Advertisement

A Multi-Objective Genetic Algorithm for Learning Linguistic Persistent Queries in Text Retrieval Environments

  • María Luque
  • Oscar Cordón
  • Enrique Herrera-Viedma
Part of the Studies in Computational Intelligence book series (SCI, volume 16)

Abstract

Persistent queries are a specific kind of queries used in information retrieval systems to represent a user’s long-term standing information need. These queries can present many different structures, being the “bag of words” that most commonly used. They can be sometimes formulated by the user, although this task is usually difficult for him and the persistent query is then automatically derived from a set of sample documents he provides.

Keywords

Pareto Front Relevance Feedback Conjunctive Normal Form Information Retrieval System Nondominated Solution 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    T. Bäck, D.B. Fogel, and Z. Michalewicz, editors. Handbook of Evolutionary Computation. IOP Publishing and Oxford University Press, 1997.Google Scholar
  2. [2]
    R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Adisson, 1999.Google Scholar
  3. [3]
    N.J. Belkin and W.B. Croft. Information Filtering and Information Retrieval: Two Sides of the same Coin? Communications of the ACM, 35(12):29–38, 1992.CrossRefGoogle Scholar
  4. [4]
    P.P. Bonissone and K.S. Decker. Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity. In L.H. Kanal and J.F. Lemer, editors, Uncertainty in Artificial Intelligence, pages 217–247. North-Holland, 1986.Google Scholar
  5. [5]
    A. Bookstein. Fuzzy Request: An Approach to Weighted Boolean Searches. Journal of the American Society for Information Science, 31:240–247, 1980.Google Scholar
  6. [6]
    G. Bordogna, P. Carrara, and G. Pasi. Fuzzy Approaches to Extend Boolean Information Retrieval. In P. Bosc and J. Kacprzyk, editors, Fuzziness in Database Management Systems, pages 231–274. Springer-Verlag, 1995.Google Scholar
  7. [7]
    G. Bordogna and G. Pasi. A Fuzzy Linguistic Approach Generalizing Boolean Information Retrieval: A Model and its Evaluation. Journal of the American Society for Information Science, 44:70–82, 1993.CrossRefGoogle Scholar
  8. [8]
    G. Bordogna and G. Pasi. Linguistic Aggregation Operators of Selection Criteria in Fuzzy Information Retrieval. International Journal of Intelligent Systems, 10:233–248, 1995.Google Scholar
  9. [9]
    G. Bordogna and G. Pasi. An Ordinal Information Retrieval Model. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(1):63–75, 2001.zbMATHCrossRefMathSciNetGoogle Scholar
  10. [10]
    D. Buell and D.H. Kraft. A Model for a Weighted Retrieval System. Journal of the American Society for Information Science, 32:211–216, 1981.Google Scholar
  11. [11]
    D. Buell and D.H. Kraft. Threshold Values and Boolean Retrieval Systems. Information Processing & Management, 17:127–136, 1981.zbMATHCrossRefGoogle Scholar
  12. [12]
    H. Chen, G. Shankaranarayanan, L. She, and A. Iyer. A Machine Learning Approach to Inductive Query by Examples: An Experiment Using Relevance Feedback, ID3, Genetic Algoritms, and Simulated Annealing. Journal of the American Society for Information Science, 49(8):693–705, 1998.CrossRefGoogle Scholar
  13. [13]
    C. A. Coello, D. A. Van Veldhuizen, and G. B. Lamant. Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, 2002.Google Scholar
  14. [14]
    O. Cordón and E. Herrera-Viedma. Editorial: Special Issue on Soft Computing Applications to Intelligent Information Retrieval on the Internet. International Journal of Approximate Reasoning, 34(2–3):89–95, 2003.CrossRefGoogle Scholar
  15. [15]
