IEEE Transactions on Software Engineering
An Intelligent Query Expansion of Searching Related Text Information by Keywords
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Rule-based intelligence in the Semantic Web-or-"I'll settle for a web that's just not so dumb!"
RULEML '06 Proceedings of the Second International Conference on Rules and Rule Markup Languages for the Semantic Web
IEEE Internet Computing
A Fuzzy-set based Semantic Similarity Matching Algorithm for Web Service
SCC '08 Proceedings of the 2008 IEEE International Conference on Services Computing - Volume 2
Toward the Intelligent Web Systems
CICSYN '09 Proceedings of the 2009 First International Conference on Computational Intelligence, Communication Systems and Networks
Web 3.0: A Real Personal Web! More Opportunities and More Threats
NGMAST '09 Proceedings of the 2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies
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Semantic Web is, without a doubt, gaining momentum in both industry and academia. The word "Semantic" refers to "meaning" -- a semantic web is a web of meaning. A web that knows what the entities on the web mean can make use of that knowledge. Why do we think that the web would be improved, if it understood the meaning of its contents? Doesn't it understand it now? Google is very good at correcting typing mistakes, figuring out what I "meant" when I miss-typed a query or suggesting keyword to expand our search query. In this paper we redefine the idea of searching related text information on web. The syntactical characters of related keywords and texts are described in detail, but it doesn't involve semantics of the keywords. Hence computers are able to determine the related keywords and texts without actually understanding the meanings or relevance of the keywords. Here Hypergeometric distribution model is used which is a discrete probability distribution that describes the number of successes in a sequence of n keywords to be searched from a finite population without replacement.