USE OF INVERSE-TERM FREQUENCY (ITF) AND RELEVANCE FEEDBACK TO IMPROVE QUERY EXPANSION
DOI:
https://doi.org/10.61841/xz6d5j17Keywords:
Inverse-term frequency, Query Expansion, Precision, KLD-mean, Sementic similarity, term-poolingAbstract
The field which is full of concerned with the structure, analysis, institution, space and searching is Retrieval of information. It has now become an essential field of investigation and research under computer science because of the amount of data available in full text, hypertext, administrative text, directory, numeric, or bibliographic text has increased dramatically. There are several points of information or data retrieval system on which it is compulsory to conduct a proper research work. The objective of this research is to investigate the query expansion procedure using inverse-term frequency to improve the efficiency and accuracy of the information retrieval system. As the method of evaluation of query expansion, we will remove unrelated, redundant and ambiguous words from the retrieved document based on user- query. In proposed work, we introduce a new method
for query expansion (QE) which is based on inverse term frequency with relevance feedback. Fetching the top revive documents use as in relevance feedback for additional QE terms and constructing candidate terms. Process of scoring method assigns score to unique terms and applying inverse term frequency (itf) to produce the rank list of terms.
These terms will filter through semantic action and reweighting produce updated (expanded) query which will again send to search tool.
Downloads
References
[1] C. Mooers, “Information retrieval viewed as temporal signaling”, in the Proc. of the
International Congress of Mathematicians, Vol 1, pp 572- 573, 1950.
[2] D. Lauren and B. Joseph, “Information Retrieval and Processing, Melville”, SIGIR,
Vol. 11, No. 2, pp 07 – 09, Fall 1976.
[3] H. J. Peat and P. Willett, “The Limitations of Term Co-Occurrence Data for Query
Expansion in Document Retrieval Systems”, ASIS, Vol. 42, No. 5, pp.378–383, June 1991.
[4] Z. Wu and M. Palmer, “Verb semantics and lexical selection,” in Proceedings of
the Annual Meeting of the Associations for Computational Linguistics, pp. 133–138, 1994.
[5] P. Resnik, “Using information content to evaluate semantic similarity in a
taxonomy,” in Proceedings of the 14th International Joint Conference on Artificial
Intelligence (IJCAI ’95), vol. 1, pp. 448–453, Montreal, Canada, 1995.
[6] C. Leacock and M. Chodorow, “Combining local context and WordNet similarity
for word sense identification,” in WordNet. An Electronic Lexical Database, pp. 265–283,
MIT Press, Cam- bridge- Mass, USA, 1998.
[7] J. Ricardo Baeza-Yates and B. Ribeiro-Neto, "Morden Information Retrival" in
ACM Press, Addison Wesley Longman Ltd.Engaland, 1999.
[8] A. M. Lam-Adesina and G.J.F. Jones, “Applying Summarization Techniques for
Term Selection in Relevance Feedback”, in Proc of the 24th annual international ACM
SIGIR conf SIGIR '01, pp.01-09, Jan 2001.
[9] A. Singhal, “Modern Information Retrieval: A Brief Overview”, Computer Society
Technical Committee on Data Engineering, Vol. 24, No. 4, pp. 35-42, 2001.
[10] B. Liu, "Web Data Mining: Exploring Hyperlinks, Contents and Usage Data”,
Springer-Verlag, Berlin Heidelberg, 2002.
[11] V. E. Verelas and P. Raftopoulou, “Semantic similarity methods in Word-Net and
their application to IR on the web”, in Web Information and Data Management, pp. 10–16,
2005.
[12] D. FRANK, "Comparing Rank and Score Combination Methods for Data Fusion in
Information Retrieval", in Springer Science & Business Media The Netherlands, vol. 08,
pp. 449–480, 2005.
[13] S.M.Shafi, and A. Rafiq, "Precision and Recall of five search engine for retrieval
of Scholarly Information in the field of Biotechnologyfor Data Fusion in Information
Retrieval", in Webology, vol.02, article 12, Aug 2005.
[14] C. Fellbaum,"Word Net(s)" Encyclopedia of Language & Linguistics, vol. 13, pp.
665–670, 2006,
[15] M. Song, Y. Song, X. Hu and B. Robert, “ Integration of association rules and
ontologies for semantic query expansion”, Data & Knowledge Engineering, Vol. 63, No.1,
pp 63-75 ,October 2007.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Author
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
