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A contemporary combined approach for query expansion

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Abstract

The use of an automatic query expansion technique is to enhance the performance of the Information Retrieval System. Selecting the candidate terms for query expansion is an essential task to make query more precise to extract the most suitable documents. This paper provides a method to select the best terms for query enhancement. Firstly, the effect of abbreviation resolution, Lexical Variation, Synonyms, n-gram pseudo-relevance feedback, Co-occurrence method on baseline approaches of query expansion is analyzed.. In this work, we used the Okapi BM25 algorithm for ranking. We used Concept-based normalization to deal with concept terms. Here our results show the improvement in results than the baseline approach. A new combined technique that integrates lexical variation, synonyms, n-gram pseudo relevance feedback for query enhancement is proposed. For experimental purpose three English written datasets CACM, CISI, and TREC-3 is used. The obtained results show improvement in the performance of query expansion concerning mean average precision, F-measure, and precision-recall curve.

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References

  1. Anand R, Kotov A (2015) An empirical comparison of statistical term association graphs with dbpedia and conceptnet for query expansion. In: Proceedings of the 7th forum for information retrieval evaluation, pp 27–30

  2. Azad HK, Deepak A (2019) Query expansion techniques for information retrieval: a survey. Inf Process Manag 56(5):1698–1735

    Article  Google Scholar 

  3. Azad HK, Deepak A (2019) A new approach for query expansion using Wikipedia and WordNet. Inf Sci 492:147–163

    Article  Google Scholar 

  4. Azad HK, Deepak A (2019) A novel model for query expansion using pseudo-relevant web knowledge. arXiv preprint arXiv:1908.10193

  5. Bendersky M, Metzler D, Croft WB (2012) Effective query formulation with multiple information sources. In: Proceedings of the fifth ACM international conference on web search and data mining, pp 443–452

  6. Bhogal J, MacFarlane A, Smith P (2007) A review of ontology based query expansion. Inf Process Manag 43(4):866–886

    Article  Google Scholar 

  7. Bouchoucha A, He J, Nie JY (2013) Diversified query expansion using conceptnet. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management, pp 1861–1864

  8. Bounhas I, Soudani N, Slimani Y (2019) Building a morpho-semantic knowledge graph for Arabic information retrieval. Inf Process Manag 102124

  9. Carpineto C, Romano G (2012) A survey of automatic query expansion in information retrieval. ACM Computing Surveys (CSUR) 44(1):1–50

    Article  MATH  Google Scholar 

  10. Carpineto C, De Mori R, Romano G, Bigi B (2001) An information-theoretic approach to automatic query expansion. ACM Transactions on Information Systems (TOIS) 19(1):1–27

    Article  Google Scholar 

  11. Chandra G, Dwivedi SK (2019) Query expansion for effective retrieval results of hindi–english cross-lingual IR. Appl Artif Intell 33(7):567–593

    Article  Google Scholar 

  12. Chang YC, Chen SM, Liau CJ (2007) A new query expansion method for document retrieval based on the inference of fuzzy rules. J Chin Inst Eng 30(3):511–515

    Article  Google Scholar 

  13. Chaudhary C, Goyal P, Goyal N, Chen YPP (2020) Image retrieval for complex queries using knowledge embedding. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16(1):1–23

    Article  Google Scholar 

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

  15. Cooper JW, Byrd RJ (1998) OBIWAN-A visual interface for prompted query refinement. In: Proceedings of the thirty-first Hawaii international conference on system sciences, vol 2. IEEE, pp 277–285

  16. Dahab MY, Alnofaie S, Kamel M (2018) A tutorial on information retrieval using query expansion. In: Intelligent natural language processing: trends and applications. Springer, Cham, pp 761–776

  17. Dalton J, Naseri S, Dietz L, Allan J (2019) Local and global query expansion for hierarchical complex topics. In: European conference on information retrieval. Springer, Cham, pp 290–303

  18. Di Marco A, Navigli R (2013) Clustering and diversifying web search results with graph-based word sense induction. Computational Linguistics 39(3):709–754

    Article  Google Scholar 

  19. Esposito M, Damiano E, Minutolo A, De Pietro G, Fujita H (2020) Hybrid query expansion using lexical resources and word embeddings for sentence retrieval in question answering. Inf Sci 514:88–105

    Article  Google Scholar 

  20. Fang F, Zhang BW, Yin XC (2018) Semantic sequential query expansion for biomedical article search. IEEE Access 6:45448–45457

    Article  Google Scholar 

  21. Fattahi R, Wilson CS, Cole F (2008) An alternative approach to natural language query expansion in search engines: text analysis of non-topical terms in web documents. Inf Process Manag 44(4):1503–1516

    Article  Google Scholar 

  22. Gong Z, Cheang CW (2006) Multi-term web query expansion using WordNet. In: International conference on database and expert systems applications. Springer, Berlin, pp 379–388

