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Fuzzy Information Retrieval Systems: A Historical Perspective

  • Donald H. Kraft
  • Erin Colvin
  • Gloria Bordogna
  • Gabriella Pasi
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 326)

Abstract

The application of fuzzy set theory to information retrieval has been applied, specifically to Boolean models. This includes fuzzy indexing procedures defined to represent the varying significance of terms in synthesizing the documents’ contents, the definition of query languages to allow the expression of soft selection conditions, and associative retrieval mechanisms to model fuzzy pseudo-thesauri, fuzzy ontologies, and fuzzy categorizations of documents.

Keywords

Fuzzy Information retrieval Query Imprecision Vagueness Indexing Ememes Geographic information retrieval 

References

  1. 1.
    Bartschi, M.: Requirements for query evaluation in weighted information retrieval. Inf. Process. Manage. 21(4), 291–303 (1985)CrossRefGoogle Scholar
  2. 2.
    Berrut, C., Chiaramella, Y.: Indexing medical reports in a multimedia environment: the RIME experimental approach, pp. 187–197. ACM-SIGIR 89, Boston (1986)Google Scholar
  3. 3.
    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)CrossRefzbMATHGoogle Scholar
  4. 4.
    Bezdek, J.C., Biswas, G., Huang, L.Y.: Transitive closures of fuzzy thesauri for information-retrieval systems. Int. J. Man Mach. Stud. 25(3), 343–356 (1986)CrossRefGoogle Scholar
  5. 5.
    Bookstein, A.: Fuzzy requests: an approach to weighted Boolean searches. J. Am. Soc. Inf. Sci. 31(4), 240–247 (1980)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Bordogna, G., Campi, A., Psaila, G., Ronchi, S.: Disambiguated query suggestions and personalized content-similarity & novelty ranking of clustered results to optimize web searches. Inf. Process. Manage. 201(48), 419–437 (2012)CrossRefGoogle Scholar
  7. 7.
    Bordogna, G., Ghisalberti, G., Psaila, G.: Geographic information retrieval: modeling uncertainty of user’s context. Fuzzy Sets Syst. 196(1), 105–124 (2012)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Bordogna, G., Pasi, G.: The application of fuzzy set theory to model information retrieval. In: Crestani, F., Pasi, G. (eds.) Soft Computing in Information Retrieval: Techniques and Applications. Physica-Verlag (2000)Google Scholar
  9. 9.
    Bordogna, G., Pasi, G.: Linguistic aggregation operators in fuzzy information retrieval. Int. J. Intell. syst. 10(2), 233–248 (1995)CrossRefGoogle Scholar
  10. 10.
    Bordogna, G., Pasi, G.: Controlling information retrieval through a user adaptive representation of documents. Int. J. Approximate Reasoning 12, 317–339 (1995)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Bordogna, G., Pasi, G.: A fuzzy linguistic approach generalizing Boolean information retrieval: a model and its evaluation. J. Am. Soc. Inf. Sci. 44(2), 70–82 (1993)CrossRefGoogle Scholar
  12. 12.
    Bordogna, G., Pasi, G.: An approach to identify ememes on the blogosphere. In: Proceedings of the Workshop NLPOE2012 held in collaboration with the 2012 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Macao, 5-10.12-(2012)Google Scholar
  13. 13.
    Bordogna, G., Pasi, G., Yager, R.: Soft approaches to information retrieval on the WEB. Int. J. Approximate Reasoning 34, 105–120 (2003)CrossRefzbMATHMathSciNetGoogle Scholar
  14. 14.
    Bosc, P.: Fuzzy databases, in Fuzzy sets in approximate reasoning and information systems. In: Bezdek, J., Dubois, D., Prade, H. (eds.) The Handbooks of Fuzzy Sets Series, Kluwer Academic Publishers (1999)Google Scholar
  15. 15.
    Buell, D.A.: A problem in information retrieval with fuzzy sets. J. Am. Soc. Inf. Sci. 36(6), 398–401 (1985)CrossRefGoogle Scholar
  16. 16.
    Buell, D.A.: An analysis of some fuzzy subset applications to information retrieval systems. Fuzzy Sets Syst. 7(1), 35–42 (1982)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Buell, D.A., Kraft, D.H.: Performance measurement in a fuzzy retrieval environment. In: Proceedings of the Fourth International Conference on Information Storage and Retrieval, pp. 56–62. ACM/SIGIR Forum, Oakland, CA, 16(1), 31 May–2 June, 1981Google Scholar
  18. 18.
    Buell, D.A., Kraft, D.H.: A model for a weighted retrieval system. J. Am. Soc. Inf. Sci. 32(3), 211–216 (1981)CrossRefGoogle Scholar
  19. 19.
    Buell, D.A., Kraft, D.H.: Threshold values and Boolean retrieval systems. Inf. Process. Manage. 17, 127–136 (1981)CrossRefzbMATHGoogle Scholar
  20. 20.
    Cater, S.C., Kraft, D.H.: A generalization and clarification of the Waller-Kraft wish-list. Inf. Process. Manage. 25, 15–25 (19R 89)Google Scholar
  21. 21.
    Cater, S.C., Kraft, D.H.: TIRS: a topological information retrieval system satisfying the requirements of the Waller-Kraft wish list. In: Proceedings of the Tenth Annual ACM/SIGIR International Conference on Research and Development in Information Retrieval, pp. 171–180. New Orleans, LA, June 1987Google Scholar
  22. 22.
    da Costa Pereira, C., Dragoni, M., Pasi, G.: Multidimensional relevance: a new aggregation criterion. In: Boughanem, M., Berrut, C., Mothe, J., Soulé-Dupuy, C. (eds.) ECIR 2009. Lecture Notes in Computer Science, vol. 5478, pp. 264–275. Springer (2009)Google Scholar
  23. 23.
    da Costa Pereira, C., Dragoni, M., Pasi, G.: A prioritized and aggregation operator for multidimensional relevance assessment. In: Serra, R., Cucchiara, R. (eds.) AI*IA. Lecture Notes in Computer Science, vol. 5883, pp. 72–81. Springer (2009)Google Scholar
  24. 24.
    da Costa Pereira, C.,  Dragoni, M., Pasi, G.: Multidimensional relevance: prioritized aggregation in a personalized information retrieval setting. Inf. Process. Manage. 48(2), 340–357 (2012)Google Scholar
  25. 25.
    Crestani, F., Lalmas, M., van Rijsbergen, C.J., Campbell, I.: Is this document relevant? … Probably. ACM Comput. Surv. 30(4), 528–552 (1998)CrossRefGoogle Scholar
  26. 26.
    Dubois, D., Prade, H.: Possibility Theory: An Approach to Computerized Processing of Uncertainty. Plenum Press, New York (1988)CrossRefzbMATHGoogle Scholar
  27. 27.
    Fuhr, N.: Models for retrieval with probabilistic indexing. Inf. Process. Manage. 25(1), 55–72 (1989)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Herrera, A., Herrera-Viedma, E.: Aggregation operators for linguistic weighted information. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 27(5), 646–656 (1997)CrossRefGoogle Scholar
  29. 29.
    Herrera-Viedma, E., Lopez-Herrera, A.G.: A model of an information retrieval system with unbalanced fuzzy linguistic information. Int. J. Intell. Syst. 22(11), 1197–1214 (2007)CrossRefzbMATHGoogle Scholar
  30. 30.
    Kohout, L.J., Keravanou, E., Bandler, W.: Information retrieval system using fuzzy relational products for thesaurus construction. In: Proceedings IFAC Fuzzy Information, pp. 7–13. Marseille, France (1983)Google Scholar
  31. 31.
    Kraft, D.H.: Advances in information retrieval: where is that /#*%@^ record? In: Yovits, M. (ed.) Advances in Computers, vol. 24, pp. 277–318. Academic Press, New York (1985)Google Scholar
  32. 32.
    Kraft, D.H., Bordogna, G., Pasi, G.: Fuzzy set techniques in information retrieval. In: Bezdek, J.C., Dubois, D., Prade, H. (eds.) The Handbooks of Fuzzy Sets Series. Fuzzy Sets in Approximate Reasoning and Information Systems, pp. 469–510. Kluwer Academic Publishers (1999)Google Scholar
  33. 33.
    Kraft, D.H., Bordogna, G., Pasi, G.: An extended fuzzy linguistic approach to generalize Boolean information retrieval. J. Inf. Sci. Appl. 2(3), 119–134 (1995)Google Scholar
  34. 34.
    Lucarella, D., Morara, R.: FIRST: fuzzy information retrieval system. J. Inf. Sci. 17(2), 81–91 (1991)CrossRefGoogle Scholar
  35. 35.
    Lucarella, D., Zanzi, A.: Information retrieval from hypertext: an approach using plausible inference. Inf. Process. Manage. 29(1), 299–312 (1993)CrossRefGoogle Scholar
  36. 36.
    Miyamoto, S.: Fuzzy Sets in Information Retrieval and Cluster Analysis. Kluwer Academic Publishers (1990)Google Scholar
  37. 37.
    Miyamoto, S.: Information retrieval based on fuzzy associations. Fuzzy Sets Syst. 38(2), 191–205 (1990)CrossRefzbMATHGoogle Scholar
  38. 38.
    Miyamoto, S.: Two approaches for information retrieval through fuzzy associations. IEEE Trans. Syst. Man Cybern. 19(1), 123–130 (1989)CrossRefzbMATHGoogle Scholar
  39. 39.
    Miyamoto, S., Nakayama, K.: Fuzzy information retrieval based on a fuzzy pseudothesaurus. IEEE Trans. Syst. Man Cybern. SMC-16(2), 278–282 (1986)Google Scholar
  40. 40.
    Molinari, A., Pasi, G.: A fuzzy representation of HTML documents for information retrieval systems. In: Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 1, pp. 107–112. New Orleans, U.S.A., 8–12 September 1996Google Scholar
  41. 41.