    O. Cordón, E. Herrera-Viedma, C. López-Pujalte, M. Luque, and C. Zarco. A Review of the Application of Evolutionary Computation to Information Retrieval. International Journal of Approximate Reasoning, 34:241–264, 2003.zbMATHCrossRefMathSciNetGoogle Scholar
  16. [16]
    O. Cordón, E. Herrera-Viedma, and M. Luque. Evolutionary Learning of Boolean Queries by Multiobjective Genetic Programming. In Lecture Notes in Computer Science 2439. Proc. of the PPSN-VII, pages 710–719, Granada (Spain), 2002.Google Scholar
  17. [17]
    O. Cordón, E. Herrera-Viedma, and M. Luque. Improving the Learning of Boolean Queries by means of a Multiobjective IQBE Evolutionary Algorithm. Information Processing and Management, 2005. To appear.Google Scholar
  18. [18]
    O. Cordón, E. Herrera-Viedma, M. Luque, F. Moya, and C. Zarco. Analyzing the Performance of a Multiobjective GA-P Algorithm for Learning Fuzzy Queries in a Machine Learning Enviroment. In Lecture Notes in Artificial Intelligence 2715. Proc. of the 10th IFSA World Congress, pages 611–615, Istambul (Turkey), 2003.Google Scholar
  19. [19]
    O. Cordón, F. Moya, and C. Zarco. A GA-P Algorithm to Automatically Formulate Extended Boolean Queries for a Fuzzy Information Retrieval System. Mathware & Soft Computing, 7(2–3):309–322, 2000.zbMATHGoogle Scholar
  20. [20]
    O. Cordón, F. Moya, and C. Zarco. A New Evolutionary Algorithm Combining Simulated Annealing and Genetic Programming for Relevance Feedback in Fuzzy Information Retrieval Systems. Soft Computing, 6(5):308–319, 2002.zbMATHGoogle Scholar
  21. [21]
    O. Cordón, F. Moya, and C. Zarco. Automatic Learning of Multiple Extended Boolean Queries by Multiobjective GA-P Algorithms. In V. Loia, M. Nikravesh, and L. A. Zadeh, editors, Fuzzy Logic and the Internet, pages 47–40. Springer, 2004.Google Scholar
  22. [22]
    F. Crestani and G. Pasi, editors. Soft Computing in Information Retrieval. Physica-Verlag, 2000.Google Scholar
  23. [23]
    W. Fan, M.D. Gordon, and P. Pathak. An Integrated Two-Stages Model for Intelligent Information Routing. Decision Support Systems, 2004. Submitted.Google Scholar
  24. [24]
    W. Fan, M.D. Gordon, and P. Pathak. Effective Profiling of Consumer Information Retrieval Needs: A Unified Framework and Empirical Comparision. Decision Support Systems, 2005. To appear.Google Scholar
  25. [25]
    J.L. Fernández-Villacañas and M. Shackleton. Investigation of the Importance of the Genotype-Phenotype Mapping in Information Retrieval. Future Generation Computer Systems, 19(1):55–68, 2003.zbMATHCrossRefGoogle Scholar
  26. [26]
    U. Hanani, B. Shapira, and P. Shoval. Information Filtering: Overview of Issues, Research and Systems. User Modeling and User-Adapted Interaction, 11:203–259, 2001.zbMATHCrossRefGoogle Scholar
  27. [27]
    F. Herrera and E. Herrera-Viedma. Aggregation Operators for Linguistic Weighted Information. IEEE Transactions on Systems, Man and Cybernetics; Part A: Systems, 27:646–656, 1997.CrossRefGoogle Scholar
  28. [28]
    F. Herrera, E. Herrera-Viedma, and L. Martínez. A Fusion Approach for Managing Multi-Granularity Linguistic Term Sets in Decision Making. Fuzzy Sets and Systems, 114:43–58, 2000.zbMATHCrossRefGoogle Scholar
  29. [29]
    E. Herrera-Viedma. An Information Retrieval System with Ordinal Linguistic Weighted Queries based on Two Weighting Elements. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 9(1):77–88, 2001.zbMATHCrossRefMathSciNetGoogle Scholar
  30. [30]
    E. Herrera-Viedma. Modeling 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, 2001.CrossRefGoogle Scholar
  31. [31]
