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Reseach Article

Conceptual Weighing Query Expansion based on User Profiles

Published on November 2012 by Amita Jain, Kanika Mittal, Smita Sabharwal
National Conference on Communication Technologies & its impact on Next Generation Computing 2012
Foundation of Computer Science USA
CTNGC - Number 1
November 2012
Authors: Amita Jain, Kanika Mittal, Smita Sabharwal
6ce326f8-b080-4f5e-b03c-c9f57a2e7a0c

Amita Jain, Kanika Mittal, Smita Sabharwal . Conceptual Weighing Query Expansion based on User Profiles. National Conference on Communication Technologies & its impact on Next Generation Computing 2012. CTNGC, 1 (November 2012), 11-15.

@article{
author = { Amita Jain, Kanika Mittal, Smita Sabharwal },
title = { Conceptual Weighing Query Expansion based on User Profiles },
journal = { National Conference on Communication Technologies & its impact on Next Generation Computing 2012 },
issue_date = { November 2012 },
volume = { CTNGC },
number = { 1 },
month = { November },
year = { 2012 },
issn = 0975-8887,
pages = { 11-15 },
numpages = 5,
url = { /proceedings/ctngc/number1/9047-1003/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Communication Technologies & its impact on Next Generation Computing 2012
%A Amita Jain
%A Kanika Mittal
%A Smita Sabharwal
%T Conceptual Weighing Query Expansion based on User Profiles
%J National Conference on Communication Technologies & its impact on Next Generation Computing 2012
%@ 0975-8887
%V CTNGC
%N 1
%P 11-15
%D 2012
%I International Journal of Computer Applications
Abstract

Proper query terms significantly affect the performance of information retrieval systems. In this paper, a conceptual weighting method for query expansion is proposed with the help of user profile. Here, the users' initial queries and the retrieved documents based on the user's query (top n relevant documents) are analyzed and then the relevant terms from the documents retrieved are weighted. The terms having higher weight and the terms from the previous searches with a greater threshold weight will be selected and are used to derive the concepts in the concept network which are then connected to the phrases. Based on the matching of those phrases with that of the query phrases, additional query terms are selected and based on those additional query terms, the user's original query is expanded and the search is enhanced.

References
  1. Efthimis N. Efthimiadis. Query expansion, Annual Review of Information Systems and Technology (ARIST), 31, 1996.
  2. G. W. Furnas, T. K. Landauer, L. M. Gomez, and S. T. Dumais, The vocabulary problem in human-system communication. Communications of the ACM, 30(11), 1987
  3. R. Baeza-Yates, B. Ribeiro-Neto, Modern information retrieval, Addison Wesley, 2011.
  4. Y. Qiu and H. P. Frei. Concept based query expansion, In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, 1993.
  5. Martin Kracker, A Fuzzy Concept Network Model and its applications ,IEEE International Conference on fuzzy systems , 1992
  6. H. K Kang, B. M. Ryu and I. K Whang, An effective Document Classification System Based on Concept Probability Vector, Lecture Notes in Computer Science 3309, pp. 457-462, 2005.
  7. G. Salton, C. Buckley, Term-weighting approaches in automatic text retrieval, Information processing & Management, Volume 24, No 5, pp. 513-523, 1988.
  8. Kim, B. M. , Kim, J. Y. and Kim, J. , Query term expansion and reweighting using term co-occurrence similarity and fuzzy inference, Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, Canada, Vol. 2, pp. 715-720, 2001.
  9. C. Wang, Yajun DU, P. Zhang, B. Han, A Term-Reweighting Method for Query Expansion, Journal of Computational Information Systems 6:11, pp. 3779-3785, 2010.
  10. John W. Kang, Hyun-Kyu. Kang, A Term Cluster Query Expansion Model based on Classification Information in Natural Language Information Retrieval, International Conference on Artificial Intelligence and Computational Intelligence, 2010.
  11. O. Hoeber, X. D. Yang, Y. Yao, Conceptual Query Expansion, Advances in web intelligence, Springer, LNAI 3528, pp. 190-196, 2005.
  12. R. Bodner, F . Song, Knowledge-based approaches to query expansion in information retrieval. In McCalla, Advances in Artificial Intelligence, pp. 146-158, 1996.
  13. Qiu, Y. and Frei, H. P, Concept based query expansion, In Proceedings of the 16th annual international ACM SIGIR conference on Research and Development in Information Retrieval, ACM Press, pp. 160-170, 1993.
  14. S. Klink, Query reformulation with collaborative concept-based expansion, In Proceedings of the First International Workshop on Web Document Analysis WDA, 2001.
  15. M. Speretta, S. Gauch, Personalized search based on user search hierarchies, Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005.
  16. K. Sugiyama, K. Hatano, M. Yoshikawa, Adaptive Web Search Based on User Profile Constructed without Any Effort from Users, ACM 1-58113-844-X/04/0005, 2004.
  17. Youjin Chang, I. Choi, J. Choi, M. Kim, V. V. Raghavan , Conceptual Retrieval based on Feature Clustering of Documents, 2002.
  18. M. Daoud, L. T. Lechani, M. Boughanem, A Session Based Personalized Search Using an Ontological User Profile, ACM 978-1-60558-166-8/09/03, 2009.
  19. S. M. Chen, Y. J. Horng, Fuzzy query processing for document retrieval based on extended fuzzy concept networks, IEEE Trans. Systems Man Cybernet. —Part B: Cybernet. 29 (1) pp. 126–135, 1999.
  20. S. M. Chen, Y. J. Horng, C. H. Lee, Document retrieval using fuzzy valued concept networks, IEEE Trans. Systems Man and Cybernet. —Part B: Cybernet. 31 (1) pp. 111–118, 2001.
  21. S. M. Chen, W. H. Hsiao, Y. J. Horng, A knowledge-based method for fuzzy query processing for document retrieval, Cybernet. Systems Internat. J. 28 (1) pp. 99–119, 1997.
  22. M. Kracker, A fuzzy concept network model and its applications, Proc. First IEEE Internat. Conf. on Fuzzy Systems, San Diego, USA, pp. 761–768, 1992.
  23. S. M. Chen, Y. J. Horng, C. H. Lee, Fuzzy information retrieval based on multi-relationship fuzzy concept networks, Elsevier, 2002.
  24. Koutrika, Loannidis, A Unified User Profile Framework for Query Disambiguation and Personalization, Proceedings of the Workshop PIA, 2005.
  25. Y. Jung, H. Park and D. Du, An Effective Term Weighting Scheme for Information Retrieval, Computer Science Technical Report TR008, Department of Computer Science, University of Minnesota, Minneapolis, minnesota, pp. 1-15, 2000.
  26. GG Chowdhury, Natural Language Processing, Annual Review of Information Science and Technology, 2003.
  27. Tanveer Siddiqui and U. S. Tiwari "Natural Language Processing and Information Retrieval" Oxford University press, 2008.
  28. S. J,Chen, H. C. Chu, A New Method for Fuzzy Query Processing of Document Retrieval based on Extended Fuzzy Concept Networks, International Conference on Electronics and Information Engineering, 2010.
  29. M. Dragoni, Celia da Costa Pereira, Andrea G. B. Tettamanzi, A Conceptual Representation of Documents and Queries for Information Retrieval System using Light Ontologies, Expert Systems with Applications 39 (2012) pp. 10376–10388, Elsevier, 2012.
  30. H. Imran, A. Sharan, Thesaurus and Query Expansion, International Journal of Computer science & Information Technology (IJCSIT), vol. 1, No 2, November 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Natural Language Processing Query Expansion Term-weighting Concept Networks User Profiles Information Retrieval