• DocumentCode
    2074943
  • Title

    An Asymmetric Similarity Measure for Ontologies Based on the Feature Contrast Model

  • Author

    Chua, Watson Wei Khong ; Goh, Angela Eck Soong

  • Author_Institution
    Sch. Of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
  • fYear
    2010
  • fDate
    15-18 Feb. 2010
  • Firstpage
    1002
  • Lastpage
    1007
  • Abstract
    Ontology alignment is a time consuming process, especially when the two ontologies to be aligned are large. A fast and accurate ontology similarity can help the user to avoid aligning ontologies without significant similarities. In this paper, we propose an Asymmetric Similarity Measure for Ontologies (ASMO) that measures how similar the source ontology is to the target ontology. Many efficient ontology similarity measures are based on syntactic similarities between entities but these measures are unable to identify concepts represented using synonyms. We introduce a Synset Clustering method (S-Clust) to measure the synonymical similarity of concepts and our experimental results show that S-Clust is able to address the limitations of syntactic similarity measures in only 1.74% of the time taken to do a pairwise synonymical similarity comparison between concepts.
  • Keywords
    ontologies (artificial intelligence); pattern clustering; asymmetric similarity measurement; feature contrast model; ontology alignment; pairwise synonymical similarity; source ontology; synset clustering method; target ontology; Clustering methods; Competitive intelligence; Ontologies; Software measurement; Software systems; Time measurement; Feature Contrast Model; Ontology Alignment; Ontology Similarity; Synset Clustering;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Complex, Intelligent and Software Intensive Systems (CISIS), 2010 International Conference on
  • Conference_Location
    Krakow
  • Print_ISBN
    978-1-4244-5917-9
  • Type

    conf

  • DOI
    10.1109/CISIS.2010.47
  • Filename
    5447382