Ontology Personalization: An Approach Based on Conceptual Prototypicality

  • Xavier Aimé
  • Frédéric Furst
  • Pascale Kuntz
  • Francky Trichet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5731)


With the current emergence of Cognitive Sciences and the development of Knowledge Management applications in Social and Human Sciences, Subjective Knowledge becomes an unavoidable subject and a real challenge, which must be integrated and developed in Ontology Engineering and Ontology-based Information Retrieval. This paper introduces a new approach dedicated to the Personalization of a Domain Ontology. Inspired by works in Cognitive Psychology, our work is based on a process which aims at capturing the user-sensitive degree of truth of the categorisation process, that is the one which is really perceived by the end-user. Practically, this process consists in decorating the Specialisation/Generalisation links (i.e. the ISA links) of the hierarchy of concepts with a specific gradient. As this gradient is defined according to the three aspects of the semiotic triangle (i.e. intensional, extensional and expressional dimension), we call it Semiotic-based Prototypicality Gradient. It enrichs the initial formal semantics of an ontology by adding a pragmatics defined according to a context of use which depends on parameters like culture, educational background and/or emotional context of the end-user.


Contextual Ontology Typicality Categorisation Conceptual prototypicality Semiotic measure Information Retrieval Personalisation Semantic Web Pragmatic Web 


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  1. 1.
    Yeung, C.M.A., 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.
    Yeung, C.M.A., 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
  3. 3.
    Bluck, S., Li, K.: Predicting memory completeness and accuracy: Emotion and exposure in repeated autobiographical recall. Applied Cognitive Psychology (15), 145–158 (2001)CrossRefGoogle Scholar
  4. 4.
    Gabora, D.L.M., Eleanor Rosch, D., Diederik Aerts, D.: Toward an ecological theory of concepts. Ecological Psychology 20(1-2), 84–116 (2008)CrossRefGoogle Scholar
  5. 5.
    Gomez-Perez, A., Fernandez-Lopez, M., Corcho, O.: Ontological Engineering. In: Advanced Information and Knowledge Processing. Springer, Heidelberg (2003)Google Scholar
  6. 6.
    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
  7. 7.
    Harnad, S.: Categorical perception. Encyclopedia of Cognitive Science LXVII(4) (2003)Google Scholar
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Ogden, C.K., Richards, L.A.: The Meaning of Meaning: A Study of the Influence of Language Upon Thought and of the Science of Symbolism, Harcourt (1989), ISBN-13: 978-0156584463Google Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Peirce, C.S.: The Essential Peirce: Selected Philosophical Writings, pp. 1893–1913. Indiana University Press (1998) (paperback)Google Scholar
  13. 13.
    Resnik, P.: Using information content to evaluate semantic similarity in a taxonomy. In: 14th International Joint Conference on Artificial Intelligence (IJCAI 1995), Montral, August 1995, vol. 1, pp. 448–453 (1995)Google Scholar
  14. 14.
    Rosch, E.: Cognitive reference points. Cognitive Psychology (7), 532–547 (1975)CrossRefGoogle Scholar
  15. 15.
    Tversky, A., Kahneman, D.: Judgment under uncertainty: Heuristics and biases. Science (185), 1124–1131 (1974)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

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

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