Skip to main content

Advertisement

Log in

Semantic approaches for query expansion

  • Special Issue
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

To increase the Information Retrieval System’s efficiency, there is a requirement to expand the native user query. There are many approaches to enhance the user query in which the primary method consists of semantic-based query expansion (QE). In a semantic-based QE approach, relevant documents are retrieved by considering all the similar terms of a given user query. The semantic-based QE approach helps in dealing with the limitation of low recall and low precision value of the Information retrieval system and deals with ambiguity and vagueness. Query Expansion techniques contain the semantic concepts that are relevant to semantic computing, computational intelligence, and information retrieval area. Computational intelligence technique is required during automatic query expansion for advanced information processing. This paper presents a comprehensive survey of the methods proposed by several researchers considering semantic-based query expansion. It also discusses the merits and demerits of each technique in a detailed manner. This paper presents the view of several semantic query expansion core techniques. This paper also discusses the various keyholes present in today’s era.

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

Access this article

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

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

    Google Scholar 

  2. Buey MG, Garrido ÁL, Ilarri S (2014, September) An approach for automatic query expansion based on NLP and semantics. In: International conference on database and expert systems applications. Springer, Cham, pp 349–356

  3. Chakraborty SA, Doshi J (2019) Reducing query processing time for non-synonymous materialized queries with differed criteria. Int J Nat Comput Res (IJNCR) 8(2):75–93

    Google Scholar 

  4. Rahman N, Borah B (2020) Improvement of query-based text summarization using word sense disambiguation. Complex Intell Syst 6:75–85. https://doi.org/10.1007/s40747-019-0115-2

    Article  Google Scholar 

  5. Parapar J, Presedo-Quindimil MA, Barreiro Á (2014) Score distributions for pseudo relevance feedback. Inf Sci 273:171–181

    Google Scholar 

  6. Yunzhi C, Huijuan L, Shapiro L, Travillian RS, Lanjuan L (2016) An approach to semantic query expansion system based on Hepatitis ontology. J Biol Res-Thessalon 23:11

    Google Scholar 

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

    Google Scholar 

  8. Padaki R, Dai Z, Callan J (2020, April) Rethinking query expansion for bert Reranking. In: European conference on information retrieval. Springer, Cham, pp 297–304

  9. Greenberg Jane (2001) Optimal query expansion (QE) processing methods with semantically encoded structured thesauri terminology. J Am Soc Inform Sci Technol 52(6):487–498

    Google Scholar 

  10. Custis T, Al-Kofahi K (2007) A new approach for evaluating query expansion: query-document term mismatch. In: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval

  11. Gupta Y, Saini A (2020) A new hybrid document clustering for PRF-based automatic query expansion approach for effective IR. Int J e-Collab (IJeC) 16(3):73–95

    Google Scholar 

  12. Sharma DK, Pamula R, Chauhan DS (2020) A contemporary combined approach for query expansion. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09172-2

    Article  Google Scholar 

  13. de Boer M, Schutte K, Kraaij W (2016) Knowledge based query expansion in complex multimedia event detection. Multimed Tools Appl 75(15):9025–9043

    Google Scholar 

  14. Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to WordNet: an online lexical database*. Int J Lexicogr 3:235–244

    Google Scholar 

  15. Devi MU, Gandhi GM (2014) Wordnet and ontology based query expansion for semantic information retrieval in sports domain, vol 2

  16. Lu M, Sun X, Wang S, Lo D, Duan Y (2015) Query expansion via wordnet for effective code search. In: 2015 IEEE 22nd international conference on software analysis, evolution and reengineering (SANER), pp 545–549

  17. Buey MG, Garrido ÁL, Ilarri S (2014, September) An approach for automatic query expansion based on NLP and semantics. In: International conference on database and expert systems applications. Springer, Cham, pp 349–356

  18. Raza MA, Mokhtar R, Ahmad N, Pasha M, Pasha U (2019) A taxonomy and survey of semantic approaches for query expansion. IEEE Access 7:17823–17833

    Google Scholar 

  19. Leung CH, Li Y, Milani A, Franzoni V (2013, June) Collective evolutionary concept distance based query expansion for effective web document retrieval. In: International conference on computational science and its applications. Springer, Berlin, Heidelberg, pp 657–672

  20. Lemos OA, de Paula AC, Zanichelli FC, Lopes CV (2014, May) Thesaurus-based automatic query expansion for interface-driven code search. In: Proceedings of the 11th working conference on mining software repositories, pp 212–221

  21. Reiss SP (2009, May) Semantics-based code search. In: 2009 IEEE 31st international conference on software engineering. IEEE, pp 243–253

  22. Pinto FJ, Martinez AF, Perez-Sanjulian CF (2008) Joining automatic query expansion based on thesaurus and word sense disambiguation using WordNet. Int J Comput Appl Technol 33(4):271–279

    Google Scholar 

  23. Lemos OAL, de Paula AC, Konishi G, Ossher J, Bajracharya S, Lopes C (2013, September) Using thesaurus-based tag clouds to improve test-driven code search. In: 2013 VII Brazilian symposium on software components, architectures and reuse. IEEE, pp 99–108

  24. Abouenour L, Bouzouba K, Rosso P (2010) An evaluated semantic query expansion and structure-based approach for enhancing Arabic question/answering. Int J Inf Commun Technol 3(3):37–51

    Google Scholar 

  25. Greenberg J (2001) Automatic query expansion via lexical–semantic relationships. J Am Soc Inform Sci Technol 52(5):402–415

    Google Scholar 

  26. Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to WordNet: an online lexical database. Int J Lexicogr 3(4):235–244

    Google Scholar 

  27. Hidayatin L, Rahutomo F (2018, September) Query expansion evaluation for Chatbot application. In: 2018 international conference on applied information technology and innovation (ICAITI), IEEE, pp 92–95

  28. Keyvanpour MR, Zandian ZK, Abdolhosseini Z (2020) HQEBSKG: hybrid query expansion based on semantic knowledgebase and grouping. IETE J Res. https://doi.org/10.1080/03772063.2020.1779618

    Article  Google Scholar 

  29. Bouziri A, Latiri C, Gaussier E (2020) LTR-expand: query expansion model based on learning to rank association rules. J Intell Inf Syst 55:261–286. https://doi.org/10.1007/s10844-020-00596-8

    Article  Google Scholar 

  30. Lin N, Kudinov VA, Zaw HM, Naing S (2020, January) Query expansion for myanmar information retrieval used by WordNet. In: 2020 IEEE conference of russian young researchers in electrical and electronic engineering (EIConRus). IEEE, pp 395–399

  31. Aklouche B, Bounhas I, Slimani Y (2018) Query expansion based on NLP and word embeddings. In: TREC

  32. Kim M, Kim J, Shin M (2020) Word embedding based knowledge representation with extracting relationship between scientific terminologies. Intell Autom Soft Comput 26:141–147

    Google Scholar 

  33. Chaudhary C, Goyal P, Goyal N, Chen YPP (2020) Image retrieval for complex queries using knowledge embedding. ACM Trans Multimed Comput Commun Appl (TOMM) 16(1):1–23

    Google Scholar 

  34. da Silva FT, Maia JEB (2018, October) Query expansion based on local distributional thesauri. In: Anais do XV Encontro Nacional de Inteligência Artificial e Computacional. SBC, pp 924–932

  35. da Silva FT, Maia JE (2019) Query expansion in text information retrieval with local context and distributional model. J Digital Inf Manag 17(6):313

    Google Scholar 

  36. Almarwi H, Ghurab M, Al-Baltah I (2020) A hybrid semantic query expansion approach for Arabic information retrieval. J Big Data 7(1):1–19

    Google Scholar 

  37. Silva A, Mendoza M (2020, September) Improving query expansion strategies with word embeddings. In: Proceedings of the ACM symposium on document engineering 2020, pp 1–4

  38. Liu Q, Huang H, Xuan J, Zhang G, Gao Y, Lu J (2020) A fuzzy word similarity measure for selecting top-k similar words in query expansion. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.2993702

    Article  Google Scholar 

  39. Do Guhan YEKE (2020) Improving document ranking with query expansion based on bert word embeddings, (doctoral dissertation, middle East Technical University)

  40. Dey L, Singh S, Rai R, Gupta S (2005) Ontology aided query expansion for retrieving relevant texts. In: International atlantic web intelligence conference, pp 126–132

  41. He Y, Li Y, Lei J, Leung CHC (2016) A framework of query expansion for image retrieval based on the knowledge base and concept similarity. Neurocomput. 204:26–32

    Google Scholar 

  42. Weber RA (2002) Ontological issues in accounting information systems. In: Arnold V, Sutton S (eds) Researching accounting as an information systems discipline. American Accounting Association, Sarasota, Florida, p 21

    Google Scholar 

  43. Nkisi-Orji I, Wiratunga N, Massie S, Hui KY, Heaven R (2018, September) Ontology alignment based on word embedding and random forest classification. In Joint European conference on machine learning and knowledge discovery in databases. Springer, Cham, pp 557–572

