• DocumentCode
    1614538
  • Title

    Ontology mapping based on concept classification

  • Author

    Yang, Kai ; Steele, Robert

  • Author_Institution
    Univ. of Sydney, Sydney, NSW, Australia
  • fYear
    2009
  • Firstpage
    656
  • Lastpage
    661
  • Abstract
    Ontology mapping is a crucial task for achieving large scale semantic inter-operation of heterogeneous information sources. Conventional mapping solutions mainly focus on the mapping of a pair of ontologies. While such approaches are effective in creating one-to-one ontology mappings, they are less efficient when dealing with the many-to-many ontology mapping scenarios. To cope with the complexity issue faced by pair-wise mapping solutions, we propose a many-to-many ontology mapping approach. By treating ontology mapping as a concept classification problem, we aim to reduce the number of concept comparisons involved in many-to-many ontology mapping. A preliminary evaluation of performance was also carried out, and results show that the proposed mapping solution requires less concept comparison cycles for many-to-many ontology mapping.
  • Keywords
    ontologies (artificial intelligence); pattern classification; semantic Web; concept classification; heterogeneous information source; large scale semantic inter-operation; many-to-many ontology mapping; semantic Web; Bioinformatics; Classification tree analysis; Computational complexity; Databases; Ecosystems; Electronic mail; Information retrieval; Large-scale systems; Ontologies; Semantic Web; Concept Classification; Many-to-Many Ontology Mapping; Ontology Mapping;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Digital Ecosystems and Technologies, 2009. DEST '09. 3rd IEEE International Conference on
  • Conference_Location
    Istanbul
  • Print_ISBN
    978-1-4244-2345-3
  • Electronic_ISBN
    978-1-4244-2346-0
  • Type

    conf

  • DOI
    10.1109/DEST.2009.5276722
  • Filename
    5276722