    Motro, A.: Imprecision and uncertainty in database systems. In: Bosc, P., Kacprzyk, J. (eds.) Fuzziness in Database Management Systems, pp. 3–22. Physica-Verlag, Heidelberg (1995)CrossRefGoogle Scholar
  42. 42.
    Murai, T., Miyakoshi, M., Shimbo, M.A.: fuzzy document retrieval method based on two-valued indexing. Fuzzy Sets Syst. 30(2), 103–120 (1989)CrossRefzbMATHMathSciNetGoogle Scholar
  43. 43.
    Neuwirth, E., Reisinger, L.: Dissimilarity and distance coefficients in automation-supported thesauri. Inf. Syst. 7(1), 47–52 (1982)CrossRefzbMATHGoogle Scholar
  44. 44.
    Nomoto, K., Wakayama, S., Kirimoto, T., Kondo, M.: A fuzzy retrieval system based on citation. Syst. Control 31(10), 748–755 (1987)Google Scholar
  45. 45.
    Paice, C.D.: Soft evaluation of Boolean search queries in information retrieval systems. Inf. Technol. Res. Dev. Appl. 3(1), 33–41 (1984)Google Scholar
  46. 46.
    Radecki, T.: Fuzzy set theoretical approach to document retrieval. Inf. Process. Manage. 15(5), 247–260 (1979)CrossRefzbMATHGoogle Scholar
  47. 47.
    Radecki, T.: Mathematical model of information retrieval system based on the concept of fuzzy thesaurus. Inf. Process. Manage. 12(5), 313–318 (1976)CrossRefzbMATHGoogle Scholar
  48. 48.
    Reisinger, L.: On fuzzy thesauri. In: Bruckman, G., et al. (eds.) COMPSTAT 1974, pp. 119–127. Physica Verlag, Vienna (1974)Google Scholar
  49. 49.
    Salton, G.: Automatic Text Processing: The Transformation, Analysis And Retrieval of Information by Computer. Addison Wesley (1989)Google Scholar
  50. 50.
    Salton, G., Allan, J., Buckley, C., Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable texts. Science 264, 1421–1426 (1994)CrossRefGoogle Scholar
  51. 51.
    Salton, G., Bergmark, D.: A citation study of computer science literature. IEEE Trans. Prof. Commun. 22(3), 146–158 (1979)CrossRefGoogle Scholar
  52. 52.
    Salton, G., Buckley, C.: Term weighting approaches in automatic text retrieval. Inf. Process. Manage. 24(5), 513–523 (1988)CrossRefGoogle Scholar
  53. 53.
    Salton, G., McGill, M.J.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)zbMATHGoogle Scholar
  54. 54.
    Sanchez, E.: Importance in Knowledge Systems. Inf. Syst. 14(6), 455–464 (1989)CrossRefGoogle Scholar
  55. 55.
    Sanchez, S.N., Triantaphyllou, E., Kraft, D.H.: A feature mining based approach for the classification of text documents into disjoint classes. Inf. Process. Manage. 38(4), 583–604 (2002)CrossRefzbMATHGoogle Scholar
  56. 56.
    Sparck Jones, K.A.: Automatic Keyword Classification for Information Retrieval. Butterworths, London (1971)Google Scholar
  57. 57.
    Sparck Jones, K.A.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 28(1), 11–20 (1972)CrossRefGoogle Scholar
  58. 58.
    Tahani, V.: A fuzzy model of document retrieval systems. Inf. Process. Manage. 12(3), 177–187 (1976)CrossRefzbMATHGoogle Scholar
  59. 59.
    van Rijsbergen, C.J.: Information Retrieval. Butterworths & Co. Ltd, London (1979)Google Scholar
  60. 60.
    Waller, W.G., Kraft, D.H.: A mathematical model of a weighted Boolean retrieval system. Inf. Process. Manage. 15, 235–245 (1979)CrossRefzbMATHGoogle Scholar
  61. 61.
    Yager, R.R.: On ordered weighted averaging aggregation operators in multi criteria decision making. IEEE Trans. Syst. Man Cybern. 18(1), 183–190 (1988)CrossRefzbMATHMathSciNetGoogle Scholar
  62. 62.
    Yager, R.R.: A note on weighted queries in information retrieval systems. J. Am. Soc. Inf. Sci. 38(1), 23–24 (1987)CrossRefGoogle Scholar
  63. 63.
    Yager, R.R., Rybalov, A.: On the fusion of documents from multiple collections information retrieval systems. J. Am. Soc. Inf. Sci. (1999)Google Scholar
  64. 64.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)CrossRefzbMATHMathSciNetGoogle Scholar
  65. 65.
    Zadeh, L.A.: Fuzzy Sets. Information and control, vol. 8, pp. 338-353 (1965)Google Scholar
  66. 66.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning, parts I, II. Inf. Sci. 8, 199–249, 301–357 (1975)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Donald H. Kraft
    • 1
  • Erin Colvin
    • 1
  • Gloria Bordogna
    • 2
  • Gabriella Pasi
    • 3
  1. 1.Colorado Technical UniversityColorado SpringsUSA
  2. 2.Istituto per il Rilevamento Elettromagnetico dell’AmbienteCNRMilano (MI)Italy
  3. 3.Disco Università degli Studi di Milano BicoccaMilanoItaly

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