coefficients through the application of linguistic graph information for a neural activity recorded during conceptual processing in the human brain. Specifically, the MiF distance is computed between each of the nouns used in a previous neural experiment and each of the in-between words in a subgraph derived from the Edinburgh Word Association Thesaurus of English. From the MiF-based information matrix, a machine learning model can accurately obtain a scalar parameter that specifies the degree to which each voxel in (the MRI image of) the brain is activated by each word or each principal component of the intermediate semantic features. Furthermore, correlating the voxel information with the MiF-based principal components, a new computational neurolinguistics model with a network connectivity paradigm is created. This allows two dimensions of context space to be incorporated with both semantic and neural distributional representations.

Quantifying narrative ability in autism spectrum disorder: a computational linguistic analysis of narrative coherence.

PubMed

Losh, Molly; Gordon, Peter C

2014-12-01

Autism is a neurodevelopmental disorder characterized by serious difficulties with the social use of language, along with impaired social functioning and ritualistic/repetitive behaviors (American Psychiatric Association in Diagnostic and statistical manual of mental disorders: DSM-5, 5th edn. American Psychiatric Association, Arlington, 2013). While substantial heterogeneity exists in symptom expression, impairments in language discourse skills, including narrative (or storytelling), are universally observed in autism (Tager-Flusberg et al. in Handbook on autism and pervasive developmental disorders, 3rd edn. Wiley, New York, pp 335-364, 2005). This study applied a computational linguistic tool, Latent Semantic Analysis (LSA), to objectively characterize narrative performance in high-functioning individuals with autism and typically-developing controls, across two different narrative contexts that differ in the interpersonal and cognitive demands placed on the narrator. Results indicated that high-functioning individuals with autism produced narratives comparable in semantic content to those produced by controls when narrating from a picture book, but produced narratives diminished in semantic quality in a more demanding narrative recall task. This pattern is similar to that detected from analyses of hand-coded picture book narratives in prior research, and extends findings to an additional narrative context that proves particularly challenging for individuals with autism. Results are discussed in terms of the utility of LSA as a quantitative, objective, and efficient measure of narrative ability.

Discovering gene annotations in biomedical text databases

PubMed Central

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-01-01

Background Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. Results In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. Conclusion GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values. PMID:18325104

Discovering gene annotations in biomedical text databases.

PubMed

Cakmak, Ali; Ozsoyoglu, Gultekin

2008-03-06

Genes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data. In this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products. In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general. GEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

A Computer-Aided Abstracting Tool Kit.

ERIC Educational Resources Information Center

Craven, Timothy C.

1993-01-01

Reports on the development of a prototype computerized abstractor's assistant called TEXNET, a text network management system. Features of the system discussed include semantic dependency links; displays of text structure; basic text editing; extracting; weighting methods; and listings of frequent words. (Contains 25 references.) (LRW)

Research in mathematical theory of computation. [computer programming applications

NASA Technical Reports Server (NTRS)

Mccarthy, J.

1973-01-01

Research progress in the following areas is reviewed: (1) new version of computer program LCF (logic for computable functions) including a facility to search for proofs automatically; (2) the description of the language PASCAL in terms of both LCF and in first order logic; (3) discussion of LISP semantics in LCF and attempt to prove the correctness of the London compilers in a formal way; (4) design of both special purpose and domain independent proving procedures specifically program correctness in mind; (5) design of languages for describing such proof procedures; and (6) the embedding of ideas in the first order checker.

ASP-based method for the enumeration of attractors in non-deterministic synchronous and asynchronous multi-valued networks.

PubMed

Ben Abdallah, Emna; Folschette, Maxime; Roux, Olivier; Magnin, Morgan

2017-01-01

This paper addresses the problem of finding attractors in biological regulatory networks. We focus here on non-deterministic synchronous and asynchronous multi-valued networks, modeled using automata networks (AN). AN is a general and well-suited formalism to study complex interactions between different components (genes, proteins,...). An attractor is a minimal trap domain, that is, a part of the state-transition graph that cannot be escaped. Such structures are terminal components of the dynamics and take the form of steady states (singleton) or complex compositions of cycles (non-singleton). Studying the effect of a disease or a mutation on an organism requires finding the attractors in the model to understand the long-term behaviors. We present a computational logical method based on answer set programming (ASP) to identify all attractors. Performed without any network reduction, the method can be applied on any dynamical semantics. In this paper, we present the two most widespread non-deterministic semantics: the asynchronous and the synchronous updating modes. The logical approach goes through a complete enumeration of the states of the network in order to find the attractors without the necessity to construct the whole state-transition graph. We realize extensive computational experiments which show good performance and fit the expected theoretical results in the literature. The originality of our approach lies on the exhaustive enumeration of all possible (sets of) states verifying the properties of an attractor thanks to the use of ASP. Our method is applied to non-deterministic semantics in two different schemes (asynchronous and synchronous). The merits of our methods are illustrated by applying them to biological examples of various sizes and comparing the results with some existing approaches. It turns out that our approach succeeds to exhaustively enumerate on a desktop computer, in a large model (100 components), all existing attractors up to a given size (20 states). This size is only limited by memory and computation time.

Knowledge-Based Environmental Context Modeling

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

A shortest-path graph kernel for estimating gene product semantic similarity.

PubMed

Alvarez, Marco A; Qi, Xiaojun; Yan, Changhui

2011-07-29

Existing methods for calculating semantic similarity between gene products using the Gene Ontology (GO) often rely on external resources, which are not part of the ontology. Consequently, changes in these external resources like biased term distribution caused by shifting of hot research topics, will affect the calculation of semantic similarity. One way to avoid this problem is to use semantic methods that are "intrinsic" to the ontology, i.e. independent of external knowledge. We present a shortest-path graph kernel (spgk) method that relies exclusively on the GO and its structure. In spgk, a gene product is represented by an induced subgraph of the GO, which consists of all the GO terms annotating it. Then a shortest-path graph kernel is used to compute the similarity between two graphs. In a comprehensive evaluation using a benchmark dataset, spgk compares favorably with other methods that depend on external resources. Compared with simUI, a method that is also intrinsic to GO, spgk achieves slightly better results on the benchmark dataset. Statistical tests show that the improvement is significant when the resolution and EC similarity correlation coefficient are used to measure the performance, but is insignificant when the Pfam similarity correlation coefficient is used. Spgk uses a graph kernel method in polynomial time to exploit the structure of the GO to calculate semantic similarity between gene products. It provides an alternative to both methods that use external resources and "intrinsic" methods with comparable performance.

New Semantic Learning in Patients With Large Medial Temporal Lobe Lesions

PubMed Central

Bayley, P.J.; O'Reilly, R.C.; Curran, T.; Squire, L.R.

2008-01-01

Two patients with large lesions of the medial temporal lobe were given four tests of semantic knowledge that could only have been acquired after the onset of their amnesia. In contrast to previous studies of postmorbid semantic learning, correct answers could be based on a simple, nonspecific sense of familiarity about single words, faces, or objects. According to recent computational models (for example, Norman and O'Reilly (2003) Psychol Rev 110:611–646), this characteristic should be optimal for detecting the kind of semantic learning that might be supported directly by the neocortex. Both patients exhibited some capacity for new learning, albeit at a level substantially below control performances. Notably, the correct answers appeared to reflect declarative memory. It was not the case that the correct answers simply popped out in some automatic way in the absence of any additional knowledge about the items. Rather, the few correct choices made by the patients tended to be accompanied by additional information about the chosen items, and the available knowledge appeared to be similar qualitatively to the kind of factual knowledge that healthy individuals gradually acquire over the years. The results are consistent with the idea that neocortical structures outside the medial temporal lobe are able to support some semantic learning, albeit to a very limited extent. Alternatively, the small amount of learning detected in the present study could depend on tissue within the posterior medial temporal lobe that remains intact in both patients. PMID:18306299

Meaning in the avian auditory cortex: Neural representation of communication calls

PubMed Central

Elie, Julie E; Theunissen, Frédéric E

2014-01-01

Understanding how the brain extracts the behavioral meaning carried by specific vocalization types that can be emitted by various vocalizers and in different conditions is a central question in auditory research. This semantic categorization is a fundamental process required for acoustic communication and presupposes discriminative and invariance properties of the auditory system for conspecific vocalizations. Songbirds have been used extensively to study vocal learning, but the communicative function of all their vocalizations and their neural representation has yet to be examined. In our research, we first generated a library containing almost the entire zebra finch vocal repertoire and organized communication calls along 9 different categories based on their behavioral meaning. We then investigated the neural representations of these semantic categories in the primary and secondary auditory areas of 6 anesthetized zebra finches. To analyze how single units encode these call categories, we described neural responses in terms of their discrimination, selectivity and invariance properties. Quantitative measures for these neural properties were obtained using an optimal decoder based both on spike counts and spike patterns. Information theoretic metrics show that almost half of the single units encode semantic information. Neurons achieve higher discrimination of these semantic categories by being more selective and more invariant. These results demonstrate that computations necessary for semantic categorization of meaningful vocalizations are already present in the auditory cortex and emphasize the value of a neuro-ethological approach to understand vocal communication. PMID:25728175

What does semantic tiling of the cortex tell us about semantics?

PubMed

Barsalou, Lawrence W

2017-10-01

Recent use of voxel-wise modeling in cognitive neuroscience suggests that semantic maps tile the cortex. Although this impressive research establishes distributed cortical areas active during the conceptual processing that underlies semantics, it tells us little about the nature of this processing. While mapping concepts between Marr's computational and implementation levels to support neural encoding and decoding, this approach ignores Marr's algorithmic level, central for understanding the mechanisms that implement cognition, in general, and conceptual processing, in particular. Following decades of research in cognitive science and neuroscience, what do we know so far about the representation and processing mechanisms that implement conceptual abilities? Most basically, much is known about the mechanisms associated with: (1) feature and frame representations, (2) grounded, abstract, and linguistic representations, (3) knowledge-based inference, (4) concept composition, and (5) conceptual flexibility. Rather than explaining these fundamental representation and processing mechanisms, semantic tiles simply provide a trace of their activity over a relatively short time period within a specific learning context. Establishing the mechanisms that implement conceptual processing in the brain will require more than mapping it to cortical (and sub-cortical) activity, with process models from cognitive science likely to play central roles in specifying the intervening mechanisms. More generally, neuroscience will not achieve its basic goals until it establishes algorithmic-level mechanisms that contribute essential explanations to how the brain works, going beyond simply establishing the brain areas that respond to various task conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Investigating the structure of semantic networks in low and high creative persons

PubMed Central

Kenett, Yoed N.; Anaki, David; Faust, Miriam

2014-01-01

According to Mednick's (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to each other by their association correlations—overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick's (1962) theory and the feasibility of using network science paradigms to investigate high level cognition. PMID:24959129

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology

PubMed Central

Zao, John K.; Gan, Tchin-Tze; You, Chun-Kai; Chung, Cheng-En; Wang, Yu-Te; Rodríguez Méndez, Sergio José; Mullen, Tim; Yu, Chieh; Kothe, Christian; Hsiao, Ching-Teng; Chu, San-Liang; Shieh, Ce-Kuen; Jung, Tzyy-Ping

2014-01-01

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system. PMID:24917804

Pervasive brain monitoring and data sharing based on multi-tier distributed computing and linked data technology.

PubMed

Zao, John K; Gan, Tchin-Tze; You, Chun-Kai; Chung, Cheng-En; Wang, Yu-Te; Rodríguez Méndez, Sergio José; Mullen, Tim; Yu, Chieh; Kothe, Christian; Hsiao, Ching-Teng; Chu, San-Liang; Shieh, Ce-Kuen; Jung, Tzyy-Ping

2014-01-01

EEG-based Brain-computer interfaces (BCI) are facing basic challenges in real-world applications. The technical difficulties in developing truly wearable BCI systems that are capable of making reliable real-time prediction of users' cognitive states in dynamic real-life situations may seem almost insurmountable at times. Fortunately, recent advances in miniature sensors, wireless communication and distributed computing technologies offered promising ways to bridge these chasms. In this paper, we report an attempt to develop a pervasive on-line EEG-BCI system using state-of-art technologies including multi-tier Fog and Cloud Computing, semantic Linked Data search, and adaptive prediction/classification models. To verify our approach, we implement a pilot system by employing wireless dry-electrode EEG headsets and MEMS motion sensors as the front-end devices, Android mobile phones as the personal user interfaces, compact personal computers as the near-end Fog Servers and the computer clusters hosted by the Taiwan National Center for High-performance Computing (NCHC) as the far-end Cloud Servers. We succeeded in conducting synchronous multi-modal global data streaming in March and then running a multi-player on-line EEG-BCI game in September, 2013. We are currently working with the ARL Translational Neuroscience Branch to use our system in real-life personal stress monitoring and the UCSD Movement Disorder Center to conduct in-home Parkinson's disease patient monitoring experiments. We shall proceed to develop the necessary BCI ontology and introduce automatic semantic annotation and progressive model refinement capability to our system.

Learning, Realizability and Games in Classical Arithmetic

NASA Astrophysics Data System (ADS)

Aschieri, Federico

2010-12-01

In this dissertation we provide mathematical evidence that the concept of learning can be used to give a new and intuitive computational semantics of classical proofs in various fragments of Predicative Arithmetic. First, we extend Kreisel modified realizability to a classical fragment of first order Arithmetic, Heyting Arithmetic plus EM1 (Excluded middle axiom restricted to Sigma^0_1 formulas). We introduce a new realizability semantics we call "Interactive Learning-Based Realizability". Our realizers are self-correcting programs, which learn from their errors and evolve through time. Secondly, we extend the class of learning based realizers to a classical version PCFclass of PCF and, then, compare the resulting notion of realizability with Coquand game semantics and prove a full soundness and completeness result. In particular, we show there is a one-to-one correspondence between realizers and recursive winning strategies in the 1-Backtracking version of Tarski games. Third, we provide a complete and fully detailed constructive analysis of learning as it arises in learning based realizability for HA+EM1, Avigad's update procedures and epsilon substitution method for Peano Arithmetic PA. We present new constructive techniques to bound the length of learning processes and we apply them to reprove - by means of our theory - the classic result of Godel that provably total functions of PA can be represented in Godel's system T. Last, we give an axiomatization of the kind of learning that is needed to computationally interpret Predicative classical second order Arithmetic. Our work is an extension of Avigad's and generalizes the concept of update procedure to the transfinite case. Transfinite update procedures have to learn values of transfinite sequences of non computable functions in order to extract witnesses from classical proofs.

