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Fuzzy Sets in Information Retrieval: State of the Art and Research Trends

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Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 220))

Abstract

In this contribution some applications of Fuzzy Set Theory to Information Retrieval are described, as well as the more recent outcomes of this research field. Fuzzy Set Theory is applied to Information Retrieval to the main aim to define flexible systems, i.e. systems that can represent and manage the vagueness and subjectivity which characterizes the process of information representation and retrieval.

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References

  1. Abiteboul S., Querying Semi-Structured Data, Lecture Notes In Computer Science, Proceedings of the 6th International Conference on Database Theory, pp. 1–18, 199.

    Google Scholar 

  2. Azzopardi, L., Girolami M. L., and van Rijsbergen C.J., Topic Based Language Models for ad hoc Information Retrieval, in: Proceedings of the International Joint Conference on Neural Networks, Budapest, Hungary, 2004.

    Google Scholar 

  3. Baeza-Yates R., Ribeiro-Neto B., Modern Information Retrieval. Addison-Wesley, Wokingham, UK, 1999.

    Google Scholar 

  4. Bordogna G. and Pasi G., A fuzzy linguistic approach generalizing Boolean information retrieval: a model and its evaluation, Journal of the American Society for Information Science, 44(2), pp. 70–82, 1993.

    Article  Google Scholar 

  5. Bordogna G. and Pasi G., Linguistic aggregation operators in fuzzy information retrieva,. International Journal of Intelligent systems, 10(2), pp. 233–248, 1995.

    Article  Google Scholar 

  6. Bordogna G. and Pasi G., Controlling retrieval trough a user-adaptive representation of documents, International Journal of Approximate Reasoning, 12, 317–339, 1995.

    Article  MATH  MathSciNet  Google Scholar 

  7. Bordogna G. and Pasi G., An Ordinal Information Retrieval Model, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Vol. 9, 2001.

    Google Scholar 

  8. Bordogna G. and Pasi G., Modelling Vagueness in Information Retrieval, in Lectures in Information Retrieval, M. Agosti, F. Crestani and G. Pasi eds., Springer Verlag., 2001.

    Google Scholar 

  9. Bordogna G., Pasi G., and Yager R.R., Soft approaches to distributed information retrieval, International Journal of Intelligent Systems, Vol. 34, pp. 105–120, 2003.

    MATH  MathSciNet  Google Scholar 

  10. Bordogna G. and Pasi G., Soft fusion of Infomation Accesses, Fuzzy Sets and Systems, 148, pp. 205–218, 2004.

    Article  MathSciNet  Google Scholar 

  11. Bordogna G., Pagani M., and Pasi G., A dynamical Hierarchical fuzzy clustering algorithm for document filtering, in “Soft Computing for Information Retrieval on the Web”, Springer Verlag, 2006.

    Google Scholar 

  12. Bordogna G. and Pasi G., Personalized Indexing and Retrieval of Heterogeneous Structured Documents, Information Retrieval, Kluwer, Vol. 8, Issue 2, pp. 301–318, 2005.

    Article  Google Scholar 

  13. Braga D., Campi A.. Damiani E., Pasi G, Lanzi PL, FXPath: flexible querying of XML documents, in Proceedings of EUROFUSE 2002, Varenna, Italy, 2002.

    Google Scholar 

  14. Boughanem M., Loiseau Y., Prade H., Improving document ranking in information retrieval using ordered weighted aggregation and leximin refinement., in: EUSFLAT-LFA 2005, 4th Conference of the European Society for Fuzzy Logic and Technology and 11me Rencontres Francophones sur la Logique Floue et ses Applications, pp. 1269–1274, 2005.

    Google Scholar 

  15. Boughanem M., Pasi G., Prade H., Baziz M., A fuzzy logic approach to information retrieval using an ontology-based representation of documents, in “Fuzzy Logic and the Semantic Web” (E. Sanchez, Ed.), Elsevier Science, 2006.

    Google Scholar 

  16. Brini A., Boughanem M., Dubois D., A Model for Information Retrieval Based on Possibilistic Networks, in: String Processing and Information Retrieval (SPIRE 2005), LNCS, Springer Verlag, pp. 271–282, 2005.

