• DocumentCode
    2709656
  • Title

    Background knowledge driven ontology discovery

  • Author

    Chen, Shan ; Alahakoon, Damminda ; Indrawan, Maria

  • Author_Institution
    Sch. of Comput. Sci. & Software Eng., Monash Univ., Clayton, Vic., Australia
  • fYear
    2005
  • fDate
    29 March-1 April 2005
  • Firstpage
    202
  • Lastpage
    207
  • Abstract
    We have previously proposed a GSOM-based hybrid model for automatic discovery of ontology, the first step towards semi-automation of ontology construction. One of the shortcomings of this previous model is the use of a threshold for selecting abstraction levels. The threshold might introduce an inappropriate concept and cause information loss. In this paper, we introduce a new parameter called context ratio (cr) to overcome this drawback. The cr is used as stopping criteria for traversing hypernyms and identifying appropriate abstraction levels. It allows us to extend the previously proposed framework to integrate methodology for multiple inheritance validation in the discovered ontology.
  • Keywords
    data mining; ontologies (artificial intelligence); self-organising feature maps; GSOM-based hybrid model; abstraction levels; context ratio; growing self-organising map; knowledge driven ontology discovery; multiple inheritance validation; Automation; Documentation; Knowledge acquisition; Knowledge based systems; Neural networks; Ontologies;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    e-Technology, e-Commerce and e-Service, 2005. EEE '05. Proceedings. The 2005 IEEE International Conference on
  • Print_ISBN
    0-7695-2274-2
  • Type

    conf

  • DOI
    10.1109/EEE.2005.40
  • Filename
    1402295