Conceptual and Lexical Prototypicality Gradients Dedicated to Ontology Personalisation

  • Xavier Aimé
  • Frédéric Furst
  • Pascale Kuntz
  • Francky Trichet
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5332)

Abstract

Since a long time, Domain Ontologies have been limited to scientific and technical domains. This situation has advantaged the sustainable development of “unbiased and universal knowledge”. With the current emergence of Cognitive Sciences and the application of Ontology Engineering to Social and Human Sciences, the need to deal with subjective knowledge becomes more and more crucial. The aim of our work is to develop the notion of Personalised Vernacular Domain Ontology (PVDO). The principle underlying a PVDO consists in considering that an ontology O is not only specific to a delimited domain D, but is also peculiar to an endogroup E which shares a common pragmatics of D. This pragmatics, which complements the formal semantics of D, is defined during a process of ontology personalisation. This process is dependent on a context of use which includes several parameters, and in particular: culture, educational background and emotional state. Thus, ontologies co-evolve with their communities of use, and human interpretation of context in the use. Inspired by works in Cognitive Psychology, our contribution to ontology personalisation is based on the formal definition of two measures which aims at capturing subjective knowledge (i.e. the pragmatics of an ontology for knowledge (re)-using): (1) the conceptual prototypicality gradient evaluates the representativeness of a concept (resp. relation) within a local decomposition of a hierarchy and (2) the lexical prototypicality gradient evaluates the representativeness of a term within a set of terms used to denote a concept (resp. relation). In this way, these gradients aims at reflecting the degree of truth users of ontologies perceive on the is-a hierarchies and to what extent the terms associated to the concepts and relations are representative, respectively.

Keywords

Contextual Ontology Typicality Categorisation Conceptual prototypicality Lexical prototypicality Information Retrieval Subjective knowledge Personalisation Semantic Web Pragmatic Web 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Au Yeung, C.M., Leung, H.F.: Formalizing typicality of objects and context-sensitivity in ontologies. In: AAMAS 2006: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems, pp. 946–948. ACM, New York (2006)CrossRefGoogle Scholar
  2. 2.
    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. 1611–3349. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Baldoni, M., Baroglio, C., Henze, N.: Personalization for the semantic web. In: Reasoning Web, pp. 173–212 (2005)Google Scholar
  4. 4.
    Bluck, S., Li, K.: Predicting memory completeness and accuracy: Emotion and exposure in repeated autobiographical recall. Applied Cognitive Psychology (15), 145–158 (2001)Google Scholar
  5. 5.
    Bouquet, P., Giunchiglia, F., van Harmelen, F., Serafini, L., Stuckenschmidt, H.: Contextualizing ontologies. Journal of Web Semantics 1(4), 325–343 (2004)CrossRefGoogle Scholar
  6. 6.
    Brusilovsky, P., Kobsa, A.: The Adaptive Web: Methods and Strategies of Web Personalization. Springer, Heidelberg (2007) ISBN 978-3-540-72078-2CrossRefGoogle Scholar
  7. 7.
    Gabora, L.M., Rosch, E., Aerts, D.: Toward an ecological theory of concepts. Ecological Psychology 20(1-2), 84–116 (2008)CrossRefGoogle Scholar
  8. 8.
    Gomez-Perez, A., Fernandez-Lopez, M., Corcho, O.: Ontological Engineering. In: Advanced Information and Knowledge Processing. Springer, Heidelberg (2003)Google Scholar
  9. 9.
    Gruber, T.: Toward principles for the design of ontologies used for knowledge sharing. In: Guarino, N., Poli, R. (eds.) Formal Ontology in Conceptual Analysis and Knowledge Representation, Deventer, The Netherlands. Kluwer Academic Publishers, Dordrecht (1993)Google Scholar
  10. 10.
    Harnad, S.: Categorical perception. Encyclopedia of Cognitive Science LXVII(4) (2003)Google Scholar
  11. 11.
    McEvoy, M.E., Nelson, D.L.: Category norms and instance norms for 106 categories of various sizes. American Journal of Psychology 95, 462–472 (1982)CrossRefGoogle Scholar
  12. 12.
    Mikulinger, M., Kedem, P., Paz, D.: Anxiety and categorization-1, the structure and boundaries of mental categories. Personnality and individual differences 11(11), 805–814 (1990)CrossRefGoogle Scholar
  13. 13.
    Park, J., Nanaji, M.: Mood and heuristics: The influence of happy and sad states on sensitivity and bias in stereotyping. Journal of Personality and Social Psychology (78), 1005–1023 (2000)Google Scholar
  14. 14.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), vol. 1, pp. 448–453 (1995)Google Scholar
  15. 15.
    Rosch, E.: Cognitive reference points. Cognitive Psychology (7), 532–547 (1975)Google Scholar
  16. 16.
    Schoop, M., de Moor, A., Dietz, J.L.G.: The pragmatic web: a manifesto. Commun. ACM 49(5), 75–76 (2006)CrossRefGoogle Scholar
  17. 17.
    Singh, M.P.: The pragmatic web. IEEE Internet Computing 6(3), 4–5 (2002)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Smith, E.E., Shoben, E.J., Rips, L.J.: Structure and process in semantic memory: a featural model for semantic decisions. Psychological Review (81), 214–241 (1974)Google Scholar
  19. 19.
    Sparck-Jones, K.: A statistical interpretation of term specificity and its application to retriever. Journal of documentation 28(1), 11–21 (1972)CrossRefGoogle Scholar
  20. 20.
    Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Science (185), 1124–1131 (1974)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Xavier Aimé
    • 1
    • 3
  • Frédéric Furst
    • 2
  • Pascale Kuntz
    • 1
  • Francky Trichet
    • 1
  1. 1.LINA - Laboratoire d’Informatique de Nantes Atlantique (UMR-CNRS 6241)University of Nantes - Team “Knowledge and Decision”Nantes Cedex 03France
  2. 2.MIS - Laboratoire Modélisation, Information et SystèmeUniversity of AmiensAmiens Cedex 01France
  3. 3.Société TENNAXIAParisFrance

Personalised recommendations