Information Retrieval

, Volume 12, Issue 2, pp 179–200 | Cite as

A computer-supported learning system to help teachers to teach Fuzzy Information Retrieval Systems

  • E. Herrera-Viedma
  • A. G. López-Herrera
  • S. Alonso
  • J. M. Moreno
  • F. J. Cabrerizo
  • C. Porcel


This paper describes a computer-supported learning system to teach students the principles and concepts of Fuzzy Information Retrieval Systems based on weighted queries. This tool is used to support the teacher’s activity in the degree course Information Retrieval Systems Based on Artificial Intelligence at the Faculty of Library and Information Sciences at the University of Granada. Learning of languages of weighted queries in Fuzzy Information Retrieval Systems is complex because it is very difficult to understand the different semantics that could be associated to the weights of queries together with their respective strategies of query evaluation. We have developed and implemented this computer-supported education system because it allows to support the teacher’s activity in the classroom to teach the use of weighted queries in FIRSs and it helps students to develop self-learning processes on the use of such queries. We have evaluated the performance of its use in the learning process according to the students’ perceptions and their results obtained in the course’s exams. We have observed that using this software tool the students learn better the management of the weighted query languages and then their performance in the exams is improved.


Teaching Education Weighted queries Fuzzy connectives Fuzzy Information Retrieval 



This work has been supported by the projects: FUZZY-LING, Ref. TIN2007-61079 and SAINFOWEB, Cod. 00602.


  1. Alessi, S. M., & Trollip, S. R. (1991). Computer-based instruction: Methods and development (2nd ed.). Englewood Cliffs, NJ: Prentince-Hall.Google Scholar
  2. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern Information Retrieval. Addison-Wesley.Google Scholar
  3. Bonissone, P. P. (1997). Soft computing: The convergence of emerging reasoning technologies. Soft Computing, 1(1), 6–18.MathSciNetGoogle Scholar
  4. Bordogna, G., & Pasi, G. (1993). A fuzzy linguistic approach generalizing boolean information retrieval: A model and its evaluation. Journal of the American Society for Information Science, 44(2), 70–82.CrossRefGoogle Scholar
  5. Buell, D., & Kraft, D. H. (1981). Threshold values and boolean retrieval systems. Information Processing & Management, 17, 127–136.zbMATHCrossRefGoogle Scholar
  6. Caruso, E. (1981). Computer aids to learning online retrieval. Annual Review of Information Science and Technology, 17, 317–336.Google Scholar
  7. Chau, M., Huang, Z., & Chen, H. (2003). Teaching key topics in computer science and information systems through a Web search engine project. ACM Journal of Educational Resources in Computing, 3(3), 1–14.CrossRefGoogle Scholar
  8. Chiclana, F., Herrera-Viedma, E., Herrera, F., & Alonso, S. (2004). Induced ordered weighted geometric operators and their use in the aggregation of multiplicative preference relations. International Journal of Intelligent Systems, 19, 233–255.zbMATHCrossRefGoogle Scholar
  9. Chiclana, F., Herrera-Viedma, E., Herrera, F., & Alonso, S. (2007). Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations. European Journal of Operational Research, 182(1), 383–399.Google Scholar
  10. Crestani, F., & Pasi, G. (2000). Soft Computing in Information Retrieval: Techniques and applications. Studies in Fuzziness and Soft Computing Series (Vol. 50). Physica-Verlag.Google Scholar
  11. Cronje, J. C., & Fouche, J. (2008). Alternatives in evaluating multimedia in secondary school science teaching. Computers & Education, 51, 659–683.CrossRefGoogle Scholar
  12. Eteokleous, N. (2008). Evaluating computer technology integration in a centralized school system. Computers & Education, 51, 669–686.CrossRefGoogle Scholar
  13. Griffith, J. C., & Norton, N. P. (1981). A computer assisted instruction program for end users on an automated information retrieval systems. In Proceedings of the second national online meeting, pp. 239–248, New York.Google Scholar
  14. Halttunen, K., & Sormunen, E. (2000). Learning information retrieval through an educational game. Is gaming sufficient for learning? Education for Information, 18(4), 289–311.Google Scholar
  15. Hegarty, M. (2004). Dynamic visualizations and learning: Getting to the difficult questions. Learning and Instructions, 14, 343–351.CrossRefGoogle Scholar
  16. Herrera, F., & Herrera-Viedma, E. (1997). Aggregation operators for linguistic weighted information. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans, 27, 646–656.CrossRefGoogle Scholar
  17. Herrera, F., & Herrera-Viedma, E. (2000). Linguistic decision analisys: Steps for solving decision problems under linguistic information. Fuzzy Sets and Systems, 115, 67–82.zbMATHCrossRefMathSciNetGoogle Scholar
  18. Herrera, F., Herrera-Viedma, E., & Verdegay, J. L. (1996). Direct approach processes in group decision making using linguistic OWA operators. Fuzzy Sets and Systems, 79, 175–190.zbMATHCrossRefMathSciNetGoogle Scholar
  19. Herrera-Viedma, E. (2001a). An information retrieval system with ordinal linguistic weighted queries based on two weighting elements. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9, 77–88.zbMATHCrossRefMathSciNetGoogle Scholar
  20. Herrera-Viedma, E. (2001b). Modelling 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(6), 460–475.CrossRefGoogle Scholar
  21. Herrera-Viedma, E., & López-Herrera, A. G. (2007). A model of information retrieval system with unbalanced fuzzy linguistic information. International Journal of Intelligent Systems, 22(11), 1197–1214.zbMATHCrossRefGoogle Scholar
  22. Herrera-Viedma, E., Cordón, O., Luque, M., López, A. G., & Muñoz, A. M. (2003). A model of fuzzy linguistic IRS based on multi-granular linguistic information. International Journal of Approximate Reasoning, 34, 221–239.zbMATHCrossRefMathSciNetGoogle Scholar
  23. Herrera Viedma, E., López Herrera, A. G., & Porcel, C. (2005). Tuning the matching function for a threshold weighting semantics in a linguistic information retrieval system. International Journal of Intelligent Systems, 20, 921–937.zbMATHCrossRefGoogle Scholar
  24. Herrera-Viedma, E., Pasi, G., & Crestani, F. (2006). Soft computing in Web Information Retrieval: Models and applications. Studies in fuzziness and soft computing series (Vol. 197). Physica-Verlag.Google Scholar
  25. Herrera-Viedma, E., López-Herrera, A. G., Luque, M., & Porcel, C. (2007). 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.zbMATHCrossRefGoogle Scholar
  26. Hull, D. (1996). Stemming algorithms: A case study for detailed evaluation. Journal of the American Society for Information Science, 52(6), 70–84.CrossRefGoogle Scholar
  27. Kraft, D. H., Bordogna, G., & Pasi, G. (1994). An extended fuzzy linguistic approach to generalize boolean information retrieval. Information Sciences, 2, 119–134.zbMATHCrossRefGoogle Scholar
  28. Levin, J., & Fox, J. (2006). Elementary statistics in social research. Boston: Allyn & Bacon.Google Scholar
  29. Mann, H. B., & Whitney, D. R. (1947). On a test of whether one of two random variables is stochastically larger than the other. The Annals of Mathematical Statistics, 18(1), 50–60.zbMATHCrossRefMathSciNetGoogle Scholar
  30. Markey, K., & Atherton, P. (1978). ONTAP online training and practice manual for ERIC data base searchers. Syracuse, New York: ERIC Clearinghouse on Information Sources, Syracuse University.Google Scholar
  31. Myers, J. L., & Well, A. D. (2006). Research design and statistical analysis (2nd ed.). Lawrence Erlbaum.Google Scholar
  32. Nikravesh, M., Loia, V., & Azvine, B. (2002). Special issue on fuzzy logic and the internet (flint): Internet, world wide Web and search engines. Soft Computing, 6(5), 287–299.zbMATHGoogle Scholar
  33. Salton, G., & McGill, M. J. (1983). An introduction to Modern Information Retrieval. McGraw-Hill.Google Scholar
  34. Sheskin, D. J. (2003). Handbook of parametric and nonparametric statistical procedures. CRC Press.Google Scholar
  35. Spearman, C. (1904). The proof and measurement of association between two things. The American Journal or Psychology, 15, 72–101.CrossRefGoogle Scholar
  36. Sprent, P. (1993). Applied nonparametric statistical methods. London: Chapman & Hall.Google Scholar
  37. Stratford, S. (1997). A review of computer-based model research in precollege science classrooms. Journal of Computers in Mathematics and Science Teaching, 16(1), 3–23.Google Scholar
  38. Waller, W. G., & Kraft, D. H. (1979). A mathematical model of a weighted boolean retrieval system. Information Processing & Management, 15, 235–245.zbMATHCrossRefGoogle Scholar
  39. Yager, R. R. (1987). A note on weighted queries in information retrieval systems. Journal of the American Society of Information Sciences, 38, 23–24.CrossRefGoogle Scholar
  40. Yager, R.R. (1988). On ordered weighted averaging aggregation operators in multicriteria decision making. IEEE Transactions on Systems, Man, and Cybernetics, 18, 83–190.Google Scholar
  41. Yager, R. R., & Filev, D. P. (1999). Induced ordered weighted averaging operators. IEEE Transaction on Systems, Man and Cybernetics, 29, 141–150.CrossRefGoogle Scholar
  42. Yaman, M., Nerdel, C., & Bayrhuber, H. (2008). The effects of instructional support and learner interests when learning using computer simulations. Computers & Education, 51, 1784–1794.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • E. Herrera-Viedma
    • 1
  • A. G. López-Herrera
    • 1
  • S. Alonso
    • 2
  • J. M. Moreno
    • 3
  • F. J. Cabrerizo
    • 4
  • C. Porcel
    • 5
  1. 1.Department of Computer Sciences and A.I.University of GranadaGranadaSpain
  2. 2.Department of Software EngineeringUniversity of GranadaGranadaSpain
  3. 3.Department of Information and Communication EngineeringUniversity of MurciaMurciaSpain
  4. 4.Department of Software Engineering and Computer SystemsDistance Learning University of Spain (UNED)MadridSpain
  5. 5.Department of Computer SciencesUniversity of JaénJaénSpain

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