CFP last date
20 May 2024
Reseach Article

Fuzzy Semantic Search Engine

by Dharmish Shah, Jheel Somaiya, Sindhu Nair
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 107 - Number 15
Year of Publication: 2014
Authors: Dharmish Shah, Jheel Somaiya, Sindhu Nair
10.5120/18829-0272

Dharmish Shah, Jheel Somaiya, Sindhu Nair . Fuzzy Semantic Search Engine. International Journal of Computer Applications. 107, 15 ( December 2014), 25-27. DOI=10.5120/18829-0272

@article{ 10.5120/18829-0272,
author = { Dharmish Shah, Jheel Somaiya, Sindhu Nair },
title = { Fuzzy Semantic Search Engine },
journal = { International Journal of Computer Applications },
issue_date = { December 2014 },
volume = { 107 },
number = { 15 },
month = { December },
year = { 2014 },
issn = { 0975-8887 },
pages = { 25-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume107/number15/18829-0272/ },
doi = { 10.5120/18829-0272 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:41:09.389460+05:30
%A Dharmish Shah
%A Jheel Somaiya
%A Sindhu Nair
%T Fuzzy Semantic Search Engine
%J International Journal of Computer Applications
%@ 0975-8887
%V 107
%N 15
%P 25-27
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Almost all the search engines that exist retrieve web pages by finding the exact keywords. The traditional keyword-based search engines suffer many problems, like synonyms and terms similar to keywords are not taken into account to search web pages, they treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to think of and input several similar keywords individually to complete a search. The restriction of exact keywords makes it inconvenient for users to search web pages. Many valuable web pages would be omitted if users did not search for several similar keywords individually. While users input several keywords to search web pages, different keywords may have different degrees of importance in their opinions. Traditional search engines treat all keywords as the same importance and cannot differentiate the importance of one keyword from that of another. The problem of information overload makes it difficult for users to find really useful information from a large amount of search results. Traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. To alleviate the mentioned problems that the users face, in this paper we have proposed and applied the fuzzy logic theory and the semantic search techniques to develop a fuzzy semantic search engine.

References
  1. Lien-Fu Lai, Chao-Chin Wu, Pei-Ying Lin, "Developing a Fuzzy Search Engine Based on Fuzzy Ontology and Semantic Search". Dept. of Computer Science and Information Engineering National Changhua University of Education Changhua, R. O. C.
  2. http://en. wikipedia. org/wiki/Web_Ontology_Language
  3. http://en. wikipedia. org/wiki/Semantic_search
  4. http://gaia. isti. cnr. it/straccia. /software/FuzzyOWL/index. html
  5. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
  6. P. T. Chang, K. C. Hung, K. P. Lin, and C. H. Chang, a Comparison of Discrete Algorithms for Fuzzy Weighted Average, IEEE Transactions on Fuzzy Systems, pp. :663-675, Oct. 2006.
  7. K. W. Church and P. Hanks Word Association Norms, Mutual Information and Lexicography, Computational Linguistics 16(1):22-29, Mar. 1990.
  8. D. Dubois and H. Prade. Fuzzy sets and systems: theory and applications. New York, London, 1980.
  9. L. F. Lai, C. C. Wu, M. Y. Shih, L. T. Huang, and W. Chiou. Parallel Processing for Fuzzy Queries in Human Resources Websites. Journal of Internet Technology, 7(11):943-953, Dec. 2010.
  10. Y. C. Lin, L. F. Lai, C. C. Wu, and L. T. Huang. A Self-Adaptation Approach to Fuzzy-Go Search Engine. The 2010 InternationalComputer Symposium (ICS 2010), pp. 1020-1025, Dec. 2010.
  11. E. W. T. Ngai and F. K. T. Wat. Fuzzy Decision Support System for Risk Analysis in E-Commerce Development. Decision Support Systems. pp. :235-255, Aug. 2005.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy Search Engine Fuzzy Ontology Semantic Search.