Graded-Inclusion-Based Information Retrieval Systems

  • Patrick Bosc
  • Vincent Claveau
  • Olivier Pivert
  • Laurent Ughetto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5478)


This paper investigates the use of fuzzy logic mechanisms coming from the database community, namely graded inclusions, to model the information retrieval process. In this framework, documents and queries are represented by fuzzy sets, which are paired with operations like fuzzy implications and T-norms. Through different experiments, it is shown that only some among the wide range of fuzzy operations are relevant for information retrieval. When appropriate settings are chosen, it is possible to mimic classical systems, thus yielding results rivaling those of state-of-the-art systems. These positive results validate the proposed approach, while negative ones give some insights on the properties needed by such a model. Moreover, this paper shows the added-value of this graded inclusion-based model, which gives new and theoretically grounded ways for a user to easily weight his query terms, to include negative information in his queries, or to expand them with related terms.


IRS models fuzzy logic graded inclusion fuzzy implication query expressiveness 


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  1. 1.
    Bosc, P., Pivert, O.: On the use of tolerant graded inclusions in information retrieval. In: Proceedings of CORIA 2008, pp. 321–336 (2008)Google Scholar
  2. 2.
    Buell, D.: An analysis of some fuzzy subset applications to information retrieval systems. Fuzzy Sets & Systems 7, 35–42 (1982)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Kraft, D.H., Pasi, G., Bordogna, G.: Vagueness and uncertainty in information retrieval: how can fuzzy sets help? In: Proceedings of IWRIDL 2006, pp. 1–10 (2006)Google Scholar
  4. 4.
    Boughanem, M., Loiseau, Y., Prade, H.: Improving document ranking in information retrieval using ordered weighted aggregation and leximin refinement. In: Proceedings of EUSFLAT 2005, pp. 1269–1274 (2005)Google Scholar
  5. 5.
    Herrera-Viedma, E.: Modeling the retrieval process for an information retrieval system using an ordinal fuzzy linguistic approach. Journal of the American Society for Information Science and Technology 52, 460–475 (2001)CrossRefGoogle Scholar
  6. 6.
    Brini, A., Boughanem, M., Dubois, D.: A model for information retrieval based on possibilistic networks. In: Consens, M.P., Navarro, G. (eds.) SPIRE 2005. LNCS, vol. 3772, pp. 271–282. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Herrera-Viedma, E., López-Herrera, A., Luque, M., Porcel, C.: A fuzzy linguistic IRS model based on a 2-tuple fuzzy linguistic approach. International Journal of Uncertainty, Fuzziness and Knowledge-based Systems 15(2), 225–250 (2007)CrossRefMATHGoogle Scholar
  8. 8.
    Oussalah, M., Khan, S., Nefti, S.: Personalized information retrieval system in the framework of fuzzy logic. Expert Systems with Applications 35, 423–433 (2008)CrossRefGoogle Scholar
  9. 9.
    Lalmas, M.: Logical models in information retrieval: Introduction and overview. Information Processing & Management 34(1), 19–33 (1998)CrossRefGoogle Scholar
  10. 10.
    Salton, G., Fox, E., Wu, H.: Extended boolean information retrieval. Communications of the ACM 26(12), 1022–1036 (1983)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Waller, W., Kraft, D.: A mathematical model of a weighted Boolean retrieval system. Information Processing & Management 15, 235–245 (1979)CrossRefMATHGoogle Scholar
  12. 12.
    Buell, D., Kraft, D.: Threshold values and Boolean retrieval systems. Information Processing & Management 17, 127–136 (1981)CrossRefMATHGoogle Scholar
  13. 13.
    Bookstein, A.: Fuzzy requests: an approach to weighted Boolean searches. J. of the American Society for Information Science 31, 240–247 (1980)CrossRefGoogle Scholar
  14. 14.
    Bosc, P., Dubois, D., Pivert, O., Prade, H.: Flexible queries in relational databases – the example of the division operator. Theoretical Comp. Sc. 171, 281–302 (1997)MathSciNetCrossRefMATHGoogle Scholar
  15. 15.
    Fodor, J., Yager, R.: Fuzzy Set-theoretic Operators and Quantifiers. In: Dubois, D., Prade, H. (eds.) Fundamentals of Fuzzy Sets. The Handbook of Fuzzy Sets Series, ch. 1.2, pp. 125–193. Kluwer Academic Publishers, Dordrecht (1999)Google Scholar
  16. 16.
    Voorhees, E.: Using WORDNET for Text Retrieval. In: Fellbaum, C. (ed.) WORDNET: An Electronic Lexical Database, pp. 285–303. MIT Press, Cambridge (1998)Google Scholar
  17. 17.
    Bosc, P., Pivert, O.: On a parameterized antidivision operator for database flexible querying. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds.) DEXA 2008. LNCS, vol. 5181, pp. 652–659. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patrick Bosc
    • 1
  • Vincent Claveau
    • 2
  • Olivier Pivert
    • 1
  • Laurent Ughetto
    • 3
  1. 1.IRISA, ENSSATLannionFrance
  2. 2.IRISA, CNRSRennes cedexFrance
  3. 3.IRISARennesFrance

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