Measuring Effectiveness in Fuzzy Information Retrieval

  • María J. Martín-Bautista
  • Daniel Sánchez
  • María-Amparo Vila
  • Henrik. L. Larsen
Part of the Advances in Soft Computing book series (AINSC, volume 7)


We investigate extensions of the classical measurement of effectiveness in information retrieval systems, precision and recall, to situations where the answer is modeled by a fuzzy set, such as in cases where each object in the answer is measured by its relevance to the query. The most used fuzzy extension of the classical precision-recall measure based on Zadeh’s relative cardinality appears to be counter-intuitive in some situations. We propose a new approach to the measurement of effectiveness, based on the evaluation of quantified sentences.


Information Retrieval Membership Degree Fuzzy Subset Information Retrieval System Fuzzy Measure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Buell and Kraft, 1981a) Buell, D.A. and Kraft, D.H. “A Model for a WeightedGoogle Scholar
  2. Retrieval Systems“. Journal of the American Society for Information Science 32(3),pp. 211–216.Google Scholar
  3. Buell and Kraft, 1981b) Buell, D.A. and Kraft, D.H. “Performance Measurement in aGoogle Scholar
  4. Fuzzy Retrieval Enviroment“. In Proc. of the Fourth International Conference on Information Storage and Retrieval,Oakland,CA. ACM/SIGIR Forum, 16(1), pp. 56–62, 1981Google Scholar
  5. (Delgado 1999)
    (Delgado 1999) Delgado, M., Sanchez, D. Martin-Bautista, M.J. and Vila, M.A. A Logic Based Definition of Fuzzy Cardinality. Fuzzy Sets and Systems, Submitted.Google Scholar
  6. (Delgado 2000)
    Delgado, M., Sanchez, D. and Vila, M.A. Fuzzy cardinality based evaluation of quantified sentences. Int. Journal of Approximate Reasoning 23, pp. 2366, 2000.MathSciNetCrossRefGoogle Scholar
  7. (De Luca and Termini 1972).
    De Luca, A. and Termini, S. A definition of a nonprobabilistic entropy in the setting of fuzzy sets theory. Information and Control 20, pp. 301–312, 1972.MathSciNetMATHCrossRefGoogle Scholar
  8. (Martin-Bautista,2000) Martin-Bautista, M.J., Vila, M.A., Larsen, H.L and Sanchez, D. “Fuzzy Genes: Improving Effectiveness of Information Retrieval”. In Proc. of the IEEE Conference on Evolutionary Computation,San Diego, California. (To appear).Google Scholar
  9. Salton and McGill, 1983) Salton, G. and McGill, M.J. Introduction to Modern Information Retrieval. New York: McGraw-Hill.Google Scholar
  10. (Sanchez 1999).
    Sanchez, D. Adquisicidn de relaciones entre atributos en bases de datos relacionales. Ph. D. Thesis, Dept. of Computer Science and A.I., University of Granada, 1999.Google Scholar
  11. (Sanchez and Pierre, 1994).
    (Sanchez and Pierre, 1994) Sanchez, E. and Pierre, P. “Fuzzy Logic and Genetic Algorithms in Information Retrieval”. In Proc. of the Third International Conference on Fuzzy Logic, Neural Nets and Soft Computing,pp. 29–35, Iizuka, Japan.Google Scholar
  12. Zadeh, 1975) Zadeh, L.A. “The concept of a linguistic variable and its application toGoogle Scholar
  13. approximate reasoning I, II and III“. Information Sciences, 8,pp. 199–251, 301–357; 9, 43–80.Google Scholar
  14. (Zadeh 1983).
    Zadeh, L.A. A computational approach to fuzzy quantifiers in natural languages. Computing and Mathematics with Applications, 9 (1), pp. 149–184, 1983.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • María J. Martín-Bautista
    • 1
  • Daniel Sánchez
    • 2
  • María-Amparo Vila
    • 2
  • Henrik. L. Larsen
    • 2
  1. 1.Dpt. of Computer Science and Artificial IntelligenceGranada UniversityGranadaSpain
  2. 2.Department of Computer ScienceRoskilde UniversityRoskildeDenmark

Personalised recommendations