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A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion


In this paper, a novel fuzzy logic-based expansion approach considering the relevance score produced by different rank aggregation approaches is proposed. It is well known that different rank aggregation approaches yield different relevance scores for each term. The proposed fuzzy logic approach combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Experimental results demonstrate that the proposed approach achieves significant improvement over individual expansion, aggregated and other related state-of-the-arts methods.

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  1. Berardi, M., Lapi, M., Leo, P., Malerba,D., Marinelli, C., Scioscia, G.: A data mining approach to pubmed query refinement. In: Proceedings of the 15th International Workshop on Database and Expert Systems Applications, Zaragoza, Spain, pp. 401–405 (2004)

  2. Chen, H., Yu, J.X., Furuse, K., Ohbo, N.: Support IR query refinement by partial keyword set. In: Proceedings of the Second International Conference on Web Information Systems Engineering, Singapore, vol. 1, pp. 245–253 (2001)

  3. Chang, Y.C., Chen, S.M., Liau, C.J.: A new query expansion method based on fuzzy rules. In: Proceedings of the Seventh Joint Conference on AI, Fuzzy System, and Grey System, Taipei, Taiwan, Republic of China (2003)

  4. Cui, H., Wen, J.R., Nie, J.Y., Ma, W.Y.: Probabilistic query expansion using query logs. In: Proceedings of the 11th International Conference on World Wide Web, Honolulu, Hawaii, pp. 325–332 (2002)

  5. Jin, Q., Zhao, J., Xu, B.: Query expansion based on term similarity tree model. In: Proceedings of the 2003 International Conference on Natural Language Processing and Knowledge Engineering, Beijing, China, pp. 400–406 (2003)

  6. Kim, B.M., Kim, J.Y., Kim, J.: Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference. Proceedings of the Joint Ninth IFSA World Congress and 20th NAFIPS International Conference, Vancouver, Canada, vol. 2, pp. 715–720 (2001)

  7. Latiri, C.C., Elloumi, S., Chevallet, J.P., Jaoua, A.: Extension of fuzzy Galois connection for information retrieval using a fuzzy quantifier. In: Proceedings of the 2003 ACS/IEEE International Conference on Computer Systems and Applications, Tunis, Tunisia (2003)

  8. Lin, H.C., Wang, L.H., Chen, S.M.: A new query expansion method for document retrieval by mining additional query terms. In: Proceedings of the 2005 International Conference on Business and Information, Hong Kong, China (2005)

  9. Wu, M.S.: Modeling query-document dependencies with topic language models for information retrieval. Inf. Sci. 312, 1–12 (2015)

    Article  Google Scholar 

  10. Pedronette, D.C.G., Almeida, J., Torres, R.D.: A scalable re-ranking method for content-based image retrieval. Inf. Sci. 265, 91–104 (2014)

    MathSciNet  Article  MATH  Google Scholar 

  11. Singh, J., Sharan, A.: Context window based co-occurrence approach for improving feedback based query expansion in information retrieval. Int. J. Inf. Retr. Res. 5(4), 31–45 (2015)

    MathSciNet  Google Scholar 

  12. Singh, J., Sharan, A.: Co-occurrence and semantic similarity based hybrid approach for improving automatic query expansion in information retrieval. LNCS, vol. 8956, pp. 415–418. Springer, Berlin (2015)

  13. Prasad, M., Li, D.L., Lin, C.T., Prakash, S., Singh, J., Joshi, S.: Designing mamdani-type fuzzy reasoning for visualizing prediction problems based on collaborative fuzzy clustering. IAENG Int. J. Comput. Sci. 42(4), 404–411 (2015)

    Google Scholar 

  14. Gupta, Y., Saini, A., Saxena, A.K.: A new fuzzy logic based ranking function for efficient information retrieval system. Expert Syst. Appl. 42(3), 1223–1234 (2015)

    Article  Google Scholar 

  15. Chen, S.M., Randyanto, Y., Cheng, S.H.: Fuzzy queries processing based on intuitionistic fuzzy social relational networks. Inf. Sci. 327, 110–124 (2016)

    MathSciNet  Article  Google Scholar 

  16. Bade, Y., Bhat, R., Borate, P.: Optimization techniques for improving the performance of the information retrieval system. Int. J. Res. Advent Technol. 2(2), 263–267 (2014)

    Google Scholar 

  17. Thompson, K.C.: Reducing the risk of query expansion via robust constrained optimization. In: Proceeding of the 18th ACM Conference on Information and Knowledge Management, CIKM’09, New York, NY, USA, pp. 837–846 (2009)

  18. Raman, K., Udupa, R., Bhattacharyya, P., Bhole, A.: On improving pseudo-relevance feedback using pseudo-irrelevant documents. In: ECIR, pp. 573–576 (2010)

  19. White, R.W., Marchionini, G.: Examining the effectiveness of real-time query expansion. In: Information Processing and Management, pp. 43, no. 3, pp. 685–704 (2007)

  20. Ye, Z., Huang, J.X., Lin, H.: Finding a good query-related topic for boosting pseudo-relevance feedback. J. Am. Soc. Inf. Sci. Technol. 62(4), 748–760 (2011)

    Article  Google Scholar 

  21. Church, K.W., Hanks, P.: Word association norms, mutual information, and lexicography. Comput. Linguist. 16(1), 22–29 (1990)

    Google Scholar 

  22. Rijsbergen, C.J.V.: A theoretical basis for the use of co-occurrence data in information retrieval. J. Doc. 33(2), 106–119 (1977)

    Article  Google Scholar 

  23. Li, Y., Luo, C., Chung, S.M.: Text clustering with feature selection by using statistical data. IEEE Trans. Knowl. Data Eng. 20(5), 641–652 (2008)

    Article  Google Scholar 

  24. Aguera, J.R.P., Araujo, L.: Comparing and combining methods for automatic query expansion. Adv. Nat. Lang. Process. Appl. Res. Comput. Sci. 33, 177–188 (2008)

    Google Scholar 

  25. Robertson, S.E.: On term selection for query expansion. J. Doc. 46(4), 359–364 (1990)

    Article  Google Scholar 

  26. Rogati, M., Yang, Y.: High-performing feature selection for text classification. In: Proceedings of the 11th ACM International Conference on Information and Knowledge Management, pp. 659–661 (2002)

  27. Fox, E.A., Shaw, J.A.: Combination of multiple searches. In Proceedings of the 2nd Text Retrieval Conference, pp. 243–252 (1994)

  28. Wei, Z., Gao, W., Ganainy, T.E., Magdy, W., Wong, K.F.: Ranking model selection and fusion for effective micro blog search. In: proceedings of the 1st International Workshop on Social Media Retrieval and Analysis, pp. 21–26 (2014)

  29. Miao, J., Huang, X., Ye, Z.: Proximity-based rocchio’s model for pseudo relevance feedback. In Proceedings of 35th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 534–544 (2012)

  30. Mamdani, E.H., Assilian, S.: An experiment in linguistic synthesis with a fuzzy logic controller. Int. J. Man Mach. Stud. 7, 1–13 (1975)

    Article  MATH  Google Scholar 

  31. Lee, C.: Fuzzy logic in control systems: fuzzy logic controller, Parts I and II. IEEE Trans. Syst. Man Cybern. 20, 404–435 (1990)

    Article  MATH  Google Scholar 

  32. The MathWorks Inc.: MATLAB the Language of Technical Computing: Function Reference, vol. 1: A-E version 7. The MathWorks, Inc., Natick (2004)

  33. Diaz, F., Metzler, D.: Improving the estimation of relevance models using large external corpora. In Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 154–161 (2006)

  34. Robertson, S.E., Walker, S., Jones, S., Beaulieu, M.M.H., Gatford, M.: Okapi at TREC-3. In: Proceedings of the Third Text REtrieval Conference, pp. 109–126 (1995)

  35. Singh, J., Sharan, A.: Relevance feedback based query expansion model using borda count and semantic similarity approach. In: Computational Intelligence and Neuroscience, pp. 1–17 (2015)

  36. Tomiye, A.C., Samuel, A.B., Ijesunor, A.B., Udo, I.: A fuzzy-ontology based information retrieval system for relevance feedback. Int. J. Comput. Sci. Issues 18(1), 382–389 (2011)

    Google Scholar 

  37. Parapar, J., Quindimil, M.A.P., Barreiro, A.: Score distributions for pseudo relevance feedback. Inf. Sci. 273, 171–181 (2014)

    Article  Google Scholar 

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The authors would like to acknowledge the funding support from the ministry of education, Singapore (tier 1 acrf, rg29/15).

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Correspondence to Mukesh Prasad.

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Singh, J., Prasad, M., Prasad, O.K. et al. A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query Expansion. Int. J. Fuzzy Syst. 18, 980–989 (2016).

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  • Fuzzy logic
  • Rank aggregation
  • Query expansion
  • Pseudo relevance feedback
  • Information retrival