  44. Zhu X, Huang J, Zhou B, Li A, Jia Y (2017) Real-time personalized twitter search based on semantic expansion and quality model. Neurocomputing 254:13–21

    Google Scholar 

  45. Deepak G, Priyadarshini JS (2018) Personalized and Enhanced Hybridized Semantic Algorithm for web image retrieval incorporating ontology classification, strategic query expansion, and content-based analysis. Comput Electr Eng 72:14–25

    Google Scholar 

  46. Akanbi AK, Agunbiade OY, Kuti S, Dehinbo OJ (2014) A semantic enhanced model for effective spatial information retrieval. arXiv:1406.1969

  47. Aurora T, Kaur B (2015) Design and Implementation of semantic based search engine for Punjabi. Int J Comput Appl 126(14):24–27

    Google Scholar 

  48. Akanbi AK (2014) Lb2co: a semantic ontology framework for b2c ecommerce transaction on the internet. arXiv:1401.0943

  49. Jabri S, Dahbi A, Gadi T, Bassir A (2018) Improving retrieval performance based on query expansion with wikipedia and text mining technique. Int J Intell Eng Syst 11:283–292

    Google Scholar 

  50. Farhoodi M, Mahmoudi M, Bidoki AMZ, Yari A, Azadnia M (2009) Query expansion using persian ontology derived from wikipedia. World Appl Sci J 7(4):410–417

    Google Scholar 

  51. Puspitaningrum D, Yulianti G, Prasetya ISWB (2017, August) Wiki-MetaSemantik: a Wikipedia-derived query expansion approach based on network properties. In; 2017 5th international conference on cyber and IT service management (CITSM). IEEE, pp 1–6

  52. Akinribido CT, Afolabi BS, Akhigbe BI, Udo IJ (2011) A fuzzy-ontology based information retrieval system for relevant feedback. Int J Comput Sci 8(1):382–389

    Google Scholar 

  53. Gupta Y, Saini A (2019) A novel term selection based automatic query expansion approach using PRF and semantic filtering. Int J Eng Adv Technol 8(5):130–137

    Google Scholar 

  54. Kadir RA, Yauri RA, Azman A (2018) Semantic ambiguous query formulation using statistical Linguistics technique. Malays J Comput Sci. https://doi.org/10.22452/mjcs.sp2018no1.4

    Article  Google Scholar 

  55. Hoeber O, Yang XD, Yao Y (2005) Conceptual query expansion. In: International Atlantic web intelligence conference, pp 190–196

  56. Peng M, Lin Q, Tian Y, Yang M, Xiao Y, Ni B (2011) Query expansion based on conceptual word cluster space graph. In: Information science and service science(NISS), 5th international conference on new trends, vol 1, pp 128–133

  57. Raza MA, Rahmah M, Noraziah A, Ashraf M (2018) Sensual semantic analysis for effective query expansion. Int J Adv Comput Sci Appl (IJACSA) 9(12):55–60

    Google Scholar 

  58. 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. Int J Web Sci 3(1):32–57

    Google Scholar 

  59. Singh J (2017) Ranks aggregation and semantic genetic approach based hybrid model for query expansion. Int J Comput Intell Syst 10(1):34–55

    Google Scholar 

  60. Yu H, Shi C, Bai Y, Zhang C, Hearne R (2019) Query expansion based on formal concept analysis from retrieved documents. J Internet Technol 20(2):409–421

    Google Scholar 

  61. Yusuf N, Amin M, Yunus M, Wahid N (2019) Query expansion based on explicit-relevant feedback and synonyms for English Quran translation information retrieval. Int J Adv Comput Sci Appl 10(5):227–234

    Google Scholar 

  62. Yusuf N, Yunus MAM, Wahid N, Nawi NM, Samsudin NA, Arbaiy N (2020) Query expansion method for quran search using semantic search and lucene ranking. J Eng Sci Technol 15(1):675–692

    Google Scholar 

  63. Nguyen HM, Nguyen HQ, Tran KN, Vo XV (2015) GeTFIRST: ontology-based keyword search towards semantic disambiguation. Int J Web Inf Syst. https://doi.org/10.1108/IJWIS-06-2015-0019

    Article  Google Scholar 

  64. Song M, Song IY, Hu X, Allen RB (2007) Integration of association rules and ontologies for semantic query expansion. Data Knowl Eng 63(1):63–75

    Google Scholar 

  65. Mahgoub A, Rashwan M, Raafat H, Zahran M, Fayek M (2014, October) Semantic query expansion for Arabic information retrieval. In: Proceedings of the EMNLP 2014 workshop on arabic natural language processing (ANLP), pp 87–92

  66. Devi MU, Gandhi GM (2020) Scalable information retrieval system in semantic web by query expansion and ontological-based LSA ranking similarity measurement. Int J Adv Intell Paradigms 17(1–2):44–66

    Google Scholar 

  67. 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

    Google Scholar 

  68. Lee MC, Tsai KH, Wang TI (2008) A practical ontology query expansion algorithm for semantic-aware learning objects retrieval. Comput Educ 50(4):1240–1257

    Google Scholar 

  69. Jain A, Mittal K, Sabharwal S (2012) Conceptual weighing query expansion on user profiles. In: National conference on communication technologies & its impact on next generation computing ctngc proceedings published by International Journal of Computer Applications (IJCA)

  70. Akrivas G, Wallace M, Andreou G, Stamou G, Kollias S (2002) Context-sensitive semantic query expansion. In: Artificial intelligence systems (ICAIS) IEEE international conference, pp 109–114

  71. Kang JW, Kang HK, Ko MC, Jeon HS, Nam J (2010) A term cluster query expansion model based on classification information in natural language information retrieval. In: IEEE International conference on Artificial intelligence and computational intelligence(AICI), vol 2, pp 172-176, 2010. International Journal of Computer Applications (0975–8887) Volume 168–No. 12, June 2017 20

  72. Chang CH, Hsu CC (1998) Integrating query expansion and conceptual relevance feedback for personalized Web information retrieval. In: Computer networks and ISDN systems, pp 621–623

  73. Furnas GW, Landauer TK, Gomez LM, Dumais ST (1987) The vocabulary problem in human-system communication. Commun ACM 30:964–971

    Google Scholar 

  74. Zingla MA, Chiraz L, Slimani Y (2016) Short query expansion for microblog retrieval. Procedia Comput Sci 96:225–234

    Google Scholar 

  75. Sharma DK, Pamula R, Chauhan DS (2019) A hybrid evolutionary algorithm based automatic query expansion for enhancing document retrieval system. J Amb Intell Human Comput. https://doi.org/10.1007/s12652-019-01247-9

    Article  Google Scholar 

  76. Zhang N, Wang J, Ma Y, He K, Li Z, Liu XF (2018) Web service discovery based on goal-oriented query expansion. J Syst Softw 142:73–91

    Google Scholar 

  77. Samih H, Rady S, Gharib TF (2020) Enhancing image retrieval for complex queries using external knowledge sources. Multimed Tools Appl 79:1–25

    Google Scholar 

  78. Natsev A, Haubold A, Tešić J, Xie L, Yan R (2007, September) Semantic concept-based query expansion and re-ranking for multimedia retrieval. In: Proceedings of the 15th ACM international conference on multimedia, pp 991–1000

  79. 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

    Google Scholar 

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

    Google Scholar 

  81. Fernández-Reyes FC, Hermosillo-Valadez J, Montes-y-Gómez M (2018) A prospect-guided global query expansion strategy using word embeddings. Inf Process Manag 54(1):1–13

    Google Scholar 

  82. 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

    Google Scholar 

  83. Wang Y, Huang H, Feng C (2017, April) Query expansion based on a feedback concept model for microblog retrieval. In Proceedings of the 26th international conference on world wide web, pp 559–568

  84. Huang Q, Yang Y, Cheng M (2019) Deep learning the semantics of change sequences for query expansion. SoftwPract Exp 49(11):1600–1617

    Google Scholar 

  85. Zheng Z, Hui K, He B, Han X, Sun L, Yates A (2020) BERT-QE: contextualized query expansion for document re-ranking. arXiv:2009.07258

  86. Sharma DK, Pamula R, Chauhan DS (2019, February) 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

  87. Sharma DK, Pamula R, Chauhan DS (2018, April) 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

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

  89. Bruce C, Gao X, Andreae P, Jabeen S (2012) Query expansionpowered by wikipedia hyperlinks. In: Thielscher M, Zhang D (eds) AI 2012, LNCS 7691. Springer, Berlin, pp 421–432

    Google Scholar 

  90. Kathuria N, Mittal K, Chhabra A (2017) A comprehensive survey on query expansion techniques, their issues, and challenges. Int J Comput Appl 168(12):17–20

    Google Scholar 

  91. Elberrichi Z, Rahmoun A, Bentaalah MA (2018) Using WordNet for text categorization. Int Arab J Inf Technol (IAJIT) 5(1)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rajendra Pamula.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharma, D., Pamula, R. & Chauhan, D.S. Semantic approaches for query expansion. Evol. Intel. 14, 1101–1116 (2021). https://doi.org/10.1007/s12065-020-00554-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00554-x

Keywords

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy