Integration of Heterogeneous, Imprecise and Incomplete Data: An Application to the Microbiological Risk Assessment

  • Patrice Buche
  • Ollivier Haemmerle
  • Rallou Thomopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2871)


This paper presents an information system developed to help the assessment of the microbiological risk in food products. UQS (Unified Querying System) is composed of two distinct bases (a relational database and a conceptual graph knowledge base) which are integrated by means of a uniform querying language. The specificity of the system is that both bases include fuzzy data. Moreover, UQS allows the expression of preferences into the queries, by means of the fuzzy set theory.


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  1. 1.
    Sowa, J.F.: Conceptual structures - Information processing in Mind and Machine. Addison-Welsey, London (1984)MATHGoogle Scholar
  2. 2.
    Bosc, P., Lietard, L., Pivert, O.: Soft querying, a new feature for database management system. In: Karagiannis, D. (ed.) DEXA 1994. LNCS, vol. 856, pp. 631–640. Springer, Heidelberg (1994)Google Scholar
  3. 3.
    Prade, H.: Lipski’s approach to incomplete information data bases restated and generalized in the setting of zadeh’s possibility theory. Information Systems 9(1), 27–42 (1984)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Mugnier, M.L., Chein, M.: Représenter des connaissances et raisonner avec des graphes. Revue d’Intelligence Artificielle 10(1), 7–56 (1996)MATHGoogle Scholar
  5. 5.
    Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems 1, 3–28 (1978)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Ullman, J.D.: Principles of database and knowledge-base systems. Computer Science Press, Rockville (1988)Google Scholar
  8. 8.
    Thomopoulos, R., Buche, P., Haemmerlé, O.: Representation of weakly structured imprecise data for fuzzy querying. To appear in Fuzzy Sets and Systems (2003)Google Scholar
  9. 9.
    Buche, P., Loiseau, S.: Using contextual fuzzy views to query imprecise data. In: Bench-Capon, T.J.M., Soda, G., Tjoa, A.M. (eds.) DEXA 1999. LNCS, vol. 1677, pp. 460–472. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  10. 10.
    Buche, P., Haemmerlé, O.: Towards a unified querying system of both structured and semi-structured imprecise data using fuzzy views. In: Ganter, B., Mineau, G.W. (eds.) ICCS 2000. LNCS, vol. 1867, pp. 207–220. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  11. 11.
    Chein, M., Mugnier, M.L.: Conceptual graphs, fundamental notions. Revue d’Intelligence Artificielle 6(4), 365–406 (1992)Google Scholar
  12. 12.
    Thomopoulos, R., Bosc, P., Buche, P., Haemmerlé, O.: Logical interpretation of fuzzy conceptual graphs. In: Proceedings of the NAFIPS 2003 Conference, Chicago, USA (July 2003) (to appear)Google Scholar
  13. 13.
    Mugnier, M.L., Chein, M.: Polynomial algorithms for projection and matching. In: Pfeiffer, H.D., Nagle, T.E. (eds.) Conceptual Structures: Theory and Implementation. LNCS, vol. 754, pp. 239–251. Springer, Heidelberg (1993)Google Scholar
  14. 14.
    Genest, D., Salvat, E.: A platform allowing typed nested graphs: How cogito became cogitant. In: Mugnier, M.-L., Chein, M. (eds.) ICCS 1998. LNCS (LNAI), vol. 1453, pp. 154–161. Springer, Heidelberg (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Patrice Buche
    • 1
  • Ollivier Haemmerle
    • 1
    • 2
  • Rallou Thomopoulos
    • 1
  1. 1.UMR INA P-G/INRA BIAParis Cedex 05France
  2. 2.LRI (UMR CNRS 8623 – Universite Paris-Sud) / INRIA (Futurs)Orsay CedexFrance

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