Skip to main content
Springer Nature Link
Log in

Helping Users in Web Information Retrieval Via Fuzzy Association Rules

  • Chapter
Soft Computing in Web Information Retrieval

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 197))

  • 366 Accesses

Summary

We present an application of fuzzy association rules to find new terms that help the user to search in the web. Once the user has made an initial query, a set of documents is retrieved from the web. Representing these documents as text transactions, each item in the transaction means the presence of the term in the document. From the set of transactions, fuzzy association rules are extracted. Based on the thresholds of support and certainty factor, a selection of rules is carried out and the terms in those rules are offered to the user to be added to the query and to improve the retrieval.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Imielinski, T. & Swami, A. “Mining Association Rules between Set of Items in Large Databases”. In Proc. of the 1993 ACM SIGMOD Conference, 207–216, 1993.

    Google Scholar 

  2. Attar, R. & Fraenkel, A.S. “Local Feedback in Full-Text Retrieval Systems”. Journal of the Association for Computing Machinery 24(3):397–417, 1977.

    MATH  Google Scholar 

  3. Au, W.H. & Chan, K.C.C. “An effective algorithm for discovering fuzzy rules in relational databases”. In Proc. Of IEEE International Conference on Fuzzy Systems, vol II, 1314–1319, 1998.

    Google Scholar 

  4. Bodner, R.C. & Song, F. “Knowledge-based approaches to query expansion in Information Retrieval”. In McCalla, G. (Ed.) Advances in Artificial Intelligence: 146–158. New-York, USA: Springer Verlag, 1996.

    Google Scholar 

  5. Bordogna, G., Carrara, P. & Pasi, G. “Fuzzy Approaches to Extend Boolean Information Retrieval”. In Bosc., Kacprzyk, J. Fuzziness in Database Management Systems, 231–274. Germany: Physica Verlag, 1995.

    Google Scholar 

  6. Bordogna, G. & 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):70–82, 1993.

    Article  Google Scholar 

  7. Buckley, C., Salton. G., Allan, J. & Singhal, A. “Automatic Query Expansion using SMART: TREC 3”. Proc. of the 3rd Text Retrieval Conference, Gaithersburg, Maryland, 1994.

    Google Scholar 

  8. Chen, H., Ng, T., Martinez, J. & Schatz, B.R. “A Concept Space Approach to Addressing the Vocabulary Problem in Scientific Information Retrieval: An Experiment on the Worm Community System”. Journal of the American Society for Information Science 48(1):17–31, 1997.

    Article  Google Scholar 

  9. Croft, W.B. & Thompson, R.H. “I3R: A new approach to the design of Document Retrieval Systems”. Journal of the American Society for Information Science 38(6), 389–404, 1987.

    Article  Google Scholar 

  10. Delgado, M., Marín, N., Sánchez, D. & Vila, M.A. “Fuzzy Association Rules: General Model and Applications”. IEEE Transactions on Fuzzy Systems 11:214–225, 2003a.

    Article  Google Scholar 

  11. Delgado, M., Marín, N., Martín-Bautista, M.J., Sánchez, D. & Vila, M.A. “Mining Fuzzy Association Rules: An Overview”. 2003 BISC International Workshop on Soft Computing for Internet and Bioinformatics”, 2003b.

    Google Scholar 

  12. Delgado, M., Martín-Bautista, M.J., Sánchez, D. & Vila, M.A. “Mining Text Data: Special Features and Patterns”. In Proc. of EPS Exploratory Workshop on Pattern Detection and Discovery in Data Mining, London, September 2002a.

    Google Scholar 

  13. Delgado, M., Sánchez, D. & Vila, M.A. “Fuzzy cardinality based evaluation of quantified sentences”. International Journal of Approximate Reasoning 23:23–66, 2000c.

    Article  MATH  MathSciNet  Google Scholar 

  14. Efthimiadis, E. “Query Expansion”. Annual Review of Information Systems and Technology 31:121–187, 1996.

    Google Scholar 

  15. Feldman, R., Fresko, M., Kinar, Y., Lindell, Y., Liphstat, O., Rajman, M., Schler, Y. & Zamir, O. “Text Mining at the Term Level”. In Proc. of the 2nd European Symposium of Principles of Data Mining and Knowledge Discovery, 65–73, 1998.

    Google Scholar 

  16. Freyne J, Smyth B. (2005) Communities, collaboration and cooperation in personalized web search. In Proc. of the 3rd Workshop on Intelligent Techniques for Web Personalization (ITWP’05). Edinburgh, Scotland, UK

    Google Scholar 

  17. Fu, L.M. & Shortliffe, E.H. “The application of certainty factors to neural computing for rule discovery”. IEEE Transactions on Neural Networks 11(3):647–657, 2000.

    Article  Google Scholar 

  18. Gauch, S. & Smith, J.B. “An Expert System for Automatic Query Reformulation”. Journal of the American Society for Information Science 44(3):124–136, 1993.

    Article  Google Scholar 

  19. Hong, T.P., Kuo, C.S. & Chi, S.C. “Mining association rules from quantitative data.” Intelligent Data Analysis 3:363–376, 1999.

    Article  MATH  Google Scholar 

  20. Jiang, M.M., Tseng, S.S. & Tsai, C.J. “Intelligent query agent for structural document databases.” Expert Systems with Applications 17:105–133, 1999.

