1. Articles in category: Semantic

    25-48 of 4047 « 1 2 3 4 5 ... 167 168 169 »
    1. Automatic extraction of named entities from texts

      Disclosed are systems, computer-readable mediums, and methods for extracting named entities from an untagged corpus of texts. Generating a set of attributes for each of the tokens based at least on a deep semantic-syntactic analysis. The set of attributes include lexical, syntactic, and semantic attributes. Selecting a subset of the attributes for each of the tokens. Retrieving classifier attributes and categories based on a trained model, wherein the classifier attributes are related to one or more categories. Comparing the subset of the attributes for each of the tokens with the classifier attributes. Classifying each of tokens to at least one ...

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    2. Integration of semantic context information

      In one implementation, a computer-implemented method includes receiving, at a computer system, a request to predict a next word in a dialog being uttered by a speaker; accessing, by the computer system, a neural network comprising i) an input layer, ii) one or more hidden layers, and iii) an output layer; identifying the local context for the dialog of the speaker; selecting, by the computer system and using a semantic model, at least one vector that represents the semantic context for the dialog; applying input to the input layer of the neural network, the input comprising i) the local context ...

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    3. Ontology-Oriented Programming for Biomedical Informatics.

      Ontology-Oriented Programming for Biomedical Informatics.

      Ontology-Oriented Programming for Biomedical Informatics.

      Stud Health Technol Inform. 2016;221:64-8

      Authors: Lamy JB

      Abstract Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites.

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    4. Context-sensitive search using a deep learning model

      A search engine is described herein for providing search results based on a context in which a query has been submitted, as expressed by context information. The search engine operates by ranking a plurality of documents based on a consideration of the query, and based, in part, on a context concept vector and a plurality of document concept vectors, both generated using a deep learning model (such as a deep neural network). The context concept vector is formed by a projection of the context information into a semantic space using the deep learning model. Each document concept vector is formed ...

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    5. Computing semantic similarity between biomedical concepts using new information content approach.

      Computing semantic similarity between biomedical concepts using new information content approach.

      Computing semantic similarity between biomedical concepts using new information content approach.

      J Biomed Inform. 2016 Feb;59:258-75

      Authors: Ben Aouicha M, Hadj Taieb MA

      Abstract The exploitation of heterogeneous clinical sources and healthcare records is fundamental in clinical and translational research. The determination of semantic similarity between word pairs is an important component of text understanding that enables the processing and structuring of textual resources.

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    6. Fingerprinting Biomedical Terminologies--Automatic Classification and Visualization of Biomedical Vocabularies through UMLS Semantic Group Profiles.

      Fingerprinting Biomedical Terminologies--Automatic Classification and Visualization of Biomedical Vocabularies through UMLS Semantic Group Profiles.

      Fingerprinting Biomedical Terminologies--Automatic Classification and Visualization of Biomedical Vocabularies through UMLS Semantic Group Profiles.

      Stud Health Technol Inform. 2015;216:771-5

      Authors: Rance B, Le T, Bodenreider O

      Abstract OBJECTIVES: To explore automatic methods for the classification of biomedical vocabularies based on their content. METHODS: We create semantic group profiles for each source vocabulary in the UMLS and compare the vectors using a Euclidian distance.

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    7. Ontology-Driven Semantic Search for Brazilian Portuguese Clinical Notes.

      Ontology-Driven Semantic Search for Brazilian Portuguese Clinical Notes.

      Ontology-Driven Semantic Search for Brazilian Portuguese Clinical Notes.

      Stud Health Technol Inform. 2015;216:1022

      Authors: Hasan SA, Zhu X, Liu J, Barra CM, Oliveira L, Farri O

      Abstract The emerging penetration of Health IT in Latin America (especially in Brazil) has exacerbated the ever-increasing amount of Electronic Health Record (EHR) clinical free text documents.This imposes a workflow efficiency challenge on clinicians who need to synthesize such documents during the typically time-constrained patient care. We propose an ontology-driven semantic search framework that effectively supports clinicians' information synthesis at the point of care.

      PMID: 26262322 [PubMed - indexed for MEDLINE]

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    8. Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

      Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

      Semantic Web Ontology and Data Integration: a Case Study in Aiding Psychiatric Drug Repurposing.

      Stud Health Technol Inform. 2015;216:1051

      Authors: Liang C, Sun J, Tao C

      Abstract There remain significant difficulties selecting probable candidate drugs from existing databases. We describe an ontology-oriented approach to represent the nexus between genes, drugs, phenotypes, symptoms, and diseases from multiple information sources.

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    9. A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

      A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

      A Metadata based Knowledge Discovery Methodology for Seeding Translational Research.

      Stud Health Technol Inform. 2015;216:1071

      Authors: Kothari CR, Payne PR

      Abstract In this paper, we present a semantic, metadata based knowledge discovery methodology for identifying teams of researchers from diverse backgrounds who can collaborate on interdisciplinary research projects: projects in areas that have been identified as high-impact areas at The Ohio State University.

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    10. Confidence ranking of answers based on temporal semantics

      A mechanism is provided, in a data processing system comprising a processor and a memory configured to implement a question and answer system (QA), for providing confidence rankings based on temporal semantics. Responsive to receiving an input question, a set of candidate answers is identified from a knowledge domain based on a correlation between an identified one or more predicates and an identified one or more arguments to the knowledge domain. A confidence score is associated with each of the candidate answers and each confidence score associated with each candidate answer is refined based on a set of temporal characteristics ...

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    11. Deep structured semantic model produced using click-through data

      A deep structured semantic module (DSSM) is described herein which uses a model that is discriminatively trained based on click-through data, e.g., such that a conditional likelihood of clicked documents, given respective queries, is maximized, and a condition likelihood of non-clicked documents, given the queries, is reduced. In operation, after training is complete, the DSSM maps an input item into an output item expressed in a semantic space, using the trained model. To facilitate training and runtime operation, a dimensionality-reduction module (DRM) can reduce the dimensionality of the input item that is fed to the DSSM. A search engine ...

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    12. Deduction of analytic context based on text and semantic layer

      A system includes reception of text, extraction of a plurality of linguistic entities and associated linguistic entity categories based on the text; determination of one or more semantic objects of a semantic layer based on the linguistic entity categories, and generation of a query of the semantic layer based on the plurality of linguistic entities, the associated linguistic entity categories, and the one or more semantic objects. The extraction of the plurality of linguistic entities may include identification of the plurality of linguistic entities from a plurality of semantic object-independent linguistic entity categories and a plurality of semantic object-dependent linguistic ...

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    13. i4 Recruitment Job Application for Semantic Web Developer | Monster.com

      By continuing you agree to Monster's Privacy Policy , Terms of Use and use of cookies . Semantic Web Developer at i4 Recruitment London, London SE19BG About the Job Semantic Web Developer - London, £45-55k plus package Our client is a successful and recognised software / web developer. Due to new business projects they require a Semantic Web Developer to join the team.

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      Mentions: London Linked Data
    14. Geographical location rendering system and method and computer readable recording medium

      A geographical location rendering method executed in a geographical location rendering system for identifying at least one semantic region is provided. A density clustering is performed on a plurality of user generated contents of respective geographical location name information to generate a plurality of region candidates. A name extraction is performed on the region candidates to extract and confirm a common region name of the region candidates as a name of the semantic region. A region scope of the region candidates is detected as a location scope of the semantic region according to a spatial density analysis.

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    15. Knowledge-based editor with natural language interface

      A computer-implemented method for knowledge based ontology editing, is provided. The method receives a language instance to update a knowledge base, using a computer. The method semantically parses the language instance to detect an ontology for editing. The method maps one or more nodes for the ontology for editing based on an ontology database and the knowledge base. The method determines whether the mapped nodes are defined or undefined within the knowledge base. The method calculates a first confidence score based on a number of the defined and undefined mapped nodes. Furthermore, the method updates the knowledge base when the ...

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    16. Semi-automated evaluation of biomedical ontologies for the biobanking domain based on competency questions.

      Semi-automated evaluation of biomedical ontologies for the biobanking domain based on competency questions.

      Semi-automated evaluation of biomedical ontologies for the biobanking domain based on competency questions.

      Stud Health Technol Inform. 2015;212:65-72

      Authors: Hofer P, Neururer S, Hauffe H, Insam T, Zeilner A, Göbel G

      Abstract BACKGROUND: Biosample collections and biobank information systems have become a key enabler for medical research. Therefore it is important to identify potentially relevant ontologies to semantically enrich information related to the biobanking domain.

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    17. Cross-language text clustering

      Methods are described for performing clustering or classification of texts of different languages. Language-independent semantic structures (LISS) are constructed before clustering is performed. These structures reflect lexical, morphological, syntactic, and semantic properties of texts. The methods suggested are able to perform cross-language text clustering which is based on the meaning derived from texts. The methods are applicable to genre classification, topic detection, news analysis, authorship analysis, internet searches, and creating corpora for other tasks, etc.

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    18. Evaluating Semantic Similarity between Chinese Biomedical Terms through Multiple Ontologies with Score Normalization: An Initial Study.

      Evaluating Semantic Similarity between Chinese Biomedical Terms through Multiple Ontologies with Score Normalization: An Initial Study.

      Evaluating Semantic Similarity between Chinese Biomedical Terms through Multiple Ontologies with Score Normalization: An Initial Study.

      J Biomed Inform. 2016 Oct 31;:

      Authors: Ning W, Yu M, Kong D

      Abstract BACKGROUND: Semantic similarity estimation significantly promotes the understanding of natural language resources and supports medical decision making.

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      Mentions: China Umls
    19. Semantic integration of medication data into the EHOP Clinical Data Warehouse.

      Semantic integration of medication data into the EHOP Clinical Data Warehouse.

      Semantic integration of medication data into the EHOP Clinical Data Warehouse.

      Stud Health Technol Inform. 2015;210:702-6

      Authors: Delamarre D, Bouzille G, Dalleau K, Courtel D, Cuggia M

      Abstract UNLABELLED: Reusing medication data is crucial for many medical research domains. Semantic integration of such data in clinical data warehouse (CDW) is quite challenging. Our objective was to develop a reliable and scalable method for integrating prescription data into EHOP (a French CDW).

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    25-48 of 4047 « 1 2 3 4 5 ... 167 168 169 »
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