A path-oriented knowledge representation system: Defusing the combinatorial system

NASA Technical Reports Server (NTRS)

Karamouzis, Stamos T.; Barry, John S.; Smith, Steven L.; Feyock, Stefan

1995-01-01

LIMAP is a programming system oriented toward efficient information manipulation over fixed finite domains, and quantification over paths and predicates. A generalization of Warshall's Algorithm to precompute paths in a sparse matrix representation of semantic nets is employed to allow questions involving paths between components to be posed and answered easily. LIMAP's ability to cache all paths between two components in a matrix cell proved to be a computational obstacle, however, when the semantic net grew to realistic size. The present paper describes a means of mitigating this combinatorial explosion to an extent that makes the use of the LIMAP representation feasible for problems of significant size. The technique we describe radically reduces the size of the search space in which LIMAP must operate; semantic nets of more than 500 nodes have been attacked successfully. Furthermore, it appears that the procedure described is applicable not only to LIMAP, but to a number of other combinatorially explosive search space problems found in AI as well.

A Large-Scale Analysis of Variance in Written Language.

PubMed

Johns, Brendan T; Jamieson, Randall K

2018-01-22

The collection of very large text sources has revolutionized the study of natural language, leading to the development of several models of language learning and distributional semantics that extract sophisticated semantic representations of words based on the statistical redundancies contained within natural language (e.g., Griffiths, Steyvers, & Tenenbaum, ; Jones & Mewhort, ; Landauer & Dumais, ; Mikolov, Sutskever, Chen, Corrado, & Dean, ). The models treat knowledge as an interaction of processing mechanisms and the structure of language experience. But language experience is often treated agnostically. We report a distributional semantic analysis that shows written language in fiction books varies appreciably between books from the different genres, books from the same genre, and even books written by the same author. Given that current theories assume that word knowledge reflects an interaction between processing mechanisms and the language environment, the analysis shows the need for the field to engage in a more deliberate consideration and curation of the corpora used in computational studies of natural language processing. Copyright © 2018 Cognitive Science Society, Inc.

RANDOMNESS of Numbers DEFINITION(QUERY:WHAT? V HOW?) ONLY Via MAXWELL-BOLTZMANN CLASSICAL-Statistics(MBCS) Hot-Plasma VS. Digits-Clumping Log-Law NON-Randomness Inversion ONLY BOSE-EINSTEIN QUANTUM-Statistics(BEQS) .

NASA Astrophysics Data System (ADS)

Siegel, Z.; Siegel, Edward Carl-Ludwig

2011-03-01

RANDOMNESS of Numbers cognitive-semantics DEFINITION VIA Cognition QUERY: WHAT???, NOT HOW?) VS. computer-``science" mindLESS number-crunching (Harrel-Sipser-...) algorithmics Goldreich "PSEUDO-randomness"[Not.AMS(02)] mea-culpa is ONLY via MAXWELL-BOLTZMANN CLASSICAL-STATISTICS(NOT FDQS!!!) "hot-plasma" REPULSION VERSUS Newcomb(1881)-Weyl(1914;1916)-Benford(1938) "NeWBe" logarithmic-law digit-CLUMPING/ CLUSTERING NON-Randomness simple Siegel[AMS Joint.Mtg.(02)-Abs. # 973-60-124] algebraic-inversion to THE QUANTUM and ONLY BEQS preferentially SEQUENTIALLY lower-DIGITS CLUMPING/CLUSTERING with d = 0 BEC, is ONLY VIA Siegel-Baez FUZZYICS=CATEGORYICS (SON OF TRIZ)/"Category-Semantics"(C-S), latter intersection/union of Lawvere(1964)-Siegel(1964)] category-theory (matrix: MORPHISMS V FUNCTORS) "+" cognitive-semantics'' (matrix: ANTONYMS V SYNONYMS) yields Siegel-Baez FUZZYICS=CATEGORYICS/C-S tabular list-format matrix truth-table analytics: MBCS RANDOMNESS TRUTH/EMET!!!

Automated geospatial Web Services composition based on geodata quality requirements

NASA Astrophysics Data System (ADS)

Cruz, Sérgio A. B.; Monteiro, Antonio M. V.; Santos, Rafael

2012-10-01

Service-Oriented Architecture and Web Services technologies improve the performance of activities involved in geospatial analysis with a distributed computing architecture. However, the design of the geospatial analysis process on this platform, by combining component Web Services, presents some open issues. The automated construction of these compositions represents an important research topic. Some approaches to solving this problem are based on AI planning methods coupled with semantic service descriptions. This work presents a new approach using AI planning methods to improve the robustness of the produced geospatial Web Services composition. For this purpose, we use semantic descriptions of geospatial data quality requirements in a rule-based form. These rules allow the semantic annotation of geospatial data and, coupled with the conditional planning method, this approach represents more precisely the situations of nonconformities with geodata quality that may occur during the execution of the Web Service composition. The service compositions produced by this method are more robust, thus improving process reliability when working with a composition of chained geospatial Web Services.

Incremental Query Rewriting with Resolution

NASA Astrophysics Data System (ADS)

Riazanov, Alexandre; Aragão, Marcelo A. T.

We address the problem of semantic querying of relational databases (RDB) modulo knowledge bases using very expressive knowledge representation formalisms, such as full first-order logic or its various fragments. We propose to use a resolution-based first-order logic (FOL) reasoner for computing schematic answers to deductive queries, with the subsequent translation of these schematic answers to SQL queries which are evaluated using a conventional relational DBMS. We call our method incremental query rewriting, because an original semantic query is rewritten into a (potentially infinite) series of SQL queries. In this chapter, we outline the main idea of our technique - using abstractions of databases and constrained clauses for deriving schematic answers, and provide completeness and soundness proofs to justify the applicability of this technique to the case of resolution for FOL without equality. The proposed method can be directly used with regular RDBs, including legacy databases. Moreover, we propose it as a potential basis for an efficient Web-scale semantic search technology.

Kernel Methods for Mining Instance Data in Ontologies

NASA Astrophysics Data System (ADS)

Bloehdorn, Stephan; Sure, York

The amount of ontologies and meta data available on the Web is constantly growing. The successful application of machine learning techniques for learning of ontologies from textual data, i.e. mining for the Semantic Web, contributes to this trend. However, no principal approaches exist so far for mining from the Semantic Web. We investigate how machine learning algorithms can be made amenable for directly taking advantage of the rich knowledge expressed in ontologies and associated instance data. Kernel methods have been successfully employed in various learning tasks and provide a clean framework for interfacing between non-vectorial data and machine learning algorithms. In this spirit, we express the problem of mining instances in ontologies as the problem of defining valid corresponding kernels. We present a principled framework for designing such kernels by means of decomposing the kernel computation into specialized kernels for selected characteristics of an ontology which can be flexibly assembled and tuned. Initial experiments on real world Semantic Web data enjoy promising results and show the usefulness of our approach.

A top-down manner-based DCNN architecture for semantic image segmentation.

PubMed

Qiao, Kai; Chen, Jian; Wang, Linyuan; Zeng, Lei; Yan, Bin

2017-01-01

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.

C-Speak Aphasia Alternative Communication Program for People with Severe Aphasia: Importance of Executive Functioning and Semantic Knowledge

PubMed Central

Nicholas, Marjorie; Sinotte, Michele P.; Helm-Estabrooks, Nancy

2011-01-01

Learning how to use a computer-based communication system can be challenging for people with severe aphasia even if the system is not word-based. This study explored cognitive and linguistic factors relative to how they affected individual patients’ ability to communicate expressively using C-Speak Aphasia, (CSA), an alternative communication computer program that is primarily picture-based. Ten individuals with severe non-fluent aphasia received at least six months of training with CSA. To assess carryover of training, untrained functional communication tasks (i.e., answering autobiographical questions, describing pictures, making telephone calls, describing a short video, and two writing tasks) were repeatedly probed in two conditions: 1) using CSA in addition to natural forms of communication, and 2) using only natural forms of communication, e.g., speaking, writing, gesturing, drawing. Four of the ten participants communicated more information on selected probe tasks using CSA than they did without the computer. Response to treatment also was examined in relation to baseline measures of non-linguistic executive function skills, pictorial semantic abilities, and auditory comprehension. Only nonlinguistic executive function skills were significantly correlated with treatment response. PMID:21506045

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

CaseLog: semantic network interface to a student computer-based patient record system.

PubMed Central

Cimino, C.; Goldman, E. K.; Curtis, J. A.; Reichgott, M. J.

1993-01-01

We have developed a computer program called CaseLog, which serves as an exemplary, computer-based patient record (CPR) system. The program allows for the introduction of the students to issues unique to patient record systems. These include record security, unique patient identifiers, and the use of controlled vocabularies. A particularly challenging aspect of the development of this program was allowing for student entry of controlled vocabulary terms. There were four goals we wished to achieve: students should be able to find the terms they are looking for; once a term has been found, it should be easy to find contextually related terms; it should be easy to determine that a sought-for term is not in the vocabulary; and the structure of the vocabulary should be dynamically altered by contextual information to allow its use for a variety of purposes. We chose a semantic network for our vocabulary structure. Within the processing power of the equipment we were working with, we achieved our goals. This paper will describe the development of the vocabulary, the design of the CaseLog program, and the feedback from student users of the program. PMID:8130581

Modular, Semantics-Based Composition of Biosimulation Models

ERIC Educational Resources Information Center

Neal, Maxwell Lewis

2010-01-01

Biosimulation models are valuable, versatile tools used for hypothesis generation and testing, codification of biological theory, education, and patient-specific modeling. Driven by recent advances in computational power and the accumulation of systems-level experimental data, modelers today are creating models with an unprecedented level of…

Explorations in Context Space: Words, Sentences, Discourse.

ERIC Educational Resources Information Center

Burgess, Curt; Livesay, Kay; Lund, Kevin

1998-01-01

Describes a computational model of high-dimensional context space: the Hyperspace Analog to Language (HAL). Shows that HAL provides sufficient information to make semantic, grammatical, and abstract distinctions. Demonstrates the cognitive compatibility of the representations with human processing; and introduces a new methodology that extracts…

What's So Hard about Understanding Language?

ERIC Educational Resources Information Center

Read, Walter; And Others

A discussion of the application of artificial intelligence to natural language processing looks at several problems in language comprehension, involving semantic ambiguity, anaphoric reference, and metonymy. Examples of these problems are cited, and the importance of the computational approach in analyzing them is explained. The approach applies…

Knowledge Representation: A Brief Review.

ERIC Educational Resources Information Center

Vickery, B. C.

1986-01-01

Reviews different structures and techniques of knowledge representation: structure of database records and files, data structures in computer programming, syntatic and semantic structure of natural language, knowledge representation in artificial intelligence, and models of human memory. A prototype expert system that makes use of some of these…

A vectorial semantics approach to personality assessment.

PubMed

Neuman, Yair; Cohen, Yochai

2014-04-23

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

Provenance Usage in the OceanLink Project

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Driver face tracking using semantics-based feature of eyes on single FPGA

NASA Astrophysics Data System (ADS)

Yu, Ying-Hao; Chen, Ji-An; Ting, Yi-Siang; Kwok, Ngaiming

2017-06-01

Tracking driver's face is one of the essentialities for driving safety control. This kind of system is usually designed with complicated algorithms to recognize driver's face by means of powerful computers. The design problem is not only about detecting rate but also from parts damages under rigorous environments by vibration, heat, and humidity. A feasible strategy to counteract these damages is to integrate entire system into a single chip in order to achieve minimum installation dimension, weight, power consumption, and exposure to air. Meanwhile, an extraordinary methodology is also indispensable to overcome the dilemma of low-computing capability and real-time performance on a low-end chip. In this paper, a novel driver face tracking system is proposed by employing semantics-based vague image representation (SVIR) for minimum hardware resource usages on a FPGA, and the real-time performance is also guaranteed at the same time. Our experimental results have indicated that the proposed face tracking system is viable and promising for the smart car design in the future.

A Vectorial Semantics Approach to Personality Assessment

NASA Astrophysics Data System (ADS)

Neuman, Yair; Cohen, Yochai

2014-04-01

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy.

Query optimization for graph analytics on linked data using SPARQL

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

Hong, Seokyong; Lee, Sangkeun; Lim, Seung -Hwan

2015-07-01

Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performancemore » of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.« less

Exchange of Computable Patient Data between the Department of Veterans Affairs (VA) and the Department of Defense (DoD): Terminology Mediation Strategy

PubMed Central

Bouhaddou, Omar; Warnekar, Pradnya; Parrish, Fola; Do, Nhan; Mandel, Jack; Kilbourne, John; Lincoln, Michael J.

2008-01-01

Complete patient health information that is available where and when it is needed is essential to providers and patients and improves healthcare quality and patient safety. VA and DoD have built on their previous experience in patient data exchange to establish data standards and terminology services to enable real-time bi-directional computable (i.e., encoded) data exchange and achieve semantic interoperability in compliance with recommended national standards and the eGov initiative. The project uses RxNorm, UMLS, and SNOMED CT terminology standards to mediate codified pharmacy and allergy data with greater than 92 and 60 percent success rates respectively. Implementation of the project has been well received by users and is being expanded to multiple joint care sites. Stable and mature standards, mediation strategies, and a close relationship between healthcare institutions and Standards Development Organizations are recommended to achieve and maintain semantic interoperability in a clinical setting. PMID:18096911

A Vectorial Semantics Approach to Personality Assessment

PubMed Central

Neuman, Yair; Cohen, Yochai

2014-01-01

Personality assessment and, specifically, the assessment of personality disorders have traditionally been indifferent to computational models. Computational personality is a new field that involves the automatic classification of individuals' personality traits that can be compared against gold-standard labels. In this context, we introduce a new vectorial semantics approach to personality assessment, which involves the construction of vectors representing personality dimensions and disorders, and the automatic measurements of the similarity between these vectors and texts written by human subjects. We evaluated our approach by using a corpus of 2468 essays written by students who were also assessed through the five-factor personality model. To validate our approach, we measured the similarity between the essays and the personality vectors to produce personality disorder scores. These scores and their correspondence with the subjects' classification of the five personality factors reproduce patterns well-documented in the psychological literature. In addition, we show that, based on the personality vectors, we can predict each of the five personality factors with high accuracy. PMID:24755833

A Contextual Information Acquisition Approach Based on Semantics and Mashup Technology

NASA Astrophysics Data System (ADS)

He, Yangfan; Li, Lu; He, Keqing; Chen, Xiuhong

Pay per use is an essential feature of cloud computing. Users can make use of some parts of a large scale service to satisfy their requirements, merely at the cost of a little payment. A good understanding of the users' requirement is a prerequisite for choosing the service in need precisely. Context implies users' potential requirements, which can be a complement to the requirements delivered explicitly. However, traditional context-aware computing research always demands some specific kinds of sensors to acquire contextual information, which renders a threshold too high for an application to become context-aware. This paper comes up with an approach which combines contextual information obtained directly and indirectly from the cloud services. Semantic relationship between different kinds of contexts lays foundation for the searching of the cloud services. And mashup technology is adopted to compose the heterogonous services. Abundant contextual information may lend strong support to a comprehensive understanding of users' context and a bettered abstraction of contextual requirements.

A computational model for simulating text comprehension.

PubMed

Lemaire, Benoît; Denhière, Guy; Bellissens, Cédrick; Jhean-Larose, Sandra

2006-11-01

In the present article, we outline the architecture of a computer program for simulating the process by which humans comprehend texts. The program is based on psycholinguistic theories about human memory and text comprehension processes, such as the construction-integration model (Kintsch, 1998), the latent semantic analysis theory of knowledge representation (Landauer & Dumais, 1997), and the predication algorithms (Kintsch, 2001; Lemaire & Bianco, 2003), and it is intended to help psycholinguists investigate the way humans comprehend texts.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

PubMed

Neal, Maxwell L; Carlson, Brian E; Thompson, Christopher T; James, Ryan C; Kim, Karam G; Tran, Kenneth; Crampin, Edmund J; Cook, Daniel L; Gennari, John H

2015-01-01

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen's semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the "Pandit-Hinch-Niederer" (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases

PubMed Central

Neal, Maxwell L.; Carlson, Brian E.; Thompson, Christopher T.; James, Ryan C.; Kim, Karam G.; Tran, Kenneth; Crampin, Edmund J.; Cook, Daniel L.; Gennari, John H.

2015-01-01

Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen’s semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the “Pandit-Hinch-Niederer” (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach. PMID:26716837

Semantic segmentation of mFISH images using convolutional networks.

PubMed

Pardo, Esteban; Morgado, José Mário T; Malpica, Norberto

2018-04-30

Multicolor in situ hybridization (mFISH) is a karyotyping technique used to detect major chromosomal alterations using fluorescent probes and imaging techniques. Manual interpretation of mFISH images is a time consuming step that can be automated using machine learning; in previous works, pixel or patch wise classification was employed, overlooking spatial information which can help identify chromosomes. In this work, we propose a fully convolutional semantic segmentation network for the interpretation of mFISH images, which uses both spatial and spectral information to classify each pixel in an end-to-end fashion. The semantic segmentation network developed was tested on samples extracted from a public dataset using cross validation. Despite having no labeling information of the image it was tested on, our algorithm yielded an average correct classification ratio (CCR) of 87.41%. Previously, this level of accuracy was only achieved with state of the art algorithms when classifying pixels from the same image in which the classifier has been trained. These results provide evidence that fully convolutional semantic segmentation networks may be employed in the computer aided diagnosis of genetic diseases with improved performance over the current image analysis methods. © 2018 International Society for Advancement of Cytometry. © 2018 International Society for Advancement of Cytometry.

Lesions to the left lateral prefrontal cortex impair decision threshold adjustment for lexical selection.

PubMed

Anders, Royce; Riès, Stéphanie; Van Maanen, Leendert; Alario, F-Xavier

Patients with lesions in the left prefrontal cortex (PFC) have been shown to be impaired in lexical selection, especially when interference between semantically related alternatives is increased. To more deeply investigate which computational mechanisms may be impaired following left PFC damage due to stroke, a psychometric modelling approach is employed in which we assess the cognitive parameters of the patients from an evidence accumulation (sequential information sampling) modelling of their response data. We also compare the results to healthy speakers. Analysis of the cognitive parameters indicates an impairment of the PFC patients to appropriately adjust their decision threshold, in order to handle the increased item difficulty that is introduced by semantic interference. Also, the modelling contributes to other topics in psycholinguistic theory, in which specific effects are observed on the cognitive parameters according to item familiarization, and the opposing effects of priming (lower threshold) and semantic interference (lower drift) which are found to depend on repetition. These results are developed for the blocked-cyclic picture naming paradigm, in which pictures are presented within semantically homogeneous (HOM) or heterogeneous (HET) blocks, and are repeated several times per block. Overall, the results are in agreement with a role of the left PFC in adjusting the decision threshold for lexical selection in language production.

Application of artifical intelligence principles to the analysis of "crazy" speech.

PubMed

Garfield, D A; Rapp, C

1994-04-01

Artificial intelligence computer simulation methods can be used to investigate psychotic or "crazy" speech. Here, symbolic reasoning algorithms establish semantic networks that schematize speech. These semantic networks consist of two main structures: case frames and object taxonomies. Node-based reasoning rules apply to object taxonomies and pathway-based reasoning rules apply to case frames. Normal listeners may recognize speech as "crazy talk" based on violations of node- and pathway-based reasoning rules. In this article, three separate segments of schizophrenic speech illustrate violations of these rules. This artificial intelligence approach is compared and contrasted with other neurolinguistic approaches and is discussed as a conceptual link between neurobiological and psychodynamic understandings of psychopathology.

Application of Semantic Tagging to Generate Superimposed Information on a Digital Encyclopedia

NASA Astrophysics Data System (ADS)

Garrido, Piedad; Tramullas, Jesus; Martinez, Francisco J.

We can find in the literature several works regarding the automatic or semi-automatic processing of textual documents with historic information using free software technologies. However, more research work is needed to integrate the analysis of the context and provide coverage to the peculiarities of the Spanish language from a semantic point of view. This research work proposes a novel knowledge-based strategy based on combining subject-centric computing, a topic-oriented approach, and superimposed information. It subsequent combination with artificial intelligence techniques led to an automatic analysis after implementing a made-to-measure interpreted algorithm which, in turn, produced a good number of associations and events with 90% reliability.

HTML5 microdata as a semantic container for medical information exchange.

PubMed

Kimura, Eizen; Kobayashi, Shinji; Ishihara, Ken

2014-01-01

Achieving interoperability between clinical electronic medical records (EMR) systems and cloud computing systems is challenging because of the lack of a universal reference method as a standard for information exchange with a secure connection. Here we describe an information exchange scheme using HTML5 microdata, where the standard semantic container is an HTML document. We embed HL7 messages describing laboratory test results in the microdata. We also annotate items in the clinical research report with the microdata. We mapped the laboratory test result data into the clinical research report using an HL7 selector specified in the microdata. This scheme can provide secure cooperation between the cloud-based service and the EMR system.

Geospatial-temporal semantic graph representations of trajectories from remote sensing and geolocation data

DOEpatents

Perkins, David Nikolaus; Brost, Randolph; Ray, Lawrence P.

2017-08-08

Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasets relative to moving objects such as vehicles.

Multiscale corner detection and classification using local properties and semantic patterns

NASA Astrophysics Data System (ADS)

Gallo, Giovanni; Giuoco, Alessandro L.

2002-05-01

A new technique to detect, localize and classify corners in digital closed curves is proposed. The technique is based on correct estimation of support regions for each point. We compute multiscale curvature to detect and to localize corners. As a further step, with the aid of some local features, it's possible to classify corners into seven distinct types. Classification is performed using a set of rules, which describe corners according to preset semantic patterns. Compared with existing techniques, the proposed approach inscribes itself into the family of algorithms that try to explain the curve, instead of simple labeling. Moreover, our technique works in manner similar to what is believed are typical mechanisms of human perception.

Automatic image enhancement based on multi-scale image decomposition

NASA Astrophysics Data System (ADS)

Feng, Lu; Wu, Zhuangzhi; Pei, Luo; Long, Xiong

2014-01-01

In image processing and computational photography, automatic image enhancement is one of the long-range objectives. Recently the automatic image enhancement methods not only take account of the globe semantics, like correct color hue and brightness imbalances, but also the local content of the image, such as human face and sky of landscape. In this paper we describe a new scheme for automatic image enhancement that considers both global semantics and local content of image. Our automatic image enhancement method employs the multi-scale edge-aware image decomposition approach to detect the underexposure regions and enhance the detail of the salient content. The experiment results demonstrate the effectiveness of our approach compared to existing automatic enhancement methods.

Computational Cognitive Neuroscience of Early Memory Development

ERIC Educational Resources Information Center

Munakata, Yuko

2004-01-01

Numerous brain areas work in concert to subserve memory, with distinct memory functions relying differentially on distinct brain areas. For example, semantic memory relies heavily on posterior cortical regions, episodic memory on hippocampal regions, and working memory on prefrontal cortical regions. This article reviews relevant findings from…

The Language of Man. Book 4.

ERIC Educational Resources Information Center

Littell, Joseph Fletcher, Ed.

Book 4 of "The Language of Man" series contains articles which deal with semantics, levels of language (including informal, formal and technical language, jargon, and gobbledygook), the hidden persuaders (advertising of merchandise and political candidates), and communications of the future (including the computer and other mass media now being…

Learned Vector-Space Models for Document Retrieval.

ERIC Educational Resources Information Center

Caid, William R.; And Others

1995-01-01

The Latent Semantic Indexing and MatchPlus systems examine similar contexts in which words appear and create representational models that capture the similarity of meaning of terms and then use the representation for retrieval. Text Retrieval Conference experiments using these systems demonstrate the computational feasibility of using…

Competencies in Organizational E-Learning: Concepts and Tools

ERIC Educational Resources Information Center

Sicilia, Miguel-Angel, Ed.

2007-01-01

"Competencies in Organizational E-Learning: Concepts and Tools" provides a comprehensive view of the way competencies can be used to drive organizational e-learning, including the main conceptual elements, competency gap analysis, advanced related computing topics, the application of semantic Web technologies, and the integration of competencies…

Implementing a frame representation in CLIPS/COOL

NASA Technical Reports Server (NTRS)

Myers, Leonard; Snyder, James

1991-01-01

An implementation is described and evaluated of frames in COOL. The test case is a frame based semantic network previously implemented in CLIPS (C Language Integrated Production System) Version 4.3 as part of the Intelligent Computer Aided Design System (ICADS) and reported at the first CLIPS conference.

Topics in Semantic Representation

ERIC Educational Resources Information Center

Griffiths, Thomas L.; Steyvers, Mark; Tenenbaum, Joshua B.

2007-01-01

Processing language requires the retrieval of concepts from memory in response to an ongoing stream of information. This retrieval is facilitated if one can infer the gist of a sentence, conversation, or document and use that gist to predict related concepts and disambiguate words. This article analyzes the abstract computational problem…

Automatically Grading Customer Confidence in a Formal Specification.

ERIC Educational Resources Information Center

Shukur, Zarina; Burke, Edmund; Foxley, Eric

1999-01-01

Describes an automatic grading system for a formal methods computer science course that is able to evaluate a formal specification written in the Z language. Quality is measured by considering first, specification correctness (syntax, semantics, and satisfaction of customer requirements), and second, specification maintainability (comparison of…

SD-Squared Revisited: Reply to Coltheart, Tree, and Saunders (2010)

ERIC Educational Resources Information Center

Woollams, Anna M.; Lambon Ralph, Matthew A.; Plaut, David C.; Patterson, Karalyn

2010-01-01

The connectionist triangle model of reading aloud proposes that semantic activation of phonology is particularly important for correct pronunciation of low-frequency exception words. Our consideration of this issue (Woollams, Lambon Ralph, Plaut, & Patterson, 2007) (see record 2007-05396-004) reported computational simulations demonstrating that…

Information-computational system for storage, search and analytical processing of environmental datasets based on the Semantic Web technologies

NASA Astrophysics Data System (ADS)

Titov, A.; Gordov, E.; Okladnikov, I.

2009-04-01

In this report the results of the work devoted to the development of working model of the software system for storage, semantically-enabled search and retrieval along with processing and visualization of environmental datasets containing results of meteorological and air pollution observations and mathematical climate modeling are presented. Specially designed metadata standard for machine-readable description of datasets related to meteorology, climate and atmospheric pollution transport domains is introduced as one of the key system components. To provide semantic interoperability the Resource Description Framework (RDF, http://www.w3.org/RDF/) technology means have been chosen for metadata description model realization in the form of RDF Schema. The final version of the RDF Schema is implemented on the base of widely used standards, such as Dublin Core Metadata Element Set (http://dublincore.org/), Directory Interchange Format (DIF, http://gcmd.gsfc.nasa.gov/User/difguide/difman.html), ISO 19139, etc. At present the system is available as a Web server (http://climate.risks.scert.ru/metadatabase/) based on the web-portal ATMOS engine [1] and is implementing dataset management functionality including SeRQL-based semantic search as well as statistical analysis and visualization of selected data archives [2,3]. The core of the system is Apache web server in conjunction with Tomcat Java Servlet Container (http://jakarta.apache.org/tomcat/) and Sesame Server (http://www.openrdf.org/) used as a database for RDF and RDF Schema. At present statistical analysis of meteorological and climatic data with subsequent visualization of results is implemented for such datasets as NCEP/NCAR Reanalysis, Reanalysis NCEP/DOE AMIP II, JMA/CRIEPI JRA-25, ECMWF ERA-40 and local measurements obtained from meteorological stations on the territory of Russia. This functionality is aimed primarily at finding of main characteristics of regional climate dynamics. The proposed system represents a step in the process of development of a distributed collaborative information-computational environment to support multidisciplinary investigations of Earth regional environment [4]. Partial support of this work by SB RAS Integration Project 34, SB RAS Basic Program Project 4.5.2.2, APN Project CBA2007-08NSY and FP6 Enviro-RISKS project (INCO-CT-2004-013427) is acknowledged. References 1. E.P. Gordov, V.N. Lykosov, and A.Z. Fazliev. Web portal on environmental sciences "ATMOS" // Advances in Geosciences. 2006. Vol. 8. p. 33 - 38. 2. Gordov E.P., Okladnikov I.G., Titov A.G. Development of elements of web based information-computational system supporting regional environment processes investigations // Journal of Computational Technologies, Vol. 12, Special Issue #3, 2007, pp. 20 - 28. 3. Okladnikov I.G., Titov A.G. Melnikova V.N., Shulgina T.M. Web-system for processing and visualization of meteorological and climatic data // Journal of Computational Technologies, Vol. 13, Special Issue #3, 2008, pp. 64 - 69. 4. Gordov E.P., Lykosov V.N. Development of information-computational infrastructure for integrated study of Siberia environment // Journal of Computational Technologies, Vol. 12, Special Issue #2, 2007, pp. 19 - 30.

Extended Full Computation-Tree Logic with Sequence Modal Operator: Representing Hierarchical Tree Structures

NASA Astrophysics Data System (ADS)

Kamide, Norihiro; Kaneiwa, Ken

An extended full computation-tree logic, CTLS*, is introduced as a Kripke semantics with a sequence modal operator. This logic can appropriately represent hierarchical tree structures where sequence modal operators in CTLS* are applied to tree structures. An embedding theorem of CTLS* into CTL* is proved. The validity, satisfiability and model-checking problems of CTLS* are shown to be decidable. An illustrative example of biological taxonomy is presented using CTLS* formulas.

Top-down methodology for human factors research

NASA Technical Reports Server (NTRS)

Sibert, J.

1983-01-01

User computer interaction as a conversation is discussed. The design of user interfaces which depends on viewing communications between a user and the computer as a conversion is presented. This conversation includes inputs to the computer (outputs from the user), outputs from the computer (inputs to the user), and the sequencing in both time and space of those outputs and inputs. The conversation is viewed from the user's side of the conversation. Two languages are modeled: the one with which the user communicates with the computer and the language where communication flows from the computer to the user. Both languages exist on three levels; the semantic, syntactic and lexical. It is suggested that natural languages can also be considered in these terms.

Knowledge-based personalized search engine for the Web-based Human Musculoskeletal System Resources (HMSR) in biomechanics.

PubMed

Dao, Tien Tuan; Hoang, Tuan Nha; Ta, Xuan Hien; Tho, Marie Christine Ho Ba

2013-02-01

Human musculoskeletal system resources of the human body are valuable for the learning and medical purposes. Internet-based information from conventional search engines such as Google or Yahoo cannot response to the need of useful, accurate, reliable and good-quality human musculoskeletal resources related to medical processes, pathological knowledge and practical expertise. In this present work, an advanced knowledge-based personalized search engine was developed. Our search engine was based on a client-server multi-layer multi-agent architecture and the principle of semantic web services to acquire dynamically accurate and reliable HMSR information by a semantic processing and visualization approach. A security-enhanced mechanism was applied to protect the medical information. A multi-agent crawler was implemented to develop a content-based database of HMSR information. A new semantic-based PageRank score with related mathematical formulas were also defined and implemented. As the results, semantic web service descriptions were presented in OWL, WSDL and OWL-S formats. Operational scenarios with related web-based interfaces for personal computers and mobile devices were presented and analyzed. Functional comparison between our knowledge-based search engine, a conventional search engine and a semantic search engine showed the originality and the robustness of our knowledge-based personalized search engine. In fact, our knowledge-based personalized search engine allows different users such as orthopedic patient and experts or healthcare system managers or medical students to access remotely into useful, accurate, reliable and good-quality HMSR information for their learning and medical purposes. Copyright © 2012 Elsevier Inc. All rights reserved.

Cooking "shrimp à la créole": a pilot study of an ecological rehabilitation in semantic dementia.

PubMed

Bier, Nathalie; Macoir, Joël; Joubert, Sven; Bottari, Carolina; Chayer, Céline; Pigot, Hélène; Giroux, Sylvain

2011-08-01

New learning in semantic dementia (SD) seems to be tied to a specific temporal and spatial context. Thus, cognitive rehabilitation could capitalise upon preserved episodic memory and focus on everyday activities which, once learned, will have an impact in everyday life. This pilot study thus explores the effectiveness of an ecological approach in one patient suffering from SD. EC, a 68-year-old woman with SD, stopped cooking complex meals due to a substantial loss of knowledge related to all food types. The therapy consisted of preparing a target recipe. She was asked to generate semantic attributes of ingredients found in one target, one control and two no-therapy recipes. The number of recipes cooked by EC between therapy sessions was computed. She was also asked to prepare a generalisation recipe combining ingredients from the target and control recipes. EC's generated semantic attributes (GSA) of ingredients pertaining to the target and control recipes increased significantly (p < .001), compared to the no-therapy recipes (ps > .79). The proportion of meals cooked also increased significantly (p = .021). For the generalisation recipe, she could not succeed without assistance. Frequent food preparation may have provided EC with new memories about the context, usage and appearance of some concepts. These memories seem very context-bound, but EC nonetheless re-introduced some recipes into her day-to-day life. The impact of these results on the relationship between semantic, episodic and procedural memory is discussed, as well as the relevance of an ecological approach in SD.

Semantics based approach for analyzing disease-target associations.

PubMed

Kaalia, Rama; Ghosh, Indira

2016-08-01

A complex disease is caused by heterogeneous biological interactions between genes and their products along with the influence of environmental factors. There have been many attempts for understanding the cause of these diseases using experimental, statistical and computational methods. In the present work the objective is to address the challenge of representation and integration of information from heterogeneous biomedical aspects of a complex disease using semantics based approach. Semantic web technology is used to design Disease Association Ontology (DAO-db) for representation and integration of disease associated information with diabetes as the case study. The functional associations of disease genes are integrated using RDF graphs of DAO-db. Three semantic web based scoring algorithms (PageRank, HITS (Hyperlink Induced Topic Search) and HITS with semantic weights) are used to score the gene nodes on the basis of their functional interactions in the graph. Disease Association Ontology for Diabetes (DAO-db) provides a standard ontology-driven platform for describing genes, proteins, pathways involved in diabetes and for integrating functional associations from various interaction levels (gene-disease, gene-pathway, gene-function, gene-cellular component and protein-protein interactions). An automatic instance loader module is also developed in present work that helps in adding instances to DAO-db on a large scale. Our ontology provides a framework for querying and analyzing the disease associated information in the form of RDF graphs. The above developed methodology is used to predict novel potential targets involved in diabetes disease from the long list of loose (statistically associated) gene-disease associations. Copyright © 2016 Elsevier Inc. All rights reserved.

Speaking two "Languages" in America: A semantic space analysis of how presidential candidates and their supporters represent abstract political concepts differently.

PubMed

Li, Ping; Schloss, Benjamin; Follmer, D Jake

2017-10-01

In this article we report a computational semantic analysis of the presidential candidates' speeches in the two major political parties in the USA. In Study One, we modeled the political semantic spaces as a function of party, candidate, and time of election, and findings revealed patterns of differences in the semantic representation of key political concepts and the changing landscapes in which the presidential candidates align or misalign with their parties in terms of the representation and organization of politically central concepts. Our models further showed that the 2016 US presidential nominees had distinct conceptual representations from those of previous election years, and these patterns did not necessarily align with their respective political parties' average representation of the key political concepts. In Study Two, structural equation modeling demonstrated that reported political engagement among voters differentially predicted reported likelihoods of voting for Clinton versus Trump in the 2016 presidential election. Study Three indicated that Republicans and Democrats showed distinct, systematic word association patterns for the same concepts/terms, which could be reliably distinguished using machine learning methods. These studies suggest that given an individual's political beliefs, we can make reliable predictions about how they understand words, and given how an individual understands those same words, we can also predict an individual's political beliefs. Our study provides a bridge between semantic space models and abstract representations of political concepts on the one hand, and the representations of political concepts and citizens' voting behavior on the other.

Automated analysis of free speech predicts psychosis onset in high-risk youths

PubMed Central

Bedi, Gillinder; Carrillo, Facundo; Cecchi, Guillermo A; Slezak, Diego Fernández; Sigman, Mariano; Mota, Natália B; Ribeiro, Sidarta; Javitt, Daniel C; Copelli, Mauro; Corcoran, Cheryl M

2015-01-01

Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry. PMID:27336038

Aesthetic perception of visual textures: a holistic exploration using texture analysis, psychological experiment, and perception modeling.

PubMed

Liu, Jianli; Lughofer, Edwin; Zeng, Xianyi

2015-01-01

Modeling human aesthetic perception of visual textures is important and valuable in numerous industrial domains, such as product design, architectural design, and decoration. Based on results from a semantic differential rating experiment, we modeled the relationship between low-level basic texture features and aesthetic properties involved in human aesthetic texture perception. First, we compute basic texture features from textural images using four classical methods. These features are neutral, objective, and independent of the socio-cultural context of the visual textures. Then, we conduct a semantic differential rating experiment to collect from evaluators their aesthetic perceptions of selected textural stimuli. In semantic differential rating experiment, eights pairs of aesthetic properties are chosen, which are strongly related to the socio-cultural context of the selected textures and to human emotions. They are easily understood and connected to everyday life. We propose a hierarchical feed-forward layer model of aesthetic texture perception and assign 8 pairs of aesthetic properties to different layers. Finally, we describe the generation of multiple linear and non-linear regression models for aesthetic prediction by taking dimensionality-reduced texture features and aesthetic properties of visual textures as dependent and independent variables, respectively. Our experimental results indicate that the relationships between each layer and its neighbors in the hierarchical feed-forward layer model of aesthetic texture perception can be fitted well by linear functions, and the models thus generated can successfully bridge the gap between computational texture features and aesthetic texture properties.

Evolvix BEST Names for semantic reproducibility across code2brain interfaces.

PubMed

Loewe, Laurence; Scheuer, Katherine S; Keel, Seth A; Vyas, Vaibhav; Liblit, Ben; Hanlon, Bret; Ferris, Michael C; Yin, John; Dutra, Inês; Pietsch, Anthony; Javid, Christine G; Moog, Cecilia L; Meyer, Jocelyn; Dresel, Jerdon; McLoone, Brian; Loberger, Sonya; Movaghar, Arezoo; Gilchrist-Scott, Morgaine; Sabri, Yazeed; Sescleifer, Dave; Pereda-Zorrilla, Ivan; Zietlow, Andrew; Smith, Rodrigo; Pietenpol, Samantha; Goldfinger, Jacob; Atzen, Sarah L; Freiberg, Erika; Waters, Noah P; Nusbaum, Claire; Nolan, Erik; Hotz, Alyssa; Kliman, Richard M; Mentewab, Ayalew; Fregien, Nathan; Loewe, Martha

2017-01-01

Names in programming are vital for understanding the meaning of code and big data. We define code2brain (C2B) interfaces as maps in compilers and brains between meaning and naming syntax, which help to understand executable code. While working toward an Evolvix syntax for general-purpose programming that makes accurate modeling easy for biologists, we observed how names affect C2B quality. To protect learning and coding investments, C2B interfaces require long-term backward compatibility and semantic reproducibility (accurate reproduction of computational meaning from coder-brains to reader-brains by code alone). Semantic reproducibility is often assumed until confusing synonyms degrade modeling in biology to deciphering exercises. We highlight empirical naming priorities from diverse individuals and roles of names in different modes of computing to show how naming easily becomes impossibly difficult. We present the Evolvix BEST (Brief, Explicit, Summarizing, Technical) Names concept for reducing naming priority conflicts, test it on a real challenge by naming subfolders for the Project Organization Stabilizing Tool system, and provide naming questionnaires designed to facilitate C2B debugging by improving names used as keywords in a stabilizing programming language. Our experiences inspired us to develop Evolvix using a flipped programming language design approach with some unexpected features and BEST Names at its core. © 2016 The Authors. Annals of the New York Academy of Sciences published by Wiley Periodicals, Inc. on behalf of New York Academy of Sciences.

Educational Software for First Order Logic Semantics in Introductory Logic Courses

ERIC Educational Resources Information Center

Mauco, María Virginia; Ferrante, Enzo; Felice, Laura

2014-01-01

Basic courses on logic are common in most computer science curricula. Students often have difficulties in handling formalisms and getting familiar with them. Educational software helps to motivate and improve the teaching-learning processes. Therefore, incorporating these kinds of tools becomes important, because they contribute to gaining…

Hierarchy and Scope of Planning in Subject-Verb Agreement Production

ERIC Educational Resources Information Center

Gillespie, Maureen; Pearlmutter, Neal J.

2011-01-01

Two subject-verb agreement error elicitation studies tested the hierarchical feature-passing account of agreement computation in production and three timing-based alternatives: linear distance to the head noun, semantic integration, and a combined effect of both (a scope of planning account). In Experiment 1, participants completed subject noun…

A Gene Ontology Tutorial in Python.

PubMed

Vesztrocy, Alex Warwick; Dessimoz, Christophe

2017-01-01

This chapter is a tutorial on using Gene Ontology resources in the Python programming language. This entails querying the Gene Ontology graph, retrieving Gene Ontology annotations, performing gene enrichment analyses, and computing basic semantic similarity between GO terms. An interactive version of the tutorial, including solutions, is available at http://gohandbook.org .

Designing Distance Learning Tasks to Help Maximize Vocabulary Development

ERIC Educational Resources Information Center

Loucky, John Paul

2012-01-01

Task-based language learning using the benefits of online computer-assisted language learning (CALL) can be effective for rapid vocabulary expansion, especially when target vocabulary has been pre-arranged into bilingual categories under simpler, common Semantic Field Keywords. Results and satisfaction levels for both Chinese English majors and…

Automatic Summary Assessment for Intelligent Tutoring Systems

ERIC Educational Resources Information Center

He, Yulan; Hui, Siu Cheung; Quan, Tho Thanh

2009-01-01

Summary writing is an important part of many English Language Examinations. As grading students' summary writings is a very time-consuming task, computer-assisted assessment will help teachers carry out the grading more effectively. Several techniques such as latent semantic analysis (LSA), n-gram co-occurrence and BLEU have been proposed to…

A Computational Model of Semantic Memory Impairment: Modality- Specificity and Emergent Category-Specificity

DTIC Science & Technology

1991-09-01

just one modality (e.g. visual or auditory agnosia ) or impaired manipulation of objects with specific uses, despite intact recognition of them (apraxia...Neurosurgery and itbiatzy, 51, 1201-1207. Farah, M. J. (1991) Patterns of co-occurence among the associative agnosias : Implications for visual object

The Mechanism of Restructuring in Geometry

DTIC Science & Technology

1990-05-01

geometric problem solving (Technical Report No. 353). Uppsala, Sweden: Department of Psychology , University of Uppsala. UNCLASSIFIED SECURITY...these questions: Psychological experiments, protocol studies, computer simulations, historical studies, semantic, logical, and mathematical analyses...triangle are congruent, then their opposite angles are congruent; and vice versa. Method Three undergraduate psychology students participated in an

Semantics vs. World Knowledge in Prefrontal Cortex

ERIC Educational Resources Information Center

Pylkkanen, Liina; Oliveri, Bridget; Smart, Andrew J.

2009-01-01

Humans have knowledge about the properties of their native language at various levels of representation; sound, structure, and meaning computation constitute the core components of any linguistic theory. Although the brain sciences have engaged with representational theories of sound and syntactic structure, the study of the neural bases of…

Concepts as Semantic Pointers: A Framework and Computational Model

ERIC Educational Resources Information Center

Blouw, Peter; Solodkin, Eugene; Thagard, Paul; Eliasmith, Chris

2016-01-01

The reconciliation of theories of concepts based on prototypes, exemplars, and theory-like structures is a longstanding problem in cognitive science. In response to this problem, researchers have recently tended to adopt either hybrid theories that combine various kinds of representational structure, or eliminative theories that replace concepts…

English Complex Verb Constructions: Identification and Inference

ERIC Educational Resources Information Center

Tu, Yuancheng

2012-01-01

The fundamental problem faced by automatic text understanding in Natural Language Processing (NLP) is to identify semantically related pieces of text and integrate them together to compute the meaning of the whole text. However, the principle of compositionality runs into trouble very quickly when real language is examined with its frequent…

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…

A semantic problem solving environment for integrative parasite research: identification of intervention targets for Trypanosoma cruzi.

PubMed

Parikh, Priti P; Minning, Todd A; Nguyen, Vinh; Lalithsena, Sarasi; Asiaee, Amir H; Sahoo, Satya S; Doshi, Prashant; Tarleton, Rick; Sheth, Amit P

2012-01-01

Research on the biology of parasites requires a sophisticated and integrated computational platform to query and analyze large volumes of data, representing both unpublished (internal) and public (external) data sources. Effective analysis of an integrated data resource using knowledge discovery tools would significantly aid biologists in conducting their research, for example, through identifying various intervention targets in parasites and in deciding the future direction of ongoing as well as planned projects. A key challenge in achieving this objective is the heterogeneity between the internal lab data, usually stored as flat files, Excel spreadsheets or custom-built databases, and the external databases. Reconciling the different forms of heterogeneity and effectively integrating data from disparate sources is a nontrivial task for biologists and requires a dedicated informatics infrastructure. Thus, we developed an integrated environment using Semantic Web technologies that may provide biologists the tools for managing and analyzing their data, without the need for acquiring in-depth computer science knowledge. We developed a semantic problem-solving environment (SPSE) that uses ontologies to integrate internal lab data with external resources in a Parasite Knowledge Base (PKB), which has the ability to query across these resources in a unified manner. The SPSE includes Web Ontology Language (OWL)-based ontologies, experimental data with its provenance information represented using the Resource Description Format (RDF), and a visual querying tool, Cuebee, that features integrated use of Web services. We demonstrate the use and benefit of SPSE using example queries for identifying gene knockout targets of Trypanosoma cruzi for vaccine development. Answers to these queries involve looking up multiple sources of data, linking them together and presenting the results. The SPSE facilitates parasitologists in leveraging the growing, but disparate, parasite data resources by offering an integrative platform that utilizes Semantic Web techniques, while keeping their workload increase minimal.

Can social semantic web techniques foster collaborative curriculum mapping in medicine?

PubMed

Spreckelsen, Cord; Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-08-15

Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives ("LOs"). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 ("good") in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system's ability to support curriculum revision. The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems.

Querying phenotype-genotype relationships on patient datasets using semantic web technology: the example of Cerebrotendinous xanthomatosis.

PubMed

Taboada, María; Martínez, Diego; Pilo, Belén; Jiménez-Escrig, Adriano; Robinson, Peter N; Sobrido, María J

2012-07-31

Semantic Web technology can considerably catalyze translational genetics and genomics research in medicine, where the interchange of information between basic research and clinical levels becomes crucial. This exchange involves mapping abstract phenotype descriptions from research resources, such as knowledge databases and catalogs, to unstructured datasets produced through experimental methods and clinical practice. This is especially true for the construction of mutation databases. This paper presents a way of harmonizing abstract phenotype descriptions with patient data from clinical practice, and querying this dataset about relationships between phenotypes and genetic variants, at different levels of abstraction. Due to the current availability of ontological and terminological resources that have already reached some consensus in biomedicine, a reuse-based ontology engineering approach was followed. The proposed approach uses the Ontology Web Language (OWL) to represent the phenotype ontology and the patient model, the Semantic Web Rule Language (SWRL) to bridge the gap between phenotype descriptions and clinical data, and the Semantic Query Web Rule Language (SQWRL) to query relevant phenotype-genotype bidirectional relationships. The work tests the use of semantic web technology in the biomedical research domain named cerebrotendinous xanthomatosis (CTX), using a real dataset and ontologies. A framework to query relevant phenotype-genotype bidirectional relationships is provided. Phenotype descriptions and patient data were harmonized by defining 28 Horn-like rules in terms of the OWL concepts. In total, 24 patterns of SWQRL queries were designed following the initial list of competency questions. As the approach is based on OWL, the semantic of the framework adapts the standard logical model of an open world assumption. This work demonstrates how semantic web technologies can be used to support flexible representation and computational inference mechanisms required to query patient datasets at different levels of abstraction. The open world assumption is especially good for describing only partially known phenotype-genotype relationships, in a way that is easily extensible. In future, this type of approach could offer researchers a valuable resource to infer new data from patient data for statistical analysis in translational research. In conclusion, phenotype description formalization and mapping to clinical data are two key elements for interchanging knowledge between basic and clinical research.

A model for the control mode man-computer interface dialogue

NASA Technical Reports Server (NTRS)

Chafin, R. L.

1981-01-01

A four stage model is presented for the control mode man-computer interface dialogue. It consists of context development, semantic development syntactic development, and command execution. Each stage is discussed in terms of the operator skill levels (naive, novice, competent, and expert) and pertinent human factors issues. These issues are human problem solving, human memory, and schemata. The execution stage is discussed in terms of the operators typing skills. This model provides an understanding of the human process in command mode activity for computer systems and a foundation for relating system characteristics to operator characteristics.

A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.

PubMed

Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev

2010-01-01

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming distance using the learned binary representation. A boosting algorithm is presented to efficiently learn the distance function. We evaluate the proposed algorithm on a mammographic image reference library with an Interactive Search-Assisted Decision Support (ISADS) system and on the medical image data set from ImageCLEF. Our results show that the boosting framework compares favorably to state-of-the-art approaches for distance metric learning in retrieval accuracy, with much lower computational cost. Additional evaluation with the COREL collection shows that our algorithm works well for regular image data sets.

Mining integrated semantic networks for drug repositioning opportunities

PubMed Central

Mullen, Joseph; Tipney, Hannah

2016-01-01

Current research and development approaches to drug discovery have become less fruitful and more costly. One alternative paradigm is that of drug repositioning. Many marketed examples of repositioned drugs have been identified through serendipitous or rational observations, highlighting the need for more systematic methodologies to tackle the problem. Systems level approaches have the potential to enable the development of novel methods to understand the action of therapeutic compounds, but requires an integrative approach to biological data. Integrated networks can facilitate systems level analyses by combining multiple sources of evidence to provide a rich description of drugs, their targets and their interactions. Classically, such networks can be mined manually where a skilled person is able to identify portions of the graph (semantic subgraphs) that are indicative of relationships between drugs and highlight possible repositioning opportunities. However, this approach is not scalable. Automated approaches are required to systematically mine integrated networks for these subgraphs and bring them to the attention of the user. We introduce a formal framework for the definition of integrated networks and their associated semantic subgraphs for drug interaction analysis and describe DReSMin, an algorithm for mining semantically-rich networks for occurrences of a given semantic subgraph. This algorithm allows instances of complex semantic subgraphs that contain data about putative drug repositioning opportunities to be identified in a computationally tractable fashion, scaling close to linearly with network data. We demonstrate the utility of our approach by mining an integrated drug interaction network built from 11 sources. This work identified and ranked 9,643,061 putative drug-target interactions, showing a strong correlation between highly scored associations and those supported by literature. We discuss the 20 top ranked associations in more detail, of which 14 are novel and 6 are supported by the literature. We also show that our approach better prioritizes known drug-target interactions, than other state-of-the art approaches for predicting such interactions. PMID:26844016

The ontology-based answers (OBA) service: a connector for embedded usage of ontologies in applications.

PubMed

Dönitz, Jürgen; Wingender, Edgar

2012-01-01

The semantic web depends on the use of ontologies to let electronic systems interpret contextual information. Optimally, the handling and access of ontologies should be completely transparent to the user. As a means to this end, we have developed a service that attempts to bridge the gap between experts in a certain knowledge domain, ontologists, and application developers. The ontology-based answers (OBA) service introduced here can be embedded into custom applications to grant access to the classes of ontologies and their relations as most important structural features as well as to information encoded in the relations between ontology classes. Thus computational biologists can benefit from ontologies without detailed knowledge about the respective ontology. The content of ontologies is mapped to a graph of connected objects which is compatible to the object-oriented programming style in Java. Semantic functions implement knowledge about the complex semantics of an ontology beyond the class hierarchy and "partOf" relations. By using these OBA functions an application can, for example, provide a semantic search function, or (in the examples outlined) map an anatomical structure to the organs it belongs to. The semantic functions relieve the application developer from the necessity of acquiring in-depth knowledge about the semantics and curation guidelines of the used ontologies by implementing the required knowledge. The architecture of the OBA service encapsulates the logic to process ontologies in order to achieve a separation from the application logic. A public server with the current plugins is available and can be used with the provided connector in a custom application in scenarios analogous to the presented use cases. The server and the client are freely available if a project requires the use of custom plugins or non-public ontologies. The OBA service and further documentation is available at http://www.bioinf.med.uni-goettingen.de/projects/oba.

The ontology-based answers (OBA) service: a connector for embedded usage of ontologies in applications

PubMed Central

Dönitz, Jürgen; Wingender, Edgar

2012-01-01

The semantic web depends on the use of ontologies to let electronic systems interpret contextual information. Optimally, the handling and access of ontologies should be completely transparent to the user. As a means to this end, we have developed a service that attempts to bridge the gap between experts in a certain knowledge domain, ontologists, and application developers. The ontology-based answers (OBA) service introduced here can be embedded into custom applications to grant access to the classes of ontologies and their relations as most important structural features as well as to information encoded in the relations between ontology classes. Thus computational biologists can benefit from ontologies without detailed knowledge about the respective ontology. The content of ontologies is mapped to a graph of connected objects which is compatible to the object-oriented programming style in Java. Semantic functions implement knowledge about the complex semantics of an ontology beyond the class hierarchy and “partOf” relations. By using these OBA functions an application can, for example, provide a semantic search function, or (in the examples outlined) map an anatomical structure to the organs it belongs to. The semantic functions relieve the application developer from the necessity of acquiring in-depth knowledge about the semantics and curation guidelines of the used ontologies by implementing the required knowledge. The architecture of the OBA service encapsulates the logic to process ontologies in order to achieve a separation from the application logic. A public server with the current plugins is available and can be used with the provided connector in a custom application in scenarios analogous to the presented use cases. The server and the client are freely available if a project requires the use of custom plugins or non-public ontologies. The OBA service and further documentation is available at http://www.bioinf.med.uni-goettingen.de/projects/oba PMID:23060901

High-Level Data-Abstraction System

NASA Technical Reports Server (NTRS)

Fishwick, P. A.

1986-01-01

Communication with data-base processor flexible and efficient. High Level Data Abstraction (HILDA) system is three-layer system supporting data-abstraction features of Intel data-base processor (DBP). Purpose of HILDA establishment of flexible method of efficiently communicating with DBP. Power of HILDA lies in its extensibility with regard to syntax and semantic changes. HILDA's high-level query language readily modified. Offers powerful potential to computer sites where DBP attached to DEC VAX-series computer. HILDA system written in Pascal and FORTRAN 77 for interactive execution.

Computer-Supplemented Structural Drill Practice Versus Computer-Supplemented Semantic Drill Practice by Beginning College German Students: A Comparative Experiment

DTIC Science & Technology

1979-01-01

language education in recent years can be seen in the movement from a teacher- to a learner -centered approach. The best evidence of a teacher-centered...most dramatic effect on how the learner is viewed. The learner now is recognized as an active participant in the learning process rather than as a... best , is optional. Cognitive psychologists, such as Ausubel, and many foreign language educators (Rivers, 1976; Grittner, 1977) believe that practice

THE APPLICATION AND IMPLEMENTATION OF DEACON TYPE SYSTEMS.

DTIC Science & Technology

management information system deriving from a project concerning development of techniques for computing with a computer in essentially unconstrained English. Deacon-type systems respond to instructions and queries concerning the subject matter of their data by appropriately manipulating and organizing the data internally. The clues that guide the organizing activity are the syntactic rules of the language and their semantic transformations. Three examples of Deacon systems are given. The ’Deacon Breadboard Summary’ of F. B. Thompson (RM 64TMP-9)

High-level user interfaces for transfer function design with semantics.

PubMed

Salama, Christof Rezk; Keller, Maik; Kohlmann, Peter

2006-01-01

Many sophisticated techniques for the visualization of volumetric data such as medical data have been published. While existing techniques are mature from a technical point of view, managing the complexity of visual parameters is still difficult for non-expert users. To this end, this paper presents new ideas to facilitate the specification of optical properties for direct volume rendering. We introduce an additional level of abstraction for parametric models of transfer functions. The proposed framework allows visualization experts to design high-level transfer function models which can intuitively be used by non-expert users. The results are user interfaces which provide semantic information for specialized visualization problems. The proposed method is based on principal component analysis as well as on concepts borrowed from computer animation.

Computing quality scores and uncertainty for approximate pattern matching in geospatial semantic graphs

DOE PAGES

Stracuzzi, David John; Brost, Randolph C.; Phillips, Cynthia A.; ...

2015-09-26

Geospatial semantic graphs provide a robust foundation for representing and analyzing remote sensor data. In particular, they support a variety of pattern search operations that capture the spatial and temporal relationships among the objects and events in the data. However, in the presence of large data corpora, even a carefully constructed search query may return a large number of unintended matches. This work considers the problem of calculating a quality score for each match to the query, given that the underlying data are uncertain. As a result, we present a preliminary evaluation of three methods for determining both match qualitymore » scores and associated uncertainty bounds, illustrated in the context of an example based on overhead imagery data.« less

Semantic Ambiguity Effects in L2 Word Recognition.

PubMed

Ishida, Tomomi

2018-06-01

The present study examined the ambiguity effects in second language (L2) word recognition. Previous studies on first language (L1) lexical processing have observed that ambiguous words are recognized faster and more accurately than unambiguous words on lexical decision tasks. In this research, L1 and L2 speakers of English were asked whether a letter string on a computer screen was an English word or not. An ambiguity advantage was found for both groups and greater ambiguity effects were found for the non-native speaker group when compared to the native speaker group. The findings imply that the larger ambiguity advantage for L2 processing is due to their slower response time in producing adequate feedback activation from the semantic level to the orthographic level.

Modelling and approaching pragmatic interoperability of distributed geoscience data

NASA Astrophysics Data System (ADS)

Ma, Xiaogang

2010-05-01

Interoperability of geodata, which is essential for sharing information and discovering insights within a cyberinfrastructure, is receiving increasing attention. A key requirement of interoperability in the context of geodata sharing is that data provided by local sources can be accessed, decoded, understood and appropriately used by external users. Various researchers have discussed that there are four levels in data interoperability issues: system, syntax, schematics and semantics, which respectively relate to the platform, encoding, structure and meaning of geodata. Ontology-driven approaches have been significantly studied addressing schematic and semantic interoperability issues of geodata in the last decade. There are different types, e.g. top-level ontologies, domain ontologies and application ontologies and display forms, e.g. glossaries, thesauri, conceptual schemas and logical theories. Many geodata providers are maintaining their identified local application ontologies in order to drive standardization in local databases. However, semantic heterogeneities often exist between these local ontologies, even though they are derived from equivalent disciplines. In contrast, common ontologies are being studied in different geoscience disciplines (e.g., NAMD, SWEET, etc.) as a standardization procedure to coordinate diverse local ontologies. Semantic mediation, e.g. mapping between local ontologies, or mapping local ontologies to common ontologies, has been studied as an effective way of achieving semantic interoperability between local ontologies thus reconciling semantic heterogeneities in multi-source geodata. Nevertheless, confusion still exists in the research field of semantic interoperability. One problem is caused by eliminating elements of local pragmatic contexts in semantic mediation. Comparing to the context-independent feature of a common domain ontology, local application ontologies are closely related to elements (e.g., people, time, location, intention, procedure, consequence, etc.) of local pragmatic contexts and thus context-dependent. Elimination of these elements will inevitably lead to information loss in semantic mediation between local ontologies. Correspondingly, understanding and effect of exchanged data in a new context may differ from that in its original context. Another problem is the dilemma on how to find a balance between flexibility and standardization of local ontologies, because ontologies are not fixed, but continuously evolving. It is commonly realized that we cannot use a unified ontology to replace all local ontologies because they are context-dependent and need flexibility. However, without coordination of standards, freely developed local ontologies and databases will bring enormous work of mediation between them. Finding a balance between standardization and flexibility for evolving ontologies, in a practical sense, requires negotiations (i.e. conversations, agreements and collaborations) between different local pragmatic contexts. The purpose of this work is to set up a computer-friendly model representing local pragmatic contexts (i.e. geodata sources), and propose a practical semantic negotiation procedure for approaching pragmatic interoperability between local pragmatic contexts. Information agents, objective facts and subjective dimensions are reviewed as elements of a conceptual model for representing pragmatic contexts. The author uses them to draw a practical semantic negotiation procedure approaching pragmatic interoperability of distributed geodata. The proposed conceptual model and semantic negotiation procedure were encoded with Description Logic, and then applied to analyze and manipulate semantic negotiations between different local ontologies within the National Mineral Resources Assessment (NMRA) project of China, which involves multi-source and multi-subject geodata sharing.

Decision theory for computing variable and value ordering decisions for scheduling problems

NASA Technical Reports Server (NTRS)

Linden, Theodore A.

1993-01-01

Heuristics that guide search are critical when solving large planning and scheduling problems, but most variable and value ordering heuristics are sensitive to only one feature of the search state. One wants to combine evidence from all features of the search state into a subjective probability that a value choice is best, but there has been no solid semantics for merging evidence when it is conceived in these terms. Instead, variable and value ordering decisions should be viewed as problems in decision theory. This led to two key insights: (1) The fundamental concept that allows heuristic evidence to be merged is the net incremental utility that will be achieved by assigning a value to a variable. Probability distributions about net incremental utility can merge evidence from the utility function, binary constraints, resource constraints, and other problem features. The subjective probability that a value is the best choice is then derived from probability distributions about net incremental utility. (2) The methods used for rumor control in Bayesian Networks are the primary way to prevent cycling in the computation of probable net incremental utility. These insights lead to semantically justifiable ways to compute heuristic variable and value ordering decisions that merge evidence from all available features of the search state.

Tracking real-time neural activation of conceptual knowledge using single-trial event-related potentials.

PubMed

Amsel, Ben D

2011-04-01

Empirically derived semantic feature norms categorized into different types of knowledge (e.g., visual, functional, auditory) can be summed to create number-of-feature counts per knowledge type. Initial evidence suggests several such knowledge types may be recruited during language comprehension. The present study provides a more detailed understanding of the timecourse and intensity of influence of several such knowledge types on real-time neural activity. A linear mixed-effects model was applied to single trial event-related potentials for 207 visually presented concrete words measured on total number of features (semantic richness), imageability, and number of visual motion, color, visual form, smell, taste, sound, and function features. Significant influences of multiple feature types occurred before 200ms, suggesting parallel neural computation of word form and conceptual knowledge during language comprehension. Function and visual motion features most prominently influenced neural activity, underscoring the importance of action-related knowledge in computing word meaning. The dynamic time courses and topographies of these effects are most consistent with a flexible conceptual system wherein temporally dynamic recruitment of representations in modal and supramodal cortex are a crucial element of the constellation of processes constituting word meaning computation in the brain. Copyright © 2011 Elsevier Ltd. All rights reserved.

KARL: A Knowledge-Assisted Retrieval Language. M.S. Thesis Final Report, 1 Jul. 1985 - 31 Dec. 1987

NASA Technical Reports Server (NTRS)

Dominick, Wayne D. (Editor); Triantafyllopoulos, Spiros

1985-01-01

Data classification and storage are tasks typically performed by application specialists. In contrast, information users are primarily non-computer specialists who use information in their decision-making and other activities. Interaction efficiency between such users and the computer is often reduced by machine requirements and resulting user reluctance to use the system. This thesis examines the problems associated with information retrieval for non-computer specialist users, and proposes a method for communicating in restricted English that uses knowledge of the entities involved, relationships between entities, and basic English language syntax and semantics to translate the user requests into formal queries. The proposed method includes an intelligent dictionary, syntax and semantic verifiers, and a formal query generator. In addition, the proposed system has a learning capability that can improve portability and performance. With the increasing demand for efficient human-machine communication, the significance of this thesis becomes apparent. As human resources become more valuable, software systems that will assist in improving the human-machine interface will be needed and research addressing new solutions will be of utmost importance. This thesis presents an initial design and implementation as a foundation for further research and development into the emerging field of natural language database query systems.

Programming chemistry in DNA-addressable bioreactors

PubMed Central

Fellermann, Harold; Cardelli, Luca

2014-01-01

We present a formal calculus, termed the chemtainer calculus, able to capture the complexity of compartmentalized reaction systems such as populations of possibly nested vesicular compartments. Compartments contain molecular cargo as well as surface markers in the form of DNA single strands. These markers serve as compartment addresses and allow for their targeted transport and fusion, thereby enabling reactions of previously separated chemicals. The overall system organization allows for the set-up of programmable chemistry in microfluidic or other automated environments. We introduce a simple sequential programming language whose instructions are motivated by state-of-the-art microfluidic technology. Our approach integrates electronic control, chemical computing and material production in a unified formal framework that is able to mimic the integrated computational and constructive capabilities of the subcellular matrix. We provide a non-deterministic semantics of our programming language that enables us to analytically derive the computational and constructive power of our machinery. This semantics is used to derive the sets of all constructable chemicals and supermolecular structures that emerge from different underlying instruction sets. Because our proofs are constructive, they can be used to automatically infer control programs for the construction of target structures from a limited set of resource molecules. Finally, we present an example of our framework from the area of oligosaccharide synthesis. PMID:25121647

Edge co-occurrences can account for rapid categorization of natural versus animal images

NASA Astrophysics Data System (ADS)

Perrinet, Laurent U.; Bednar, James A.

2015-06-01

Making a judgment about the semantic category of a visual scene, such as whether it contains an animal, is typically assumed to involve high-level associative brain areas. Previous explanations require progressively analyzing the scene hierarchically at increasing levels of abstraction, from edge extraction to mid-level object recognition and then object categorization. Here we show that the statistics of edge co-occurrences alone are sufficient to perform a rough yet robust (translation, scale, and rotation invariant) scene categorization. We first extracted the edges from images using a scale-space analysis coupled with a sparse coding algorithm. We then computed the “association field” for different categories (natural, man-made, or containing an animal) by computing the statistics of edge co-occurrences. These differed strongly, with animal images having more curved configurations. We show that this geometry alone is sufficient for categorization, and that the pattern of errors made by humans is consistent with this procedure. Because these statistics could be measured as early as the primary visual cortex, the results challenge widely held assumptions about the flow of computations in the visual system. The results also suggest new algorithms for image classification and signal processing that exploit correlations between low-level structure and the underlying semantic category.

Formal ontologies in biomedical knowledge representation.

PubMed

Schulz, S; Jansen, L

2013-01-01

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

Foveal splitting causes differential processing of Chinese orthography in the male and female brain.

PubMed

Hsiao, Janet Hui-Wen; Shillcock, Richard

2005-10-01

Chinese characters contain separate phonetic and semantic radicals. A dominant character type exists in which the semantic radical is on the left and the phonetic radical on the right; an opposite, minority structure also exists, with the semantic radical on the right and the phonetic radical on the left. We show that, when asked to pronounce isolated tokens of these two character types, males responded significantly faster when the phonetic information was on the right, whereas females showed a non-significant tendency in the opposite direction. Recent research on foveal structure and reading suggests that the two halves of a centrally fixated character are initially processed in different hemispheres. The male brain typically relies more on the left hemisphere for phonological processing compared with the female brain, causing this gender difference to emerge. This interaction is predicted by an implemented computational model. This study supports the existence of a gender difference in phonological processing, and shows that the effects of foveal splitting in reading extend far enough into word recognition to interact with the gender of the reader in a naturalistic reading task.

Knowledge Reasoning with Semantic Data for Real-Time Data Processing in Smart Factory

PubMed Central

Wang, Shiyong; Li, Di; Liu, Chengliang

2018-01-01

The application of high-bandwidth networks and cloud computing in manufacturing systems will be followed by mass data. Industrial data analysis plays important roles in condition monitoring, performance optimization, flexibility, and transparency of the manufacturing system. However, the currently existing architectures are mainly for offline data analysis, not suitable for real-time data processing. In this paper, we first define the smart factory as a cloud-assisted and self-organized manufacturing system in which physical entities such as machines, conveyors, and products organize production through intelligent negotiation and the cloud supervises this self-organized process for fault detection and troubleshooting based on data analysis. Then, we propose a scheme to integrate knowledge reasoning and semantic data where the reasoning engine processes the ontology model with real time semantic data coming from the production process. Based on these ideas, we build a benchmarking system for smart candy packing application that supports direct consumer customization and flexible hybrid production, and the data are collected and processed in real time for fault diagnosis and statistical analysis. PMID:29415444

Applying Semantic Web technologies to improve the retrieval, credibility and use of health-related web resources.

PubMed

Mayer, Miguel A; Karampiperis, Pythagoras; Kukurikos, Antonis; Karkaletsis, Vangelis; Stamatakis, Kostas; Villarroel, Dagmar; Leis, Angela

2011-06-01

The number of health-related websites is increasing day-by-day; however, their quality is variable and difficult to assess. Various "trust marks" and filtering portals have been created in order to assist consumers in retrieving quality medical information. Consumers are using search engines as the main tool to get health information; however, the major problem is that the meaning of the web content is not machine-readable in the sense that computers cannot understand words and sentences as humans can. In addition, trust marks are invisible to search engines, thus limiting their usefulness in practice. During the last five years there have been different attempts to use Semantic Web tools to label health-related web resources to help internet users identify trustworthy resources. This paper discusses how Semantic Web technologies can be applied in practice to generate machine-readable labels and display their content, as well as to empower end-users by providing them with the infrastructure for expressing and sharing their opinions on the quality of health-related web resources.

The role of the frontal cortex in memory: an investigation of the Von Restorff effect

PubMed Central

Elhalal, Anat; Davelaar, Eddy J.; Usher, Marius

2014-01-01

Evidence from neuropsychology and neuroimaging indicate that the pre-frontal cortex (PFC) plays an important role in human memory. Although frontal patients are able to form new memories, these memories appear qualitatively different from those of controls by lacking distinctiveness. Neuroimaging studies of memory indicate activation in the PFC under deep encoding conditions, and under conditions of semantic elaboration. Based on these results, we hypothesize that the PFC enhances memory by extracting differences and commonalities in the studied material. To test this hypothesis, we carried out an experimental investigation to test the relationship between the PFC-dependent factors and semantic factors associated with common and specific features of words. These experiments were performed using Free-Recall of word lists with healthy adults, exploiting the correlation between PFC function and fluid intelligence. As predicted, a correlation was found between fluid intelligence and the Von-Restorff effect (better memory for semantic isolates, e.g., isolate “cat” within category members of “fruit”). Moreover, memory for the semantic isolate was found to depend on the isolate's serial position. The isolate item tends to be recalled first, in comparison to non-isolates, suggesting that the process interacts with short term memory. These results are captured within a computational model of free recall, which includes a PFC mechanism that is sensitive to both commonality and distinctiveness, sustaining a trade-off between the two. PMID:25018721

Linking DICOM pixel data with radiology reports using automatic semantic annotation

NASA Astrophysics Data System (ADS)

Pathak, Sayan D.; Kim, Woojin; Munasinghe, Indeera; Criminisi, Antonio; White, Steve; Siddiqui, Khan

2012-02-01

Improved access to DICOM studies to both physicians and patients is changing the ways medical imaging studies are visualized and interpreted beyond the confines of radiologists' PACS workstations. While radiologists are trained for viewing and image interpretation, a non-radiologist physician relies on the radiologists' reports. Consequently, patients historically have been typically informed about their imaging findings via oral communication with their physicians, even though clinical studies have shown that patients respond to physician's advice significantly better when the individual patients are shown their own actual data. Our previous work on automated semantic annotation of DICOM Computed Tomography (CT) images allows us to further link radiology report with the corresponding images, enabling us to bridge the gap between image data with the human interpreted textual description of the corresponding imaging studies. The mapping of radiology text is facilitated by natural language processing (NLP) based search application. When combined with our automated semantic annotation of images, it enables navigation in large DICOM studies by clicking hyperlinked text in the radiology reports. An added advantage of using semantic annotation is the ability to render the organs to their default window level setting thus eliminating another barrier to image sharing and distribution. We believe such approaches would potentially enable the consumer to have access to their imaging data and navigate them in an informed manner.

NoteCards: A Multimedia Idea Processing Environment.

ERIC Educational Resources Information Center

Halasz, Frank G.

1986-01-01

Notecards is a computer environment designed to help people work with ideas by providing a set of tools for a variety of specific activities, which can range from sketching on the back of an envelope to formally representing knowledge. The basic framework of this hypermedia system is a semantic network of electronic notecards connected by…

Towards a Script-Based Representation Language for Educational Films.

ERIC Educational Resources Information Center

Parkes, Alan P.

1987-01-01

Discusses aspects of the syntax and semantics of film, and presents a scenario for the use of film by intelligent computer assisted instruction (ICAI) systems. An outline of a representation language for educational films on videodisc is presented, and an appendix provides conceptual graphs that explain notations used in examples. (Author/LRW)

The Syntax-Semantics Interface in Distributed Morphology

ERIC Educational Resources Information Center

Kelly, Justin Robert

2013-01-01

Distributed Morphology (DM; Halle & Marantz 1993; Marantz 1997) is founded on the premise that the syntax is the only computational component of the grammar. Much research focuses on how this premise is relevant to the syntax-morphology interface in DM. In this dissertation, I examine theory-internal issues related to the syntax-semantics…

Pervasive Knowledge, Social Networks, and Cloud Computing: E-Learning 2.0

ERIC Educational Resources Information Center

Anshari, Muhammad; Alas, Yabit; Guan, Lim Sei

2015-01-01

Embedding Web 2.0 in learning processes has extended learning from traditional based learning-centred to a collaborative based learning-centred institution that emphasises learning anywhere and anytime. While deploying Semantic Web into e-learning offers a broader spectrum of pervasive knowledge acquisition to enrich users' experience in learning.…

Medical Language Processing for Knowledge Representation and Retrievals

PubMed Central

Lyman, Margaret; Sager, Naomi; Chi, Emile C.; Tick, Leo J.; Nhan, Ngo Thanh; Su, Yun; Borst, Francois; Scherrer, Jean-Raoul

1989-01-01

The Linguistic String Project-Medical Language Processor, a system for computer analysis of narrative patient documents in English, is being adapted for French Lettres de Sortie. The system converts the free-text input to a semantic representation which is then mapped into a relational database. Retrievals of clinical data from the database are described.

A Semantic Navigation Model for Video Games

NASA Astrophysics Data System (ADS)

van Driel, Leonard; Bidarra, Rafael

Navigational performance of artificial intelligence (AI) characters in computer games is gaining an increasingly important role in the perception of their behavior. While recent games successfully solve some complex navigation problems, there is little known or documented on the underlying approaches, often resembling a primitive conglomerate of ad-hoc algorithms for specific situations.

A Chatbot for a Dialogue-Based Second Language Learning System

ERIC Educational Resources Information Center

Huang, Jin-Xia; Lee, Kyung-Soon; Kwon, Oh-Woog; Kim, Young-Kil

2017-01-01

This paper presents a chatbot for a Dialogue-Based Computer-Assisted second Language Learning (DB-CALL) system. A DB-CALL system normally leads dialogues by asking questions according to given scenarios. User utterances outside the scenarios are normally considered as semantically improper and simply rejected. In this paper, we assume that raising…

Semantic Grammar: An Engineering Technique for Constructing Natural Language Understanding Systems.

ERIC Educational Resources Information Center

Burton, Richard R.

In an attempt to overcome the lack of natural means of communication between student and computer, this thesis addresses the problem of developing a system which can understand natural language within an educational problem-solving environment. The nature of the environment imposes efficiency, habitability, self-teachability, and awareness of…

Human-Machine Cooperation in Large-Scale Multimedia Retrieval: A Survey

ERIC Educational Resources Information Center

Shirahama, Kimiaki; Grzegorzek, Marcin; Indurkhya, Bipin

2015-01-01

"Large-Scale Multimedia Retrieval" (LSMR) is the task to fast analyze a large amount of multimedia data like images or videos and accurately find the ones relevant to a certain semantic meaning. Although LSMR has been investigated for more than two decades in the fields of multimedia processing and computer vision, a more…

Interpreting beyond Syntactics: A Semiotic Learning Model for Computer Programming Languages

ERIC Educational Resources Information Center

May, Jeffrey; Dhillon, Gurpreet

2009-01-01

In the information systems field there are numerous programming languages that can be used in specifying the behavior of concurrent and distributed systems. In the literature it has been argued that a lack of pragmatic and semantic consideration decreases the effectiveness of such specifications. In other words, to simply understand the syntactic…

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.

Comparing Latent Dirichlet Allocation and Latent Semantic Analysis as Classifiers

ERIC Educational Resources Information Center

Anaya, Leticia H.

2011-01-01

In the Information Age, a proliferation of unstructured text electronic documents exists. Processing these documents by humans is a daunting task as humans have limited cognitive abilities for processing large volumes of documents that can often be extremely lengthy. To address this problem, text data computer algorithms are being developed.…

Applications and Methods Utilizing the Simple Semantic Web Architecture and Protocol (SSWAP) for Bioinformatics Resource Discovery and Disparate Data and Service Integration

USDA-ARS?s Scientific Manuscript database

Scientific data integration and computational service discovery are challenges for the bioinformatic community. This process is made more difficult by the separate and independent construction of biological databases, which makes the exchange of scientific data between information resources difficu...

Using a Combination of UML, C2RM, XML, and Metadata Registries to Support Long-Term Development/Engineering

DTIC Science & Technology

2003-01-01

Authenticat’n (XCBF) Authorizat’n (XACML) (SAML) Privacy (P3P) Digital Rights Management (XrML) Content Mngmnt (DASL) (WebDAV) Content Syndicat’n...Registry/ Repository BPSS eCommerce XML/EDI Universal Business Language (UBL) Internet & Computing Human Resources (HR-XML) Semantic KEY XML SPECIFICATIONS

Using Novel Word Context Measures to Predict Human Ratings of Lexical Proficiency

ERIC Educational Resources Information Center

Berger, Cynthia M.; Crossley, Scott A.; Kyle, Kristopher

2017-01-01

This study introduces a model of lexical proficiency based on novel computational indices related to word context. The indices come from an updated version of the Tool for the Automatic Analysis of Lexical Sophistication (TAALES) and include associative, lexical, and semantic measures of word context. Human ratings of holistic lexical proficiency…

Simulating Cross-Language Priming with a Dynamic Computational Model of the Lexicon

ERIC Educational Resources Information Center

Zhao, Xiaowei; Li, Ping

2013-01-01

Cross-language priming is a widely used experimental paradigm in psycholinguistics to study how bilinguals' two languages are represented and organized. Researchers have observed a number of interesting patterns from the priming effects of both translation equivalents and semantically related word pairs across languages. In this study, we…

Formalization of Generalized Constraint Language: A Crucial Prelude to Computing With Words.

PubMed

Khorasani, Elham S; Rahimi, Shahram; Calvert, Wesley

2013-02-01

The generalized constraint language (GCL), introduced by Zadeh, serves as a basis for computing with words (CW). It provides an agenda to express the imprecise and fuzzy information embedded in natural language and allows reasoning with perceptions. Despite its fundamental role, the definition of GCL has remained informal since its introduction by Zadeh, and to our knowledge, no attempt has been made to formulate a rigorous theoretical framework for GCL. Such formalization is necessary for further theoretical and practical advancement of CW for two important reasons. First, it provides the underlying infrastructure for the development of useful inference patterns based on sound theories. Second, it determines the scope of GCL and hence facilitates the translation of natural language expressions into GCL. This paper is an attempt to step in this direction by providing a formal syntax together with a compositional semantics for GCL. A soundness theorem is defined, and Zadeh's deduction rules are proved to be valid in the defined semantics. Furthermore, a discussion is provided on how the proposed language may be used in practice.

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

PubMed

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

2016-07-19

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

Benefits of computer-based memory and attention training in healthy older adults.

PubMed

Chambon, Caroline; Herrera, Cathy; Romaiguere, Patricia; Paban, Véronique; Alescio-Lautier, Béatrice

2014-09-01

Multifactorial cognitive training programs have a positive effect on cognition in healthy older adults. Among the age-sensitive cognitive domains, episodic memory is the most affected. In the present study, we evaluated the benefits on episodic memory of a computer-based memory and attention training. We targeted consciously controlled processes at encoding and minimizing processing at retrieval, by using more familiarity than recollection during recognition. Such an approach emphasizes processing at encoding and prevents subjects from reinforcing their own errors. Results showed that the training improved recognition performances and induced near transfer to recall. The largest benefits, however, were for tasks with high mental load. Improvement in free recall depended on the modality to recall; semantic recall was improved but not spatial recall. In addition, a far transfer was also observed with better memory self-perception and self-esteem of the participants. Finally, at 6-month follow up, maintenance of benefits was observed only for semantic free recall. The challenge now is to corroborate far transfer by objective measures of everyday life executive functioning. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Center of Excellence for Geospatial Information Science research plan 2013-18

USGS Publications Warehouse

Usery, E. Lynn

2013-01-01

The U.S. Geological Survey Center of Excellence for Geospatial Information Science (CEGIS) was created in 2006 and since that time has provided research primarily in support of The National Map. The presentations and publications of the CEGIS researchers document the research accomplishments that include advances in electronic topographic map design, generalization, data integration, map projections, sea level rise modeling, geospatial semantics, ontology, user-centered design, volunteer geographic information, and parallel and grid computing for geospatial data from The National Map. A research plan spanning 2013–18 has been developed extending the accomplishments of the CEGIS researchers and documenting new research areas that are anticipated to support The National Map of the future. In addition to extending the 2006–12 research areas, the CEGIS research plan for 2013–18 includes new research areas in data models, geospatial semantics, high-performance computing, volunteered geographic information, crowdsourcing, social media, data integration, and multiscale representations to support the Three-Dimensional Elevation Program (3DEP) and The National Map of the future of the U.S. Geological Survey.

Vector Symbolic Spiking Neural Network Model of Hippocampal Subarea CA1 Novelty Detection Functionality.

PubMed

Agerskov, Claus

2016-04-01

A neural network model is presented of novelty detection in the CA1 subdomain of the hippocampal formation from the perspective of information flow. This computational model is restricted on several levels by both anatomical information about hippocampal circuitry and behavioral data from studies done in rats. Several studies report that the CA1 area broadcasts a generalized novelty signal in response to changes in the environment. Using the neural engineering framework developed by Eliasmith et al., a spiking neural network architecture is created that is able to compare high-dimensional vectors, symbolizing semantic information, according to the semantic pointer hypothesis. This model then computes the similarity between the vectors, as both direct inputs and a recalled memory from a long-term memory network by performing the dot-product operation in a novelty neural network architecture. The developed CA1 model agrees with available neuroanatomical data, as well as the presented behavioral data, and so it is a biologically realistic model of novelty detection in the hippocampus, which can provide a feasible explanation for experimentally observed dynamics.

Lexical is as lexical does: computational approaches to lexical representation

PubMed Central

Woollams, Anna M.

2015-01-01

In much of neuroimaging and neuropsychology, regions of the brain have been associated with ‘lexical representation’, with little consideration as to what this cognitive construct actually denotes. Within current computational models of word recognition, there are a number of different approaches to the representation of lexical knowledge. Structural lexical representations, found in original theories of word recognition, have been instantiated in modern localist models. However, such a representational scheme lacks neural plausibility in terms of economy and flexibility. Connectionist models have therefore adopted distributed representations of form and meaning. Semantic representations in connectionist models necessarily encode lexical knowledge. Yet when equipped with recurrent connections, connectionist models can also develop attractors for familiar forms that function as lexical representations. Current behavioural, neuropsychological and neuroimaging evidence shows a clear role for semantic information, but also suggests some modality- and task-specific lexical representations. A variety of connectionist architectures could implement these distributed functional representations, and further experimental and simulation work is required to discriminate between these alternatives. Future conceptualisations of lexical representations will therefore emerge from a synergy between modelling and neuroscience. PMID:25893204

The Differential Contributions of Conceptual Representation Format and Language Structure to Levels of Semantic Abstraction Capacity.

PubMed

Gainotti, Guido

2017-06-01

This paper reviews some controversies concerning the original and revised versions of the 'hub-and-spoke' model of conceptual representations and their implication for abstraction capacity levels. The 'hub-and-spoke' model, which is based on data gathered in patients with semantic dementia (SD), is the most authoritative model of conceptual knowledge. Patterson et al.'s (Nature Reviews Neuroscience, 8(12), 976-987, 2007) classical version of this model maintained that conceptual representations are stored in a unitary 'amodal' format in the right and left anterior temporal lobes (ATLs), because in SD the semantic disorder cuts across modalities and categories. Several authors questioned the unitary nature of these representations. They showed that the semantic impairment is 'multi-modal'only in the advanced stages of SD, when atrophy affects the ATLs bilaterally, but that impariments can be modality-specific in lateralised (early) stages of the disease. In these cases, SD mainly affects lexical-semantic knowledge when atrophy predominates on the left side and pictorial representations when atrophy prevails on the right side. Some aspects of the model (i.e. the importance of spokes, the multimodal format of representations and the graded convergence of modalities within the ATLs), which had already been outlined by Rogers et al. (Psychological Review, 111(1), 205-235, 2004) in a computational model of SD, were strengthened by these results. The relevance of these theoretical problems and of empirical data concerning the neural substrate of concrete and abstract words is discussed critically. The conclusion of the review is that the highest levels of abstraction are due more to the structuring influence of language than to the format of representations.

Expanding the Extent of a UMLS Semantic Type via Group Neighborhood Auditing

PubMed Central

Chen, Yan; Gu, Huanying; Perl, Yehoshua; Halper, Michael; Xu, Junchuan

2009-01-01

Objective Each Unified Medical Language System (UMLS) concept is assigned one or more semantic types (ST). A dynamic methodology for aiding an auditor in finding concepts that are missing the assignment of a given ST, S is presented. Design The first part of the methodology exploits the previously introduced Refined Semantic Network and accompanying refined semantic types (RST) to help narrow the search space for offending concepts. The auditing is focused in a neighborhood surrounding the extent of an RST, T (of S) called an envelope, consisting of parents and children of concepts in the extent. The audit moves outward as long as missing assignments are discovered. In the second part, concepts not reached previously are processed and reassigned T as needed during the processing of S's other RSTs. The set of such concepts is expanded in a similar way to that in the first part. Measurements The number of errors discovered is reported. To measure the methodology's efficiency, “error hit rates” (i.e., errors found in concepts examined) are computed. Results The methodology was applied to three STs: Experimental Model of Disease (EMD), Environmental Effect of Humans, and Governmental or Regulatory Activity. The EMD experienced the most drastic change. For its RST “EMD ∩ Neoplastic Process” (RST “EMD”) with only 33 (31) original concepts, 915 (134) concepts were found by the first (second) part to be missing the EMD assignment. Changes to the other two STs were smaller. Conclusion The results show that the proposed auditing methodology can help to effectively and efficiently identify concepts lacking the assignment of a particular semantic type. PMID:19567802

SEMANTIC3D.NET: a New Large-Scale Point Cloud Classification Benchmark

NASA Astrophysics Data System (ADS)

Hackel, T.; Savinov, N.; Ladicky, L.; Wegner, J. D.; Schindler, K.; Pollefeys, M.

2017-05-01

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

Hierarchical Recurrent Neural Hashing for Image Retrieval With Hierarchical Convolutional Features.

PubMed

Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong

Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.Hashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

PubMed

Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

2018-04-01

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

An index-based algorithm for fast on-line query processing of latent semantic analysis

PubMed Central

Li, Pohan; Wang, Wei

2017-01-01

Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm. PMID:28520747

Building a semantic web-based metadata repository for facilitating detailed clinical modeling in cancer genome studies.

PubMed

Sharma, Deepak K; Solbrig, Harold R; Tao, Cui; Weng, Chunhua; Chute, Christopher G; Jiang, Guoqian

2017-06-05

Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains. We leveraged a Semantic Web-based metadata repository enhanced with both ISO11179 metadata standard and Clinical Information Modeling Initiative (CIMI) Reference Model. We used the common data elements (CDEs) defined in The Cancer Genome Atlas (TCGA) data dictionary, and extracted the metadata of the CDEs using the NCI Cancer Data Standards Repository (caDSR) CDE dataset rendered in the Resource Description Framework (RDF). The ITEM/ITEM_GROUP pattern defined in the latest CIMI Reference Model is used to represent reusable model elements (mini-Archetypes). We produced a metadata repository with 38 clinical cancer genome study domains, comprising a rich collection of mini-Archetype pattern instances. We performed a case study of the domain "clinical pharmaceutical" in the TCGA data dictionary and demonstrated enriched data elements in the metadata repository are very useful in support of building detailed clinical models. Our informatics approach leveraging Semantic Web technologies provides an effective way to build a CIMI-compliant metadata repository that would facilitate the detailed clinical modeling to support use cases beyond TCGA in clinical cancer study domains.

An index-based algorithm for fast on-line query processing of latent semantic analysis.

PubMed

Zhang, Mingxi; Li, Pohan; Wang, Wei

2017-01-01

Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm.

Mining Quality Phrases from Massive Text Corpora

PubMed Central

Liu, Jialu; Shang, Jingbo; Wang, Chi; Ren, Xiang; Han, Jiawei

2015-01-01

Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method. PMID:26705375

A grammar-based semantic similarity algorithm for natural language sentences.

PubMed

Lee, Ming Che; Chang, Jia Wei; Hsieh, Tung Cheng

2014-01-01

This paper presents a grammar and semantic corpus based similarity algorithm for natural language sentences. Natural language, in opposition to "artificial language", such as computer programming languages, is the language used by the general public for daily communication. Traditional information retrieval approaches, such as vector models, LSA, HAL, or even the ontology-based approaches that extend to include concept similarity comparison instead of cooccurrence terms/words, may not always determine the perfect matching while there is no obvious relation or concept overlap between two natural language sentences. This paper proposes a sentence similarity algorithm that takes advantage of corpus-based ontology and grammatical rules to overcome the addressed problems. Experiments on two famous benchmarks demonstrate that the proposed algorithm has a significant performance improvement in sentences/short-texts with arbitrary syntax and structure.

The Semantic Network of Flood Hydrological Data for Kelantan, Malaysia

NASA Astrophysics Data System (ADS)

Yusoff, Aziyati; Din, Norashidah Md; Yussof, Salman; Ullah Khan, Samee

2016-03-01

Every year, authorities in Malaysia are putting efforts on disaster management mechanisms including the flood incidence that might hit the east coast of Peninsular Malaysia. This includes the state of Kelantan of which it was reported that flood is just a normal event occurred annually. However, the aftermath was always unmanageable and had left the state to struggle for its own recoveries. Though it was expected that flood occurred every year, among the worst were in 1967, 1974, 1982 and recently in December 2014. This study is proposing a semantic network as an approach to the method of utilising big data analytics in analysing the huge data from the state’s flood reading stations. It is expected that by using current computing edge can also facilitate mitigating this particular disaster.

Classification of Aerial Photogrammetric 3d Point Clouds

NASA Astrophysics Data System (ADS)

Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

2017-05-01

We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

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

PubMed Central

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

2009-01-01

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

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

PubMed

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

2009-03-01

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

Can Social Semantic Web Techniques Foster Collaborative Curriculum Mapping In Medicine?

PubMed Central

Finsterer, Sonja; Cremer, Jan; Schenkat, Hennig

2013-01-01

Background Curriculum mapping, which is aimed at the systematic realignment of the planned, taught, and learned curriculum, is considered a challenging and ongoing effort in medical education. Second-generation curriculum managing systems foster knowledge management processes including curriculum mapping in order to give comprehensive support to learners, teachers, and administrators. The large quantity of custom-built software in this field indicates a shortcoming of available IT tools and standards. Objective The project reported here aims at the systematic adoption of techniques and standards of the Social Semantic Web to implement collaborative curriculum mapping for a complete medical model curriculum. Methods A semantic MediaWiki (SMW)-based Web application has been introduced as a platform for the elicitation and revision process of the Aachen Catalogue of Learning Objectives (ACLO). The semantic wiki uses a domain model of the curricular context and offers structured (form-based) data entry, multiple views, structured querying, semantic indexing, and commenting for learning objectives (“LOs”). Semantic indexing of learning objectives relies on both a controlled vocabulary of international medical classifications (ICD, MeSH) and a folksonomy maintained by the users. An additional module supporting the global checking of consistency complements the semantic wiki. Statements of the Object Constraint Language define the consistency criteria. We evaluated the application by a scenario-based formative usability study, where the participants solved tasks in the (fictional) context of 7 typical situations and answered a questionnaire containing Likert-scaled items and free-text questions. Results At present, ACLO contains roughly 5350 operational (ie, specific and measurable) objectives acquired during the last 25 months. The wiki-based user interface uses 13 online forms for data entry and 4 online forms for flexible searches of LOs, and all the forms are accessible by standard Web browsers. The formative usability study yielded positive results (median rating of 2 (“good”) in all 7 general usability items) and produced valuable qualitative feedback, especially concerning navigation and comprehensibility. Although not asked to, the participants (n=5) detected critical aspects of the curriculum (similar learning objectives addressed repeatedly and missing objectives), thus proving the system’s ability to support curriculum revision. Conclusions The SMW-based approach enabled an agile implementation of computer-supported knowledge management. The approach, based on standard Social Semantic Web formats and technology, represents a feasible and effectively applicable compromise between answering to the individual requirements of curriculum management at a particular medical school and using proprietary systems. PMID:23948519

E-Learning 3.0 = E-Learning 2.0 + Web 3.0?

ERIC Educational Resources Information Center

Hussain, Fehmida

2012-01-01

Web 3.0, termed as the semantic web or the web of data is the transformed version of Web 2.0 with technologies and functionalities such as intelligent collaborative filtering, cloud computing, big data, linked data, openness, interoperability and smart mobility. If Web 2.0 is about social networking and mass collaboration between the creator and…

A Theoretical Analysis of Learning with Graphics--Implications for Computer Graphics Design.

ERIC Educational Resources Information Center

ChanLin, Lih-Juan

This paper reviews the literature pertinent to learning with graphics. The dual coding theory provides explanation about how graphics are stored and precessed in semantic memory. The level of processing theory suggests how graphics can be employed in learning to encourage deeper processing. In addition to dual coding theory and level of processing…

Modeling of Word Translation: Activation Flow from Concepts to Lexical Items

ERIC Educational Resources Information Center

Roelofs, Ardi; Dijkstra, Ton; Gerakaki, Svetlana

2013-01-01

Whereas most theoretical and computational models assume a continuous flow of activation from concepts to lexical items in spoken word production, one prominent model assumes that the mapping of concepts onto words happens in a discrete fashion (Bloem & La Heij, 2003). Semantic facilitation of context pictures on word translation has been taken to…

A Synchronization Account of False Recognition

ERIC Educational Resources Information Center

Johns, Brendan T.; Jones, Michael N.; Mewhort, Douglas J. K.

2012-01-01

We describe a computational model to explain a variety of results in both standard and false recognition. A key attribute of the model is that it uses plausible semantic representations for words, built through exposure to a linguistic corpus. A study list is encoded in the model as a gist trace, similar to the proposal of fuzzy trace theory…

Toward a Theory-Based Natural Language Capability in Robots and Other Embodied Agents: Evaluating Hausser's SLIM Theory and Database Semantics

ERIC Educational Resources Information Center

Burk, Robin K.

2010-01-01

Computational natural language understanding and generation have been a goal of artificial intelligence since McCarthy, Minsky, Rochester and Shannon first proposed to spend the summer of 1956 studying this and related problems. Although statistical approaches dominate current natural language applications, two current research trends bring…

A Model Based Framework for Semantic Interpretation of Architectural Construction Drawings

ERIC Educational Resources Information Center

Babalola, Olubi Oluyomi

2011-01-01

The study addresses the automated translation of architectural drawings from 2D Computer Aided Drafting (CAD) data into a Building Information Model (BIM), with emphasis on the nature, possible role, and limitations of a drafting language Knowledge Representation (KR) on the problem and process. The central idea is that CAD to BIM translation is a…

The Paradox of Abstraction: Precision Versus Concreteness

ERIC Educational Resources Information Center

Iliev, Rumen; Axelrod, Robert

2017-01-01

We introduce a novel measure of abstractness based on the amount of information of a concept computed from its position in a semantic taxonomy. We refer to this measure as "precision". We propose two alternative ways to measure precision, one based on the path length from a concept to the root of the taxonomic tree, and another one based…

Cognitive Representations of Obligation and Prohibition Signs when They Provide the Same Amount of Semantic Information

ERIC Educational Resources Information Center

Castro, C.; Moreno-Rios, S.; Tornay, F. J.

2012-01-01

The aim of this research was to test whether there is an inherent difficulty in understanding prohibition signs rather than obligation signs. In the experiment conducted, participants decided whether simple car movements presented on a computer screen were allowed or not according to either obligation or prohibition traffic signs. The information…

A Dynamic Dialog System Using Semantic Web Technologies

ERIC Educational Resources Information Center

Ababneh, Mohammad

2014-01-01

A dialog system or a conversational agent provides a means for a human to interact with a computer system. Dialog systems use text, voice and other means to carry out conversations with humans in order to achieve some objective. Most dialog systems are created with specific objectives in mind and consist of preprogrammed conversations. The primary…

Network-Based Learning and Assessment Applications on the Semantic Web

ERIC Educational Resources Information Center

Gibson, David

2005-01-01

Today's Web applications are already "aware" of the network of computers and data on the Internet, in the sense that they perceive, remember, and represent knowledge external to themselves. However, Web applications are generally not able to respond to the meaning and context of the information in their memories. As a result, most applications are…

Development and use of the Cytoscape app GFD-Net for measuring semantic dissimilarity of gene networks

PubMed Central

Diaz-Montana, Juan J.; Diaz-Diaz, Norberto

2014-01-01

Gene networks are one of the main computational models used to study the interaction between different elements during biological processes being widely used to represent gene–gene, or protein–protein interaction complexes. We present GFD-Net, a Cytoscape app for visualizing and analyzing the functional dissimilarity of gene networks. PMID:25400907

Semantic Annotation of Resources to Learn with Connected Things

ERIC Educational Resources Information Center

Bouchereau, Aymeric; Roxin, Ioan

2017-01-01

Computer systems tend to be ubiquitous as they become more integrated in our everyday activities, embedded in tables, shoes, watch and plenty of others connected things (CT). In the e-learning field, the transformations induced by the Internet of Things (IoT) allow individuals to learn whenever they want, accessing a quantity of diverse digital…