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Concept based Ranking of Results using an Ontology and Fuzzy Network for a Personalized Web Search Engine

by B. Bhaskara Rao, Valli Kumari Vatsavayi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 13
Year of Publication: 2013
Authors: B. Bhaskara Rao, Valli Kumari Vatsavayi
10.5120/14073-2350

B. Bhaskara Rao, Valli Kumari Vatsavayi . Concept based Ranking of Results using an Ontology and Fuzzy Network for a Personalized Web Search Engine. International Journal of Computer Applications. 81, 13 ( November 2013), 17-24. DOI=10.5120/14073-2350

@article{ 10.5120/14073-2350,
author = { B. Bhaskara Rao, Valli Kumari Vatsavayi },
title = { Concept based Ranking of Results using an Ontology and Fuzzy Network for a Personalized Web Search Engine },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 13 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 17-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number13/14073-2350/ },
doi = { 10.5120/14073-2350 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:59.203494+05:30
%A B. Bhaskara Rao
%A Valli Kumari Vatsavayi
%T Concept based Ranking of Results using an Ontology and Fuzzy Network for a Personalized Web Search Engine
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 13
%P 17-24
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

For a given end user query, a personalized search engine returns an enormous set of related results. The results pertinent to a user are not regularly put on the top. The most fretting issue for the user would be to quickly find the related information in the first few. An efficient personalized search engine should be able to rank the search results and display more relevant ones as first few on the top. It is much more convenient for any user to find their required related result with lesser effort to search for it in the wide and huge list of information produced from the search results. The ranking of personalized web search results is a process of finding small number of highly relevant documents from large number of search results. The relevance is dependent on the user query and context of the subject. Ranking reflects the most relevant results to the user. These are very few and to be placed on top. In this paper, we proposed a method for ranking of search results using fuzzy networks that have been developed using enriched extended user profile. Our approach learns the user profile and constructs fuzzy net by calculating togetherness between concepts, documents or both. This can be done in two phases. In the first phase, we construct the fuzzy nets with enriched extended user profile. In second phase, we evaluate the rank of each document by using clustering algorithm.

References
  1. Guan-yu LI, sui-ming YU and Sha-sha DAI, "Ontology based query system design and implementation", International Conference on network and prarallel computing, pp. 1010-1015, 2007.
  2. Wang Wei, Payam M. Barjaghi and Andrzej Bargiela, "Semantic enhanced information search and retrieval", Sixth International Technology, pp. 218-223, 2007.
  3. Marchiori, M. 1997, "The quest for correct information on web: Hyper search engines", in proceeding of the 6th International World Wide Conference.
  4. S. Park, D. M. Pennock, "Applying Collaborative filtering techniques to movie search for better ranking and browsing", in Proc KDD'07, 2007, pp. 550-559.
  5. Bhaskara Rao Boddu, Valli Kumari Vatsavayi, "A Modified Ontology Based Personalized Seaech Engine Using Bond Energy Algorithm, ACC(2) 2011: 296-306.
  6. Michal Cutler, Yungming Shih, Weiyi Meng. "Using the Structures of HTML Documents to improve retrieval". Usenix symposium on Intenet Technologies and Systems, 1997.
  7. L. Page, S. Brin, R. Motwani, and T. Winograd, "The PageRank CitationRanking: Bringing Order to the WEB", January 1998.
  8. K. Sugiyama, K. hatano, andM. Yoshikawa. "Adaptive web search based on use profile constructed without any effort form users". In Proceedings of WWW 2004, pages 675-684, 2004.
  9. J. Pitkow, H. Schutze, T. Cass, R. Cooley, D. Turnbull, A. . Edmonds, E. Adar and T. Breuel, "Personalized search", Communication of the ACM, vol. 45, pp. 50-55, 2002.
  10. Sun Kim and Byoung-Tak Zhang, "Genetic Mining of HTML Structures for Effective Web Document Retrieval", applied Intelligence, vol. 18, pp. 243-256, 2003.
  11. C. Rocha, D. Schwabe, and M. P. Aragao. "A Hybrid Approach for Searching in the Semantic web". In Proc. 13th International World Wide Web Conference, NewYork, NY, pp. 374-383, 2004.
  12. W. T. McCormick, P. J. Schweitzer, and T. W. White "Problem Decomposition and data reorganization by a clustering Technique" Oper. Res. 1972 20(5): 993-1009.
  13. Tian Chong "A Kind of Algorithm for PageRank Based on Classified Tree In Search Engine", International Conference on Computer Application and System Modeling, vol. 13, pp 538-540, 2010.
  14. Rohini U, Vasudeva Varma "A Novel Approch for Re-ranking of Search Results using Collaborating Filtering" IEEE proceeding of ICCTA 2007, pp 491-496.
  15. Jae-won Lee, Han-joon Kim, sang-goo Lee "Applying taxonomic Knowledge and Semantic Collaborating filtering to Personalized Search: a Bayesian belief Network Based approach" International Asia-Pacific Web Conference, 2010, pp 75-81.
  16. Jianxiong Yang, Junzo Watada "Decomposition of Term-Document Matrix Represenation for Clustering Analysis", International Conference on Fuzzy systems, pp. 976-983, 2011.
  17. WordNet, a lexical database for English, Princeton University. www. princeton. edu/wornet/download/ current-version/.
Index Terms

Computer Science
Information Sciences

Keywords

Personalized Search Engine Ranking Fuzzy Networks Document Ontology Concepts Relevance Enriched Extended User Profile.

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