    Google Scholar 

  17. Buell D.A., and Kraft D.H., Threshold values and Boolean retrieval systems, Information Processing & Management 17, pp. 127–136, 1981.

    Article  MATH  Google Scholar 

  18. Crestani F. and Pasi G. eds., Soft Computing in Information Retrieval: Techniques and Applications, Physica Verlag, series Studies in Fuzziness, 2000.

    Google Scholar 

  19. Crestani F. and Pasi G., Soft Information Retrieval: Applications of Fuzzy Set Theory and Neural Networks, in: “Neuro-fuzzy Techniques for Intelligent Information Systems”, N.Kasabov and Robert Kozma Editors, Physica-Verlag , Springer-Verlag Group , pp. 287–313, 1999.

    Google Scholar 

  20. Glover E. J., Lawrence S., Gordon M. D., Birmingham W. P., and Lee Giles C., Web Search – YourWay, Communications of the ACM, 1999.

    Google Scholar 

  21. Hathaway R.J., Bezdek J.C., and Hu Y., Generalized Fuzzy C-Means Clustering Strategies Using Lp Norm Distances, IEEE Transactions on Fuzzy Systems, 8(5), pp. 576–582, 2000.

    Google Scholar 

  22. Herrera-Viedma E., Modeling the Retrieval Process of an Information Retrieval System Using an Ordinal Fuzzy Linguistic Approach, Journal of the American Society for Information Science and Technology (JASIST), Vol. 52 N. 6, pp. 60–475, 2001.

    Google Scholar 

  23. Herrera-Viedma E., Cordon O., Luque M., Lopez A.G., Muñoz A.N., A Model of Fuzzy Linguistic IRS Based on Multi-Granular Linguistic Information, Int. Journal of Approximate Reasoning, 34(3), pp. 221–239, 2003.

    Article  MATH  Google Scholar 

  24. Herrera-Viedma E., Pasi G. and Crestani F. eds., Soft Computing in Web Information Retrieval: Models and Applications, Series of Studies in Fuzziness and Soft Computing, Springer Verlag, 2006.

    Google Scholar 

  25. Fuhr N., Lalmas M eds., Introduction to the Special Issue on INEX, Information Retrieval, Kluwer, 8(4), pp. 515–519, 2005.

    Article  Google Scholar 

  26. Kraft D., Chen J., Martin–Bautista M.J., Vila M.A., Textual Information Retrieval with User Profiles using Fuzzy Clustering and Inferencing, in: Intelligent Exploration of the Web, Szczepaniak P., Segovia J., Kacprzyk J., Zadeh L.A. eds., Studies in Fuzziness and Soft Comp. Series, 111, Physica Verlag, 2003.

    Google Scholar 

  27. Kraft D., Bordogna G., Pasi G., Fuzzy Set Techniques in Information Retrieval, in: “Fuzzy Sets in Approximate Reasoning and Information Systems”, J. C. Bezdek, D. Dubois and H. Prade eds, volume of the series “The Handbooks of Fuzzy Sets Series”, Kluwer Academic Publishers, pp. 469–510, 1999.

    Google Scholar 

  28. Lin K., Ravikuma K., A Similarity-Based Soft Clustering Algorithm for Documents, in: Proceedings of the 7th International Conference on Database Systems for Advanced Applications, pp. 40–47, 2001.

    Google Scholar 

  29. Loiseau Y., Boughanem M., Prade H., Evaluation of term-based queries using possibilistic ontologies, in: Soft Computing for Information Retrieval on the Web, Herrera-Viedma E., Pasi G., Crestani F. Eds., Springer-Verlag, 2006.

    Google Scholar 

  30. Losada D., Diaz-Hermida F. and Bugarin A., Semi-fuzzy quantifiers for information retrieval, in: “Soft Computing in Web Information Retrieval: Models and Applications”, Series of Studies in Fuzziness and Soft Computing, Springer Verlag. Edited by E .Herrera-Viedma, G. Pasi and F. Crestani, volume 197/2006.

    Google Scholar 

  31. Marques Pereira R.A., Molinari A., and Pasi G., Contextual weighted representations and indexing models for the retrieval of HTML documents, Soft Computing, Vol. 9, Issue 7, pp. 481–492, July 2005.

