Structured Intelligent Search Engine for effective information retrieval using query clustering technique and Semantic Web

In this paper we present Structured Intelligent Search Engine (SISE), personalized search engine architecture catering the interest each user. In SISE, query clustering technique is used along with web semantics is used to produce much efficient and precise search results than current existing search systems. This claim is supported by the experimental results carried out on the NASA and FIRE data set. At present a vast majority of the popular search engines majorly depend upon the term frequency of the query issued by the users i.e., how many times the query has been repeatedly used in the article. Despite the rapid advancement in the field of information retrieval and especially in the advancement in the field of search engine there is still a lot of scope for development of personalized search engine. Apart from the problems of scaling traditional search techniques to deal with the ambiguous query, there are new technical challenges involved with using the additional information present in user search history to produce better search results. This paper addresses this question of how to build a personalised search system which can exploit the context of the query by using users search history to gather additional information present in user history.