  23. Gupta Y, Saini A (2017) A novel fuzzy-PSO term weighting automatic query expansion approach using combined semantic filtering. Knowl-Based Syst 136:97–120

    Article  Google Scholar 

  24. Gupta Y, Saini A (2019) A new swarm-based efficient data clustering approach using KHM and fuzzy logic. Soft Comput 23(1):145–162

    Article  Google Scholar 

  25. Gupta Y, Saini A (2019) A novel term selection based automatic query expansion approach using PRF and semantic filtering. International Journal of Engineering and Advanced Technology 8(C):130–137

    Google Scholar 

  26. Gupta Y, Saini A, Saxena A (2013) A review on important aspects of information retrieval. International Journal of Computer, Information science and Engineering 7(12):940–948

    Google Scholar 

  27. Gupta Y, Saini A, Saxena A (2014) Fuzzy logic based approach to develop hybrid similarity measure for efficient information retrieval. J Inf Sci 40:846–857

    Article  Google Scholar 

  28. 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 

  29. Horng JT, Yeh CC (2000) Applying genetic algorithms to query optimization in document retrieval. Inf Process Manag 36(5):737–759

    Article  Google Scholar 

  30. Hsu MH, Tsai MF, Chen HH (2008) Combining WordNet and ConceptNet for automatic query expansion: a learning approach. In: Asia information retrieval symposium. Springer, Berlin, pp 213–224

  31. Htun NN, Halvey M, Baillie L (2018) Beyond traditional collaborative search: understanding the effect of awareness on multi-level collaborative information retrieval. Inf Process Manag 54(1):60–87

    Article  Google Scholar 

  32. Huang Q, Yang Y, Zhan X, Wan H, Vakis G (2018) Query expansion based on statistical learning from code changes. Software: Practice and Experience 48(7):1333–1351

    Google Scholar 

  33. Keyword (2020) Query size by country. https://www.keyworddiscovery.com/keyword-stats.html

  34. Khan L, Luo F (2002) Ontology construction for information selection. In: 14th IEEE international conference on tools with artificial intelligence, 2002. (ICTAI 2002). Proceedings. IEEE, pp 122–127

  35. Khennak I, Drias H (2017) An accelerated PSO for query expansion in web information retrieval: application to medical dataset. Appl Intell 47(3):793–808

    Article  Google Scholar 

  36. Khennak I, Drias H (2020) A novel hybrid correlation measure for query expansion-based information retrieval. In: Critical approaches to information retrieval research. IGI Global, pp 1–19

  37. Kotov A, Zhai C (2012) Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries. In: Proceedings of the fifth ACM international conference on web search and data mining, pp 403–412

  38. Krovetz R, Croft WB (1992) Lexical ambiguity and information retrieval. ACM Transactions on Information Systems (TOIS) 10(2):115–141

    Article  Google Scholar 

  39. Kumar R, Bhanodai G, Pamula R (2019) Book search using social information, user profiles and query expansion with Pseudo relevance feedback. Appl Intell 49(6):2178–2200

    Article  Google Scholar 

  40. Lafourcade M, Zarrouk M, Joubert A (2014) About inferences in a crowdsourced lexical-semantic network. In: Proceedings of the 14th conference of the European chapter of the Association for Computational Linguistics, pp 174–182

  41. Latiri C, Haddad H, Hamrouni T (2012) Towards an effective automatic query expansion process using an association rule mining approach. J Intell Inf Syst 39(1):209–247

    Article  Google Scholar 

  42. Li H, Xu J (2014) Semantic matching in the search. Foundations and Trends® in Information Retrieval 7(5):343–469

    Article  Google Scholar 

  43. Macdonald C, Ounis I (2007) Expertise drift and query expansion in expert search. In: proceedings of the sixteenth ACM conference on conference on information and knowledge management, pp 341–350

  44. Mahler D (2004) Holistic query expansion using graphical models. New Directions in Question Answering 2004:203–227

    Google Scholar 

  45. Nasir JA, Varlamis I, Ishfaq S (2019) A knowledge-based semantic framework for query expansion. Inf Process Manag 56(5):1605–1617

    Article  Google Scholar 

  46. Nowacka K, Zadrozny S, Kacprzyk J (2008) A new fuzzy logic based information retrieval model. In: 12th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2008), Malaga, Spain

  47. Pérez F, Font J, Arcega L, Cetina C (2019) Collaborative feature location in models through automatic query expansion. Autom Softw Eng 26(1):161–202

    Article  Google Scholar 

  48. Raza MA, Mokhtar R, Ahmad N (2018) A survey of statistical approaches for query expansion. Knowl Inf Syst:1–25

  49. Robertson SE, Walker S, Jones S, Hancock-Beaulieu MM, Gatford M (1995) Okapi at TREC-3. Nist Special Publication Sp, 109, 109

