A Formal Model of Fuzzy Ontology with Property Hierarchy and Object Membership

  • Yi Cai
  • Ho-fung Leung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5231)


In this paper, we propose a formal model of fuzzy ontology with property hierarchy by combining theories in cognitive psychology and fuzzy set theory. A formal mechanism used to determine object memberships in concepts is also proposed. In this mechanism, object membership is based on the defining properties of concepts and properties which objects possess. We show that our model is more reasonable in calculating object memberships and more powerful in concept representation than previous models by an example.


Characteristic Vector Description Logic Membership Degree Property Vector Classical View 
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  1. 1.
    Antoniou, G., van Harmelen, F.: A Semantic Web Primer: Cooperative Information Systems. MIT Press, Cambridge (2004)Google Scholar
  2. 2.
    Staab, S., Studer, R.: Handbook on Ontologies. Springer, Heidelberg (2004)CrossRefzbMATHGoogle Scholar
  3. 3.
    Stracia, U.: A fuzzy description logic. In: AAAI 1998/IAAI 1998: Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence, pp. 594–599 (1998)Google Scholar
  4. 4.
    Straccia, U.: Towards a fuzzy description logic for the semantic web. In: Proceedings of the Second European Semantic Web Conference, pp. 167–181 (2005)Google Scholar
  5. 5.
    Stoilos, G., Stamou, G., Tzouvaras, V., Pan, J.Z., Horrocks, I.: The Fuzzy Description Logic f-SHIN. In: Proc. of the International Workshop on Uncertainty Reasoning for the Semantic Web (2005)Google Scholar
  6. 6.
    Au Yeung, C.M., Leung, H.F.: Ontology with likeliness and typicality of objects in concepts. In: Embley, D.W., Olivé, A., Ram, S. (eds.) ER 2006. LNCS, vol. 4215, pp. 98–111. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Murphy, G.L.: The big book of concepts. MIT Press, Cambridge (2002)Google Scholar
  8. 8.
    Galotti., K.M.: Cognitive Psychology In and Out of the Laboratory, 3rd edn. Wadsworth, Belmont (2004)Google Scholar
  9. 9.
    Parsons, J., Wand, Y.: Attribute-based semantic reconciliation of multiple data sources. Journal on Data Semantics 2800, 21–47 (2003)Google Scholar
  10. 10.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Smith, E.E., Medin, D.L.: Categories and Concepts. Harvard University Press (1981)Google Scholar
  12. 12.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The description logic handbook: theory, implementation, and applications. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  13. 13.
    Zadeh, L.A.: Fuzzy logic. Computer 21(4), 83–93 (1988)CrossRefGoogle Scholar
  14. 14.
    Klir, G.J., Yuan, B.: Fuzzy sets and fuzzy logic:theory and applications. Prentice hall PTR, Englewood Cliffs (1995)zbMATHGoogle Scholar
  15. 15.
    Cross, V., Voss, C.R.: Fuzzy ontologies for multilingual document exploitation. In: Proceedings of the 1999 conference of NAFIPS, pp. 392–397 (1999)Google Scholar
  16. 16.
    Yager, R.R.: On mean type aggregation. IEEE Transactions on Systems, Man and Cybernetics 26, 209–221 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yi Cai
    • 1
  • Ho-fung Leung
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong Kong ShatinHong KongChina

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