    E. Herrera-Viedma, O. Cordón, M. Luque, A. G. López, and A. M. Muñoz. A Model of Fuzzy Linguistic IRS Based on Multi-Granular Linguistic Information. International Journal of Approximate Reasoning, 34:221–239, 2003.zbMATHCrossRefMathSciNetGoogle Scholar
  32. [32]
    J. Koza. Genetic Programming. On the Programming of Computers by Means of Natural Selection. The MIT Press, 1992.Google Scholar
  33. [33]
    D.H. Kraft, G. Bordogna, and G. Pasi. An Extended Fuzzy Linguistic Approach to Generalize Boolean Information Retrieval. Information Sciences, 2:119–134, 1994.zbMATHCrossRefGoogle Scholar
  34. [34]
    D.H. Kraft and D.A. Buell. Fuzzy Sets and Generalized Boolean Retrieval Systems. International Journal of Man-Machine Studies, 19:45–56, 1983.CrossRefGoogle Scholar
  35. [35]
    D.H. Kraft, F.E. Petry, B.P. Buckles, and T. Sadasivan. Genetic Algorithms for Query Optimization in Information Retrieval: Relevance Feedback. In E. Sanchez, T. Shibata, and L.A. Zadeh, editors, Genetic Algorithms and Fuzzy Logic Systems, pages 155–173. World Scientific, 1997.Google Scholar
  36. [36]
    V. I. Levenshtein. Binary Codes of Correcting Deletions, Insertions and Reversal. Sov. Phys. Dokl., 6:705–710, 1996.Google Scholar
  37. [37]
    M. Nikravesh, V. Loia, and B. Azvine. Fuzzy Logic and the Internet (FLINT): Internet, World Wide Web and Search Engines. Soft Computing, 6(4):287–299, 2002.zbMATHGoogle Scholar
  38. [38]
    D.W. Oard and G. Marchionini. A Conceptual Framework for Text Filtering. Technical Report CS-TR-3643, University of Maryland, College Park, 1996.Google Scholar
  39. [39]
    G. Pasi. Intelligent Information Retrieval: Some Research Trends. In J.M. Benítez, O. Cordón, F. Hoffmann, and R. Roy, editors, Advances in Soft Computing. Engineering Design and Manufacturing, pages 157–171. Springer, 2003.Google Scholar
  40. [40]
    M.P. Smith and M. Smith. The Use of Genetic Programming to Build Boolean Queries for Text Retrieval through Relevance Feedback. Journal of Information Science, 23(6):423–431, 1997.CrossRefGoogle Scholar
  41. [41]
    P. Thrift. Fuzzy Logic Synthesis with Genetic Algorithms. In Proceedings of the Fourth International Conference on Genetic Algorithms, pages 509–513, 1991.Google Scholar
  42. [42]
    W.G. Waller and D.H. Kraft. A Mathematical Model of a Weighted Boolean Retrieval System. Information Processing & Management, 15:235–245, 1979.zbMATHCrossRefGoogle Scholar
  43. [43]
    R.R Yager. A Note on Weighted Queries in Information Retrieval Systems. Journal of the American Society for Information Science, 38:23–24, 1987.CrossRefGoogle Scholar
  44. [44]
    R.R. Yager. On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decision Making. IEEE Transactions on Systems, Man, and Cybernetics, 18:183–190, 1988.zbMATHCrossRefMathSciNetGoogle Scholar
  45. [45]
    L.A. Zadeh. The Concept of a Linguistic Variable and its Applications to Approximate Reasoning. Part I, II & III, Information Science, 8:199–249, 8:301–157, 9:43–80, 1975.CrossRefMathSciNetGoogle Scholar
  46. [46]
    E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Empirical Results. Evolutionary Computation, 8(2):173–195, 2000.CrossRefGoogle Scholar
  47. [47]
    E. Zitzler and L. Thiele. Multiobjective Evolutionary Algorithms: A comparative Case Study and the Strength Pareto Approach. IEEE Transactions on Evolutionary Computation, 3(4):257–271, 1999.CrossRefGoogle Scholar

Copyright information

© Springer 2006

Authors and Affiliations

  • María Luque
    • 1
  • Oscar Cordón
    • 2
  • Enrique Herrera-Viedma
    • 2
  1. 1.Dept. of Computer ScienceN.A. University of CórdobaCórdobaSpain
  2. 2.Dept. of Computer Science and A.I. E.T.S. de Ingeniería InformáticaUniversity of Granada.GranadaSpain

Personalised recommendations