  50. Robertson SE, Walker S, Beaulieu M, Willett P (1999) Okapi at TREC-7: automatic ad hoc, filtering, VLC and interactive track. Nist Special Publication SP, (500), 253–264

  51. Schwartz AS, Hearst MA (2002) A simple algorithm for identifying abbreviation definitions in biomedical text. In: Biocomputing 2003, pp 451–462

  52. Sharma DK, Pamula R, Chauhan DS (2018) A comparative analysis of fuzzy logic based query expansion approaches for document retrieval. In: International conference on advances in computing and data sciences. Springer, Singapore, pp 336–345

  53. Sharma DK, Pamula R, Chauhan DS (2019) A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system. Journal of ambient intelligence and humanized computing:1–20

  54. Sharma DK, Pamula R, Chauhan DS (2019) Soft computing techniques based automatic query expansion approach for improving document retrieval. In: 2019 Amity International conference on artificial intelligence (AICAI). IEEE, pp 972–976

  55. Sharma DK, Pamula R, Chauhan DS (2019) Combined techniques based query expansion approach for document retrieval system. In: 2019 international conference on contemporary computing and informatics (IC3I). IEEE, pp 101–105

  56. Singh J, Kumar R (2017) Lexical co-occurrence and contextual window-based approach with semantic similarity for query expansion. International Journal of Intelligent Information Technologies (IJIIT) 13(3):57–78

    Article  MathSciNet  Google Scholar 

  57. Singh J, Sharan A (2017) A new fuzzy logic-based query expansion model for efficient information retrieval using relevance feedback approach. Neural Comput & Applic 28(9):2557–2580

    Article  Google Scholar 

  58. Singh J, Sharan A (2018) Rank fusion and semantic genetic notion based automatic query expansion model. Swarm and Evolutionary Computation 38:295–308

    Article  Google Scholar 

  59. Singh J, Sharan A, Saini M (2017) Term co-occurrence and context window-based combined approach for query expansion with the semantic notion of terms. International Journal of Web Science 3(1):32–57

    Article  Google Scholar 

  60. Spink A, Wolfram D, Jansen MB, Saracevic T (2001) Searching the web: the public and their queries. J Am Soc Inf Sci Technol 52(3):226–234

    Article  Google Scholar 

  61. Stokes N, Li Y, Cavedon L, Huang E, Rong J, Zobel J (2007) Entity-based relevance feedback for genomic list answer retrieval. In: TREC

  62. Stokes N, Li Y, Cavedon L, Zobel J (2009) Exploring criteria for successful query expansion in the genomic domain. Inf Retr 12(1):17–50

    Article  Google Scholar 

  63. Torjmen-Khemakhem M, Gasmi K (2019) Document/query expansion based on selecting significant concepts for context based retrieval of medical images. J Biomed Inform 95:103210

    Article  Google Scholar 

  64. Wang Z, Qiang N (2012) Research on hybrid query expansion algorithm. International Journal of Hybrid Information Technology 5(2):207–212

    Google Scholar 

  65. Wang Y, Huang H, Feng C (2019) Query expansion with local conceptual word embeddings in microblog retrieval. IEEE Trans Knowl Data Eng:1

  66. Wasim M, Asim MN, Ghani MU, Rehman ZU, Rho S, Mehmood I (2019) Lexical paraphrasing and pseudo relevance feedback for biomedical document retrieval. Multimed Tools Appl 78(21):29681–29712

    Article  Google Scholar 

  67. Wu Y, Li Y, Xu Y (2019) Dual pattern-enhanced representations model for query-focused multi-document summarization. Knowl-Based Syst 163:736–748

    Article  Google Scholar 

  68. Zhang C, Qin Z, Yan X (2005) Association-based segmentation for Chinese-crossed query expansion. IEEE Intelligent Informatics Bulletin 5(1):18–25

    Google Scholar 

  69. Zhou W, Clement TY, Torvik VI, Smalheiser NR (2006) A concept-based framework for passage retrieval at genomics. In: TREC vol 8, no 2, pp 8–2

  70. Zingla MA, Latiri C, Mulhem P, Berrut C, Slimani Y (2018) Hybrid query expansion model for text and microblog information retrieval. Information Retrieval Journal 21(4):337–367

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology(ISM), Dhanbad, India

    Dilip Kumar Sharma & Rajendra Pamula

  2. GLA University, Mathura, India

    Dilip Kumar Sharma & D. S. Chauhan

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  1. Dilip Kumar Sharma
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  2. Rajendra Pamula
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  3. D. S. Chauhan
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Sharma, D.K., Pamula, R. & Chauhan, D.S. A contemporary combined approach for query expansion. Multimed Tools Appl 81, 35195–35221 (2022). https://doi.org/10.1007/s11042-020-09172-2

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