    Article  Google Scholar 

  21. Kanawati R., Jaczynski M., Trousse B., Andreoli J.M. (1999) Applying the Broadway recommendation computation approach for implementing a query refinement service in the CBKB meta search engine. In Proc. of the French Conference of CBR (RaPC99), Palaiseau, France

    Google Scholar 

  22. Kraft, D.H., Martín-Bautista, M.J., Chen, J. & Sánchez, D. “Rules and fuzzy rules in text: concept, extraction and usage”. International Journal of Approximate Reasoning 34, 145–161, 2003.

    Article  MATH  Google Scholar 

  23. Korfhage R.R. (1997) Information Storage and Retrieval. John Wiley & Sons, New York

    Google Scholar 

  24. Kuok, C.-M., Fu, A. & Wong, M.H. “Mining fuzzy association rules in databases,” SIGMOD Record 27(1):41–46, 1998.

    Article  Google Scholar 

  25. Lee, J.H. & Kwang, H.L. “An extension of association rules using fuzzy sets”. In Proc. of IFSA’97, Prague, Czech Republic, 1997.

    Google Scholar 

  26. Lin, S.H., Shih, C.S., Chen, M.C., Ho, J.M., Ko, M.T., Huang, Y.M. “Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A Semantic Approach”. In Proc. of ACM/SIGIR’98, 241–249. Melbourne, Australia, 1998.

    Google Scholar 

  27. Miller, G. “WordNet: An on-line lexical database”. International Journal of Lexicography 3(4):235–312, 1990.

    Google Scholar 

  28. Mitra, M., Singhal, A. & Buckley, C. “Improving Automatic Query Expansion”. In Proc. Of ACM SIGIR, 206–214. Melbourne, Australia, 1998.

    Google Scholar 

  29. Moliniari, A. & Pasi, G. “A fuzzy representation of HTML documents for information retrieval system.” Proceedings of the fifth IEEE International Conference on Fuzzy Systems, vol. I, pp. 107–112. New Orleans, EEUU, 1996.

    Google Scholar 

  30. Peat, H.P. & Willet, P. “The limitations of term co-occurrence data for query expansion in document retrieval systems”. Journal of the American Society for Information Science 42(5), 378–383, 1991.

    Article  Google Scholar 

  31. Qui, Y. & Frei, H.P. “Concept Based Query Expansion”. In Proc. Of the Sixteenth Annual International ACM-SIGIR’93 Conference on Research and Development in Information Retrieval, 160–169, 1993.

    Google Scholar 

  32. Rajman, M. & Besançon, R. “Text Mining: Natural Language Techniques and Text Mining Applications”. In Proc. of the 3rd International Conference on Database Semantics (DS-7). Chapam & Hall IFIP Proceedings serie, 1997.

    Google Scholar 

  33. Salton, G. & Buckley, C. “Term weighting approaches in automatic text retrieval”. Information Processing and Management 24(5), 513–523, 1988.

    Article  Google Scholar 

  34. Salton, G. & McGill, M.J. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.

    Google Scholar 

  35. Srinivasan, P., Ruiz, M.E., Kraft, D.H. & Chen, J. “Vocabulary mining for information retrieval: rough sets and fuzzy sets”. Information Processing and Management 37:15–38, 2001.

    Article  MATH  Google Scholar 

  36. Van Rijsbergen, C.J., Harper, D.J. & Porter, M.F. “The selection of good search terms”. Information Processing and Management 17:77–91, 1981.

    Article  Google Scholar 

  37. Vélez, B., Weiss, R., Sheldon, M.A. & Gifford, D.K. “Fast and Effective Query Refinement”. In Proc. Of the 20th ACM Conference on Research and Development in Information Retrieval (SIGIR’97). Philadelphia, Pennsylvania, 1997.

    Google Scholar 

  38. Voorhees, E. “Query expansion using lexical-semantic relations. Proc. of the 17th International Conference on Research and Development in Information Retrieval (SIGIR). Dublin, Ireland, July, 1994.

    Google Scholar 

  39. Xu, J. & Croft, W.B. “Query Expansion Using Local and Global Document Analysis”. In Proc. of the Nineteenth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 4–11, 1996.

    Google Scholar 

  40. Zadeh, L.A. “A computational approach to fuzzy quantifiers in natural languages”. Computing and Mathematics with Applications 9(1):149–184, 1983.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, University of Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    M. J. Martín-Bautista, D. Sánchez & M. A. Vila

  2. Department of Computer Science, University of Jaén, Campus Las Lagunillas, 23071, Jaén, Spain

    J. M. Serrano

Authors
  1. M. J. Martín-Bautista
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Sánchez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. J. M. Serrano
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. A. Vila
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and A.I E.T.S.I. Informatica, University of Granada, C/Periodista Daniel, Saucedo Aranda s/n, Granada, Spain

    Enrique Herrera-Viedma

  2. Department of Informatics Systems and Communication (DISCo), Università degli Studi di Milano Bicocca, Via Bicocca degli Arcimboldi, 8 (Edificio U7), 20126, Milano, Itay

    Gabriella Pasi

  3. Department of Computer and Information Sciences, University of Strathclyde, Livingstone Tower, 26 Richmond Street, Glasgow, G1 1XH, Scotland, UK

    Fabio Crestani

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Martín-Bautista, M.J., Sánchez, D., Serrano, J.M., Vila, M.A. (2006). Helping Users in Web Information Retrieval Via Fuzzy Association Rules. In: Herrera-Viedma, E., Pasi, G., Crestani, F. (eds) Soft Computing in Web Information Retrieval. Studies in Fuzziness and Soft Computing, vol 197. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-31590-X_11

Download citation

  • DOI: https://doi.org/10.1007/3-540-31590-X_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-31588-9

  • Online ISBN: 978-3-540-31590-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics