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A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach

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Abstract

Efficient query expansion (QE) terms selection methods are really very important for improving the accuracy and efficiency of the system by removing the irrelevant and redundant terms from the top-retrieved feedback documents corpus with respect to a user query. Each individual QE term selection method has its weaknesses and strengths. To overcome the weaknesses and to utilize the strengths of the individual method, we used multiple terms selection methods together. In this paper, we present a new method for QE based on fuzzy logic considering the top-retrieved document as relevance feedback documents for mining additional QE terms. Different QE terms selection methods calculate the degrees of importance of all unique terms of top-retrieved documents collection for mining additional expansion terms. These methods give different relevance scores for each term. The proposed method combines different weights of each term by using fuzzy rules to infer the weights of the additional query terms. Then, the weights of the additional query terms and the weights of the original query terms are used to form the new query vector, and we use this new query vector to retrieve documents. All the experiments are performed on TREC and FIRE benchmark datasets. The proposed QE method increases the precision rates and the recall rates of information retrieval systems for dealing with document retrieval. It gets a significant higher average recall rate, average precision rate and F measure on both datasets.

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References

  1. Aguera JRP, Araujo L (2008) Comparing and combining methods for automatic query expansion. In: Advances in natural language processing and applications, research in computing science, vol 33, pp 177–188

  2. Bache R, Ballie M, Crestani F (2013) The likelihood property in general retrieval operations original research article. Inf Sci 234:97–111

    Article  Google Scholar 

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

    Google Scholar 

  4. Berardi M, Lapi M, Leo P, Malerba D, Marinelli C, Scioscia G (2004) 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

  5. Carpineto C, Romano G (2012) A survey of automatic query expansion in information retrieval. ACM Comput Surv 44(1):1–50

    Article  MATH  Google Scholar 

  6. Chang YC, Chen SM, Liau CJ (2003) 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

  7. Chen H, Yu JX, Furuse K, Ohbo N (2001) Support IR query refinement by partial keyword set. Proceedings of the second international conference on web information systems engineering, Singapore, vol 1, pp 245–253

  8. Choi J, Kim M, Raghavan VV (2006) Adaptive relevance feedback method of extended boolean model using hierarchical clustering techniques. Inform Process Manag 42:331–349

    Article  MATH  Google Scholar 

  9. Christopher DM, Raghavan P, Schutze H (2009) An introduction to information retrieval. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  10. Cui H, Wen JR, Nie JY, Ma WY (2002) Probabilistic query expansion using query logs. In: Proceedings of the 11th international conference on World Wide Web, Honolulu, Hawaii, pp 325–332

  11. Diaz F, Metzler D (2006) 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

  12. Furnas G et al (1987) The vocabulary problem in human-system communication. Commun ACM 30(11):964–971

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. He B, Huang JX, Zhou X (2011) Modeling term proximity for probabilistic information retrieval models. Inf Sci 181(14):3017–3031

    Article  MathSciNet  Google Scholar 

  15. Jang JSR, Sun CT (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Upper Saddle River

    Google Scholar 

  16. Jean M, Yves C, Philippe M (2011) A relational vector space model using an advanced weighting scheme for image retrieval. Inform Process Manag 47(3):391–414

    Article  Google Scholar 

  17. Jin Q, Zhao J, Xu B (2003) 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

  18. Khan YD, Ahmad F, Khan SA (2014) Content-based image retrieval using extroverted semantics: a probabilistic approach. Neural Comput Appl 24(7):1735–1748

    Article  Google Scholar 

  19. Kim BM, Kim JY, Kim J (2001) Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference. In: Proceedings of the joint ninth IFSA world congress and 20th NAFIPS international conference, Vancouver, Canada, vol 2, pp 715–720

  20. Latiri CC, Elloumi S, Chevallet JP, Jaoua A (2003a) 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

  21. Latiri CC, Yahia SB, Chevallet JP, Jaoua A (2003b) Query expansion using fuzzy association rules between terms. In: Proceedings of the 2003 fourth JIM international conference on knowledge discovery and discrete mathematics, Mets, France

  22. Leacock C, Chodorow M (1998) Combining local context and wordnet similarity for word sense identification in WordNet. An Electronic Lexical Database. MIT Press, Cambridge, pp 265–283

    Google Scholar 

  23. Lee C (1990) Fuzzy logic in control systems: fuzzy logic controller, Parts I and II. IEEE Transaction on System, Man and Cybernetics 20:404–435

    Article  MATH  Google Scholar 

  24. Lee KS, Croft WB (2013) A deterministic re-sampling method using overlapping document clusters for pseudo-relevance feedback. Inf Process Manage 49(4):792–806

    Article  Google Scholar 

  25. Lee HM, Lin SK, Huang CW (2001) Interactive query expansion based on fuzzy association thesaurus for Web information retrieval. Fuzzy Systems 2:724–727

    Google Scholar 

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

    Article  Google Scholar 

  27. Lin HC, Wang LH, Chen SM (2005) An 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

  28. Liu S, Liu F, Yu C, Meng W (2004) An effective approach to document retrieval via utilizing wordnet and recognizing phrases. In: Proceedings of the ACM SIGIR Conference on Research and development in Information Retrieval, pp 266–272

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

    Article  MATH  Google Scholar 

  30. Miao J, Huang X, Ye Z (2012). 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

  31. Murata M, Toda H, Matsuura Y, Kataoka R (2009) Access concentration detection in click logs to improve mobile Web-IR. Inf Sci 179(12):1859–1869

    Article  Google Scholar 

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

    Article  Google Scholar 

  33. Pedronette DCG, Almeida J, Torres RD (2014) A scalable re-ranking method for content-based image retrieval. Inf Sci 265:91–104

    Article  MathSciNet  MATH  Google Scholar 

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

  35. Rijsbergen CJV (1977) A theoretical basis for the use of co-occurrence data in information retrieval. Journal of Documentation 33(2):106–119

    Article  Google Scholar 

  36. Robertson SE (1990) On term selection for query expansion. Journal of documentation 46(4):359–364

    Article  Google Scholar 

  37. Robertson SE, Walker S, Jones S, Beaulieu MMH, Gatford M (1995). Okapi at TREC-3. In: Proceedings of the third text REtrieval conference, pp 109–126

  38. Shah RR, Shaikh AD, Yu Y, Geng W, Zimmermann R (2014) ADVISOR—Personalized video soundtrack recommendation by late fusion with heuristic rankings. In: Proceedings of the 22rd ACM international conference on Multimedia, Orlando, Florida, USA, pp 607–616

  39. Shah RR, Shaikh AD, Yu Y, Geng W, Zimmermann R, Wu G (2015) EventBuilder: Real-time multimedia event summarization by visualizing social media. In: Proceedings of the 23rd ACM international conference on Multimedia, Brisbane, Australia, pp 185–188

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

    Google Scholar 

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

  42. Singh J, Sharan A (2015c) Relevance feedback based query expansion model using Borda count and semantic similarity approach. Comput Intel Neurosci 2015:1–13, Article ID 568197

  43. Subtil P, Mouaddib N, Faucout O (1996) A fuzzy information retrieval and management system and its applications. In Proceedings of the ACM symposium on applied computing. USA

  44. Swets JA (1963) Information retrieval systems. Science 141(3577):245–250

    Article  Google Scholar 

  45. The MathWorks, Inc (2004) MATLAB the language of technical computing: function reference, vol 1: A-E version 7. The MathWorks, Inc., Natick

  46. Thompson KC (2009) Reducing the risk of query expansion via robust constrained optimization. In CIKM’09: Proceeding of the 18th ACM conference on Information and knowledge management, CIKM’09, pp 837–846, New York, NY, USA

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

    Google Scholar 

  48. Valdivia MTM, Galiano MCD, Raez AM, López LAU (2008) Using information gain to improve multi-modal information retrieval systems. Inf Process Manage 44(3):1146–1158

    Article  Google Scholar 

  49. Verelas VE, Raftopoulou P (2005) Semantic similarity methods in wordnet and their application to ir on the web. In: Web information and data management, pp 10–16

  50. Wang HJ, Chang CY (2012) Semantic real-world image classification for image retrieval with fuzzy-ART neural network. Neural Comput Appl 21(8):2137–2151

    Article  Google Scholar 

  51. White RW, Marchionini G (2007) Examining the effectiveness of real-time query expansion. Inf Process Manage 43(3):685–704

    Article  Google Scholar 

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

    Article  Google Scholar 

  53. Xu Y, Benaroch M (2005) Information retrieval with a hybrid automatic query expansion and data fusion procedure. Inf Retrieval 8(1):41–65

    Article  Google Scholar 

  54. Yates RB, Berthier R (1999) Modern information retrieval. Addisson Wesley, Boston

    Google Scholar 

  55. Ye Z, He B, Huang X, Lin H (2010). Revisiting rocchio’s relevance feedback algorithm for probabilistic models. In: Asia information retrieval society conference, pp. 151–161

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

    Article  Google Scholar 

  57. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

  58. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MathSciNet  MATH  Google Scholar 

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  1. Jawaharlal Nehru University, New Delhi, India

    Jagendra Singh & Aditi Sharan

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  1. Jagendra Singh
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  2. Aditi Sharan
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Correspondence to Jagendra Singh.

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Singh, J., Sharan, A. A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach. Neural Comput & Applic 28, 2557–2580 (2017). https://doi.org/10.1007/s00521-016-2207-x

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