IJMECS Vol. 8, No. 2, 8 Feb. 2016
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Concepts, Concept Based Thesaurus Network, Latent Semantic Analysis, Information Retrieval System, Information Retrieval, Best Fit Concept Based Document Cluster
One natural and successful technique to have retrieved documents that is relevant to users’ intention is by expanding the original query with other words that best capture the goal of users. However, no matter the means implored on the clustered document while expanding the user queries, only a concept driven document clustering technique has better capacity to spawn results with conceptual relevance to the users’ goal. Therefore, this research extends the Concept Based Thesaurus Network document clustering techniques by using the Latent Semantic Analysis tool to identify the Best Fit Concept Based Document Cluster in the network. The Fuzzy Latent Semantic Query Expansion Model process achieved a better precision and recall rate values on experimentation and evaluations when compared with some existing information retrieval approaches.
Olufade F.W Onifade, Ayodeji O.J. Ibitoye, "Fuzzy Latent Semantic Query Expansion Model for Enhancing Information Retrieval", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.2, pp.49-53, 2016. DOI:10.5815/ijmecs.2016.02.06
[1]D Christopher, Manning, P.R., Schtze, H.: An Introduction to Information Retrieval. Cambridge University, 2009.
[2]P. H. Cleverley and S. Burnett, “Retrieving haystacks: a data driven information needs model for faceted search,” Journal of Information Science, vol. 41, no. 1, pp. 97–113, 2015.
[3]C. D Manning, P. Raghavan, and H Schutze,. Introduction to Information Retrieval. Cambridge University Press, 2008.
[4]M. C. de Andrade and A. A. Baptista, “Researchers’ information needs in the bibliographic database: A literature review,” Information Services and Use, vol. 34, no. 3, pp. 241–248, 2014.
[5]Hele-Mai and Tanel Mauri, A survey of concept based information retrieval tool, 2008.
[6]B He, and I Ounis. Studying Query Expansion Effectiveness. Proceedings of ECIR’09 European Conference in Information Retrieval, 2009.
[7]H Cui, J Wen,.R., J.-Y Nie, and Ma, W.-Y. Query expansion by mining user logs. Knowledge and Data Engineering, IEEE Transactions on, 15(4):829–839, 2003.
[8]G. Sudeepthi, G. Anuradha, P. M. Surendra, and P. Babu, “A Survey on Semantic Web Search Engine,” vol. 9, no. 2, pp. 241– 245, 2012.
[9]Baeza-Yates, B. Ribeiro-Neto, “Modern information retrieval”, Addison Wesley, 2011.
[10]G Cao, J.-Y Nie, J Gao, and Robertson, S. Selecting good expansion terms for pseudo-relevance feedback. In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 243{250, New York, NY, USA. ACM. 2008.
[11]Xin He and Mark Baker. xhrank: Ranking entities on the semantic web. In ISWC Posters & Demos’10.
[12]Y Lv, and C Zhai. Positional relevance model for pseudo-relevance feedback. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '10, pages 579{586, New York, NY, USA. ACM. 2010.
[13]Axel Ngonga. Generating conjunctive queries for keyword search on rdf data. In In Sixth ACM WSDM (Web Search and Data Mining) Conference, 2013.
[14]Julia Stoyanovich, Srikanta J. Bedathur, Klaus Berberich, and Gerhard Weikum. Entityauthority: Semantically enriched graph-based authority propagation. In WebDB, 2007.
[15]M Dragoni,. Celia da Costa Pereira, G.B Andrea. Tettamanzi, A Conceptual Representation of Documents and Queries for Information Retrieval System using Light Ontologies, Expert Systems with Applications 39 pp.10376–10388, Elsevier, 2012.
[16]K Sugimoto, H Nishizaki, and Y Sekiguchi, “Effect of document expansion using web documents for spoken documents retrieval,” in Proceedings of the 2nd Asia-Paciļ¬c Signal and Information Processing Association Annual Summit and Conference, pp. 526–529, 2010.
[17]O Hoeber., X.D. Yang, Y. Yao, Conceptual Query Expansion, Advances in web intelligence, Springer, LNAI 3528, pp. 190-196, 2005.
[18]M Beigbeder, and A. Mercier. An information retrieval model using the fuzzy proximity degree of term occurences. Proceedings of SAC ’05. New York, USA: ACM Press. 2005.
[19]F Clarizia, L Greco, P Napoletano: A new technique for identification of relevant web pages in informational queries results. In: Proceedings of the 12th International Conference on Enterprise Information Systems: Databases and Information Systems Integration. 8-12 June 2010.
[20]T Akiba. and K Honda., “Effects of query expansion for spoken documnet passage retrieval,” in Proceedings of International Conference on Speech Communication and Technology, pp. 2137–2140, 2011.