• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Ontology Mapping based on Similarity Measure and Fuzzy Logic

    Thumbnail
    View/Open
    46374_1.pdf (51.37Kb)
    Author(s)
    Niwattanakul, S.
    Martin, Philippe
    Eboueya, M.
    Khaimook, K.
    Griffith University Author(s)
    Martin, Philippe A.
    Year published
    2007
    Metadata
    Show full item record
    Abstract
    In this paper, we present a method of an ontology mapping based on a similarity measure and Fuzzy logic in order to classify (i) the similarity of the ontology structure of learning object repositories and (ii) LOR which stores metadata of learning objects based on our ontology model. In this model, values of the ontology similarity are computed for concepts, properties, and relations. The ontology similarity uses parameters based on the Fuzzy Control Language (FCL) which consists of a fuzzy set of the ontology similarity ("Less", "Same", "More"), 7 classes of ontology similarity, and rules of the classification of ontologies. ...
    View more >
    In this paper, we present a method of an ontology mapping based on a similarity measure and Fuzzy logic in order to classify (i) the similarity of the ontology structure of learning object repositories and (ii) LOR which stores metadata of learning objects based on our ontology model. In this model, values of the ontology similarity are computed for concepts, properties, and relations. The ontology similarity uses parameters based on the Fuzzy Control Language (FCL) which consists of a fuzzy set of the ontology similarity ("Less", "Same", "More"), 7 classes of ontology similarity, and rules of the classification of ontologies. The formula of similarity measure by the Jaccard's coefficient is applied to map a similarity of ontology structures. At the end of the article, we show an experience of implementation this model as a prototype.
    View less >
    Conference Title
    Proceedings of E-Learn 2007
    Publisher URI
    http://www.editlib.org/p/26800
    Copyright Statement
    © AACE. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Reprinted from the Proceedings of E-Learn 2007 with permission of AACE (http://www.aace.org).
    Publication URI
    http://hdl.handle.net/10072/18649
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander