Advertisement

Conceptual Graphs as Cooperative Formalism to Build and Validate a Domain Expertise

  • Rallou Thomopoulos
  • Jean-François Baget
  • Ollivier Haemmerlé
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4604)

Abstract

This work takes place in the general context of the construction and validation of a domain expertise. It aims at the cooperation of two kinds of knowledge, heterogeneous by their granularity levels and their formalisms: expert statements represented in the conceptual graph model and experimental data represented in the relational model. We propose to automate two stages: firstly, the generation of an ontology (terminological part of the conceptual graph model) guided both by the relational schema and by the data it contains; secondly, the evaluation of the validity of the expert statements within the experimental data, using annotated conceptual graph patterns.

Keywords

Relational Database Expert Knowledge Atomic Formula Relational Schema Formal Concept Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, London, UK (1984)zbMATHGoogle Scholar
  2. 2.
    Bos, C., Botella, B., Vanheeghe, P.: Modeling and Simulating Human Behaviors with Conceptual Graphs. In: Delugach, H.S., Keeler, M.A., Searle, L., Lukose, D., Sowa, J.F. (eds.) ICCS 1997. LNCS, vol. 1257, pp. 275–289. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  3. 3.
    Genest, D.: Extension du modèle des graphes conceptuels pour la recherche d’informations. PhD thesis, Université Montpellier II (December 2000)Google Scholar
  4. 4.
    Mugnier, M.-L.: Knowledge Representation and Reasoning based on Graph Homomorphism. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 172–192. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Pernelle, N., Rousset, M.C., Ventos, V.: Automatic construction and refinement of a class hierarchy over multi-valued data. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 386–398. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Tilley, T.A., Cole, R.J., Becker, P., Eklund, P.W.: A survey of formal concept analysis support for software engineering activities. In: Ganter, B., Stumme, G., Wille, R. (eds.) Formal Concept Analysis. LNCS (LNAI), vol. 3626, pp. 250–271. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Sendall, S., Kozaczynski, W.: Model transformation: The heart and soul of model-driven software development. IEEE Software 20(5), 42–45 (2003)CrossRefGoogle Scholar
  8. 8.
    Euzenat, J., Le Bach, T., Barrasa, J., Bouquet, P., De Bo, J., Dieng-Kuntz, R., Ehrig, M., Hauswirth, M., Jarrar, M., Lara, R., Maynard, D., Napoli, A., Stamou, G., Stuckenschmidt, H., Shvaiko, P., Tessaris, S., Van Acker, S., Zaihrayeu, I.: State of the art on ontology alignment. deliverable 2.2.3, Knowledge web NoE (2004)Google Scholar
  9. 9.
    Kolaitis, P.G., Vardi, M.Y.: Conjunctive-Query Containment and Constraint Satisfaction. In: Proceedings of PODS 1998 (1998)Google Scholar
  10. 10.
    Haemmerlé, O., Buche, P., Thomopoulos, R.: The MIEL system: uniform interrogation of structured and weakly structured imprecise data. Journal of Intelligent Information Systems (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Rallou Thomopoulos
    • 1
    • 2
  • Jean-François Baget
    • 3
    • 2
  • Ollivier Haemmerlé
    • 4
  1. 1.INRA, UMR1208, F-34060 Montpellier cedex 1France
  2. 2.LIRMM (CNRS & Université Montpellier II), F-34392 Montpellier cedex 5France
  3. 3.LIG/INRIA Rhône-Alpes, F-38334 St-Ismier cedexFrance
  4. 4.IRIT, Université Toulouse le Mirail, F-31058 Toulouse cedexFrance

Personalised recommendations