    Google Scholar 

  32. Mendes Rodrigues M.E.S. and Sacks L., A Scalable Hierarchical Fuzzy Clustering Algorithm for Text Mining, in: Proceedings of the 4th International Conference on Recent Advances in Soft Computing, RASC’2004, pp. 269–274, Nottingham, UK, 2004.

    Google Scholar 

  33. Miyamoto S., Fuzzy sets in Information Retrieval and Cluster Analysis. Kluwer Academic Publishers, 1990.

    Google Scholar 

  34. Miyamoto S., Information retrieval based on fuzzy associations, Fuzzy Sets and Systems, 38(2), pp. 191–205, 1990.

    Article  MATH  Google Scholar 

  35. Molinari A., and Pasi G., A Fuzzy Representation of HTML Documents for Information Retrieval Systems, in: Procedings of the IEEE International Conference on Fuzzy Systems, 8–12 September, New Orleans, U.S.A., Vol. 1, pp. 107–112, 1996.

    Google Scholar 

  36. Nomoto, K., Wakayama, S., Kirimoto, T., and Kondo, M., A fuzzy retrieval system based on citation, Systems and Control, 31(10), pp. 748–755, 1987.

    Google Scholar 

  37. Ogawa, Y., Morita, T., and Kobayashi, K., A fuzzy document retrieval system using the keyword connection matrix and a learning method, Fuzzy Sets and Systems, 39(2), pp. 163–179, 1991.

    Article  MathSciNet  Google Scholar 

  38. Pasi G., Modelling Users’ Preferences in Systems for Information Access, International Journal of Intelligent Systems, Vol. 18, pp. 793–808, 2003.

    Article  MATH  Google Scholar 

  39. Pedrycz W., Clustering and Fuzzy Clustering, Chap. 1, in: Knowledge-based clustering, J. Wiley and Son, 2005.

    Google Scholar 

  40. Salton G., Automatic Text Processing - The Transformation, Analysis and Retrieval of Information by Computer, Addison Wesley Publishing Company, 1989.

    Google Scholar 

  41. Salton G., and McGill M.J., Introduction to modern information retrieval. New York, NY: McGraw-Hill, 1983.

    MATH  Google Scholar 

  42. Sparck Jones K. A., A statistical interpretation of term specificity and its application in retrieval, Journal of Documentation, 28(1), pp. 11–20, 1972.

    Google Scholar 

  43. Thomopoulos R., Buche P., Haemmerlé O., Representation of weakly structured imprecise data for fuzzy querying. Fuzzy Sets and Systems, 140, 111–128, 2003.

    Article  MATH  MathSciNet  Google Scholar 

  44. van Rijsbergen C.J., Information Retrieval. London, England, Btterworths & Co., Ltd., 1979.

    Google Scholar 

  45. Vincke P., Multicriteria Decision Aid, John Wiley & Sons, 1992.

    Google Scholar 

  46. Zadeh L. A., The concept of a linguistic variable and its application to approximate reasoning, parts I, II, Information Science, 8, pp. 199–249, pp. 301–357, 1975.

    Google Scholar 

  47. Zadeh L.A., A computational Approach to Fuzzy Quantifiers in Natural Languages, Computing and Mathematics with Applications. 9, 149–184, 1983.

    Article  MATH  MathSciNet  Google Scholar 

  48. Yager, R.R., On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decision Making, IEEE Transactions on Systems Man and Cybernetics, 18(1), pp. 183–190, 1988.

    Article  MATH  MathSciNet  Google Scholar 

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Authors
  1. Gabriella Pasi
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Editor information

Editors and Affiliations

  1. Department of Automatics and Computation, Universidad Pública de Navarra, Campus Arrosadia s/n, 31006 Pamplona, Spain

    Humberto Bustince

  2. Department of Computer Science and Artificial Intelligence (DECSAI), University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain

    Francisco Herrera

  3. Faculty of Mathematics, Universidad Complutense, Plaza de las Ciencias 3, 28040 Madrid, Spain

    Javier Montero

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Pasi, G. (2008). Fuzzy Sets in Information Retrieval: State of the Art and Research Trends. In: Bustince, H., Herrera, F., Montero, J. (eds) Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, vol 220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73723-0_26

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  • DOI: https://doi.org/10.1007/978-3-540-73723-0_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73722-3

  • Online ISBN: 978-3-540-73723-0

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