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A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge

  • Datos identificativos

    Identificador: imarina:9385370
    Autores:
    Sanchez, DavidBatet, Montserrat
    Resumen:
    The Information Content (IC) of a concept quantifies the amount of information it provides when appearing in a context. In the past, IC used to be computed as a function of concept appearance probabilities in corpora, but corpora-dependency and data sparseness hampered results. Recently, some other authors tried to overcome previous approaches, estimating IC from the knowledge modeled in an ontology. In this paper, the authors develop this idea, by proposing a new model to compute the IC of a concept exploiting the taxonomic knowledge modeled in an ontology. In comparison with related works, their proposal aims to better capture semantic evidences found in the ontology. To test the authors' approach, they have applied it to well-known semantic similarity measures, which were evaluated using standard benchmarks. Results show that the use of the authors' model produces, in most cases, more accurate similarity estimations than related works.
  • Otros:

    Autor según el artículo: Sanchez, David; Batet, Montserrat
    Departamento: Enginyeria Informàtica i Matemàtiques
    Autor/es de la URV: Batet Sanromà, Montserrat / Sánchez Ruenes, David
    Palabras clave: Semantic similarity estimation Semantic similarity Semantic similarit Semantic knowledge Relatednes Ontologies Knowledge management Information content Computational linguistics
    Resumen: The Information Content (IC) of a concept quantifies the amount of information it provides when appearing in a context. In the past, IC used to be computed as a function of concept appearance probabilities in corpora, but corpora-dependency and data sparseness hampered results. Recently, some other authors tried to overcome previous approaches, estimating IC from the knowledge modeled in an ontology. In this paper, the authors develop this idea, by proposing a new model to compute the IC of a concept exploiting the taxonomic knowledge modeled in an ontology. In comparison with related works, their proposal aims to better capture semantic evidences found in the ontology. To test the authors' approach, they have applied it to well-known semantic similarity measures, which were evaluated using standard benchmarks. Results show that the use of the authors' model produces, in most cases, more accurate similarity estimations than related works.
    Áreas temáticas: Social statistics and informatics Science and technology studies Planejamento urbano e regional / demografia Pedagogical & educational research Linguistics Library and information science Interdisciplinary research in the social sciences Interdisciplinary research in the humanities Information systems Computer science, information systems Computer science, artificial intelligence Computer networks and communications Ciencias sociales Ciencias humanas Business and management
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: montserrat.batet@urv.cat david.sanchez@urv.cat
    Identificador del autor: 0000-0001-8174-7592 0000-0001-7275-7887
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/submittedVersion
    Enlace a la fuente original: https://www.igi-global.com/article/new-model-compute-information-content/70742
    Referencia al articulo segun fuente origial: International Journal On Semantic Web And Information Systems. 8 (2): 34-50
    Referencia de l'ítem segons les normes APA: Sanchez, David; Batet, Montserrat (2012). A New Model to Compute the Information Content of Concepts from Taxonomic Knowledge. International Journal On Semantic Web And Information Systems, 8(2), 34-50. DOI: 10.4018/jswis.2012040102
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    DOI del artículo: 10.4018/jswis.2012040102
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2012
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Computer Networks and Communications,Computer Science, Artificial Intelligence,Computer Science, Information Systems,Information Systems
    Semantic similarity estimation
    Semantic similarity
    Semantic similarit
    Semantic knowledge
    Relatednes
    Ontologies
    Knowledge management
    Information content
    Computational linguistics
    Social statistics and informatics
    Science and technology studies
    Planejamento urbano e regional / demografia
    Pedagogical & educational research
    Linguistics
    Library and information science
    Interdisciplinary research in the social sciences
    Interdisciplinary research in the humanities
    Information systems
    Computer science, information systems
    Computer science, artificial intelligence
    Computer networks and communications
    Ciencias sociales
    Ciencias humanas
    Business and management
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