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Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval

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Abstract

Query expansion is an important approach utilized to improve the efficiency of data retrieval tasks. Numerous works are carried out by the researchers to generate fair constructive results; however, they do not provide acceptable results for all kinds of queries particularly phrase and individual queries. The utilization of identical data sources and weighting strategies for expanding such terms are the major cause of this issue which leads the model unable to capture the comprehensive relationship between the query terms. In order to tackle this issue, we developed a novel approach for query expansion technique to analyze the different data sources namely WordNet, Wikipedia, and Text REtrieval Conference. This paper presents an Improved Aquila Optimization-based COOT(IAOCOOT) algorithm for query expansion which retrieves the semantic aspects that match the query term. The semantic heterogeneity associated with document retrieval mainly impacts the relevance matching between the query and the document. The main cause of this issue is that the similarity among the words is not evaluated correctly. To overcome this problem, we are using a Modified Needleman Wunsch algorithm algorithm to deal with the problems of uncertainty, imprecision in the information retrieval process, and semantic ambiguity of indexed terms in both the local and global perspectives. The k most similar word is determined and returned from a candidate set through the top-k words selection technique and it is widely utilized in different tasks. The proposed IAOCOOT model is evaluated using different standard Information Retrieval performance metrics to compute the validity of the proposed work by comparing it with other state-of-art techniques.

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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.

References

  1. Kumar R, Sharma SC (2018) Information retrieval system: an overview, issues, and challenges. Int J Technol Diffus (IJTD) 9(1):1–10

    Article  MathSciNet  Google Scholar 

  2. Sundararaj V, Selvi M (2021) Opposition grasshopper optimizer based multimedia data distribution using user evaluation strategy. Multimed Tools Appl 80(19):29875–29891

  3. Sundararaj V (2019) Optimal task assignment in mobile cloud computing by queue based ant-bee algorithm. Wireless Pers Commun 104(1):173–197

  4. Maksimov N, Golitsina O, Monankov K, Gavrilkina A (2020) Knowledge representation models and cognitive search support tools. Procedia Comput Sci 169:81–89

    Article  Google Scholar 

  5. Oyefolahan IO, Aminu EF, Abdullahi MB, Salaudeen MT (2018) A review of ontology-based information retrieval techniques on generic domains.

  6. Manzoor S, Rocha YG, Joo SH, Bae SH, Kim EJ, Joo KJ, Kuc TY (2021) Ontology-based knowledge representation in robotic systems: a survey oriented toward applications. Appl Sci 11(10):4324

    Article  Google Scholar 

  7. Yang D, Shen DR, Yu G, Kou Y, Nie TZ (2013) Query intent disambiguation of keyword-based semantic entity search in dataspaces. J Comput Sci Technol 28(2):382–393

    Article  MATH  Google Scholar 

  8. Jain S, Seeja KR, Jindal R (2021) A fuzzy ontology framework in information retrieval using semantic query expansion. Int J Inf Manag Data Insights 1(1):100009

    Google Scholar 

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

  10. 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

    Article  Google Scholar 

  11. Afuan L, Ashari A, Suyanto Y (2019) A study: query expansion methods in information retrieval. J Phys Conf Series 1367(1):012001 (IOP Publishing)

    Article  Google Scholar 

  12. Azad HK, Deepak A (2019) A new approach for query expansion using wikipedia and WORDNET. Inf Sci 492:147–163

    Article  Google Scholar 

  13. 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 

  14. Malik S, Shoaib U, Bukhari SAC, El Sayed H, Khan MA (2022) A hybrid query expansion framework for the optimal retrieval of the biomedical literature. Smart Health 23:100247

    Article  Google Scholar 

  15. Wang J, Pan M, He T, Huang X, Wang X, Tu X (2020) A pseudo-relevance feedback framework combining relevance matching and semantic matching for information retrieval. Inf Process Manage 57(6):102342

    Article  Google Scholar 

  16. Jafarzadeh P, Ensan F (2022) A semantic approach to post-retrieval query performance prediction. Inf Process Manag 59(1):102746

    Article  Google Scholar 

  17. Dahir S, El Qadi A (2021) A query expansion method based on topic modeling and DBpedia features. Int J Inf Manag Data Insights 1(2):100043

    Google Scholar 

  18. Kaur N, Aggarwal H (2021) Query reformulation approach using domain specific ontology for semantic information retrieval. Int J Inf Technol 13(5):1745–1753

    Google Scholar 

  19. Kammoun H, Gabsi I, Amous I (2022) Mesh-based semantic indexing approach to enhance biomedical information retrieval. Comput J 65(3):516–536

    Article  Google Scholar 

  20. 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 

  21. Selvalakshmi B, Subramaniam M (2019) Intelligent ontology-based semantic information retrieval using feature selection and classification. Clust Comput 22(5):12871–12881

    Article  Google Scholar 

  22. 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 29(8):2132–2144

    Article  Google Scholar 

  23. Aspland E, Harper PR, Gartner D, Webb P, Barrett-Lee P (2021) Modified Needleman–Wunsch algorithm for clinical pathway clustering. J Biomed Inform 115:103668

    Article  Google Scholar 

  24. Wang S, Jia H, Abualigah L, Liu Q, Zheng R (2021) An improved hybrid aquila optimizer and harrishawks algorithm for solving industrial engineering optimization problems. Processes 9(9):1551

    Article  Google Scholar 

  25. Naruei I, Keynia F (2021) A new optimization method based on COOT bird natural life model. Expert Syst Appl 183:115352

    Article  Google Scholar 

  26. Data-English documents. Text REtrieval conference (TREC) english documents. (n.d.). Retrieved from https://trec.nist.gov/data/docs_eng.html. Accessed 27 May 2022

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Authors and Affiliations

  1. Electronics and Computer Discipline, DPT, Indian Institute of Technology, Roorkee, India

    Ram Kumar & S. C. Sharma

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  1. Ram Kumar
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  2. S. C. Sharma
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [RK] and [SCS]. The first draft of the manuscript was written by [RK] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ram Kumar.

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Kumar, R., Sharma, S.C. Hybrid optimization and ontology-based semantic model for efficient text-based information retrieval. J Supercomput 79, 2251–2280 (2023). https://doi.org/10.1007/s11227-022-04708-9

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