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Enabling semantic similarity estimation across multiple ontologies: An evaluation in the biomedical domain

  • Identification data

    Identifier: imarina:9245869
    Authors:
    Sanchez, DavidSole-Ribalta, AlbertBatet, MontserratSerratosa, Francesc
    Abstract:
    The estimation of the semantic similarity between terms provides a valuable tool to enable the understanding of textual resources. Many semantic similarity computation paradigms have been proposed both as general-purpose solutions or framed in concrete fields such as biomedicine. In particular, ontology-based approaches have been very successful due to their efficiency, scalability, lack of constraints and thanks to the availability of large and consensus ontologies (like Word Net or those in the UMLS). These measures, however, are hampered by the fact that only one ontology is exploited and, hence, their recall depends on the ontological detail and coverage. In recent years, some authors have extended some of the existing methodologies to support multiple ontologies. The problem of integrating heterogeneous knowledge sources is tackled by means of simple terminological matchings between ontological concepts. In this paper, we aim to improve these methods by analysing the similarity between the modelled taxonomical knowledge and the structure of different ontologies. As a result, we are able to better discover the commonalities between different ontologies and hence, improve the accuracy of the similarity estimation. Two methods are proposed to tackle this task. They have been evaluated and compared with related works by means of several widely-used benchmarks of biomedical terms using two standard ontologies (Word Net and MeSH). Results show that our methods correlate better, compared to related works, with the similarity assessments provided by experts in biomedicine. (C) 2011 Elsevier Inc. All rights reserved.
  • Others:

    Author, as appears in the article.: Sanchez, David; Sole-Ribalta, Albert; Batet, Montserrat; Serratosa, Francesc
    Department: Enginyeria Informàtica i Matemàtiques
    URV's Author/s: Batet Sanromà, Montserrat / Sánchez Ruenes, David / Serratosa Casanelles, Francesc d'Assís / Solé Ribalta, Albert
    Keywords: Wordnet Word net Web Unified medical language system Semantics Semantic similarity Semantic knowledge Multiple ontologies Mesh Medical subject headings Medical informatics Graph edit distance Computation Algorithms
    Abstract: The estimation of the semantic similarity between terms provides a valuable tool to enable the understanding of textual resources. Many semantic similarity computation paradigms have been proposed both as general-purpose solutions or framed in concrete fields such as biomedicine. In particular, ontology-based approaches have been very successful due to their efficiency, scalability, lack of constraints and thanks to the availability of large and consensus ontologies (like Word Net or those in the UMLS). These measures, however, are hampered by the fact that only one ontology is exploited and, hence, their recall depends on the ontological detail and coverage. In recent years, some authors have extended some of the existing methodologies to support multiple ontologies. The problem of integrating heterogeneous knowledge sources is tackled by means of simple terminological matchings between ontological concepts. In this paper, we aim to improve these methods by analysing the similarity between the modelled taxonomical knowledge and the structure of different ontologies. As a result, we are able to better discover the commonalities between different ontologies and hence, improve the accuracy of the similarity estimation. Two methods are proposed to tackle this task. They have been evaluated and compared with related works by means of several widely-used benchmarks of biomedical terms using two standard ontologies (Word Net and MeSH). Results show that our methods correlate better, compared to related works, with the similarity assessments provided by experts in biomedicine. (C) 2011 Elsevier Inc. All rights reserved.
    Thematic Areas: Saúde coletiva Medical informatics Mathematical & computational biology Interdisciplinar Health informatics Ensino Engenharias iv Computer science, interdisciplinary applications Computer science applications Ciências biológicas i Ciência da computação
    licence for use: https://creativecommons.org/licenses/by/3.0/es/
    Author's mail: montserrat.batet@urv.cat albert.sole@urv.cat david.sanchez@urv.cat francesc.serratosa@urv.cat
    Author identifier: 0000-0001-8174-7592 0000-0002-2953-5338 0000-0001-7275-7887 0000-0001-6112-5913
    Record's date: 2024-10-12
    Papper version: info:eu-repo/semantics/publishedVersion
    Link to the original source: https://www.sciencedirect.com/science/article/pii/S1532046411001717?via%3Dihub
    Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
    Papper original source: Journal Of Biomedical Informatics. 45 (1): 141-155
    APA: Sanchez, David; Sole-Ribalta, Albert; Batet, Montserrat; Serratosa, Francesc (2012). Enabling semantic similarity estimation across multiple ontologies: An evaluation in the biomedical domain. Journal Of Biomedical Informatics, 45(1), 141-155. DOI: 10.1016/j.jbi.2011.10.005
    Article's DOI: 10.1016/j.jbi.2011.10.005
    Entity: Universitat Rovira i Virgili
    Journal publication year: 2012
    Publication Type: Journal Publications
  • Keywords:

    Computer Science Applications,Computer Science, Interdisciplinary Applications,Health Informatics,Mathematical & Computational Biology,Medical Informatics
    Wordnet
    Word net
    Web
    Unified medical language system
    Semantics
    Semantic similarity
    Semantic knowledge
    Multiple ontologies
    Mesh
    Medical subject headings
    Medical informatics
    Graph edit distance
    Computation
    Algorithms
    Saúde coletiva
    Medical informatics
    Mathematical & computational biology
    Interdisciplinar
    Health informatics
    Ensino
    Engenharias iv
    Computer science, interdisciplinary applications
    Computer science applications
    Ciências biológicas i
    Ciência da computação
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