• DocumentCode
    188510
  • Title

    A Semantic Approach for Ontology Evaluation

  • Author

    Batet, Montserrat ; Sanchez, Dominick

  • Author_Institution
    Dept. of Comput. Eng. & Math., Univ. Rovira i Virgili, Tarragona, Spain
  • fYear
    2014
  • fDate
    10-12 Nov. 2014
  • Firstpage
    138
  • Lastpage
    145
  • Abstract
    In recent years, ontologies have experienced an enormous development due to their importance in knowledge-based systems. Because of the discrepancies that may appear during the modeling of ontologies, and due to the availability of ontologies covering overlapping domains, ontology evaluation is crucial in order to select the most appropriate ontology for a specific application. Many ontology evaluation mechanisms available in the literature assess the quality of ontologies according to their structural features. Even though, most of them propose ad-hoc scores aggregating different features, which lacks semantic and mathematical coherence. In this paper, we present an intuitive measure for ontology evaluation that quantifies the semantic dispersion of the ontology, which is both mathematically and semantically coherent. Our proposal is inspired in the standard notion of numerical dispersion of a sample and on a recent empirical study showing which ontological features can better predict the accuracy of ontologies. Our experiments, performed over a set of widely used ontologies, suggest that our measure positively correlates with such features, while offering a more coherent ontology evaluation score.
  • Keywords
    knowledge based systems; ontologies (artificial intelligence); knowledge-based systems; ontological features; ontologies modelling; ontology evaluation; ontology semantic dispersion; semantic approach; structural features; Accuracy; Dispersion; Numerical models; Ontologies; Semantics; Standards; Taxonomy; Knowledge representation; dispersion; ontology evaluation; semantics;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence (ICTAI), 2014 IEEE 26th International Conference on
  • Conference_Location
    Limassol
  • ISSN
    1082-3409
  • Type

    conf

  • DOI
    10.1109/ICTAI.2014.30
  • Filename
    6984466