Efficient Framework for Semantic Search on Web

Abstract

With frequent and faster growth of the Web and dependence on the Web for relevant information retrieval, search engines have become the most popular and powerful tool for accessing desired information online. However, it is observed that the Web pages returned by even a renowned search engine are not so accurately useful. The necessity of finding the most relevant information has given rise to the research in the field of semantic search. Traditional Web search methods where basic relevance criteria rely primarily on the presence of query keywords within the returned pages are required to be replaced with more effective semantic search techniques.Semantic based search would be able to provide users a more intelligent form of finding what they are looking for within the global source of information available online. In this thesis, various approaches for semantic based search on Web have been studied and analyzed resulting in the identification of two broad perspectives of semantic search as elaborated in the chapter on literature review. Fundamental limitations identified in the existing approaches have been major motivation for proposing efficient semantic based search approach. Later a framework for QUery-context based Information retrieval using Corpus Knowledge (QUICK) is proposed which has been elaborated in the chapter on proposed framework. Here the Web pages returned by a baseline system in response to original query are used to generate a corpus of words related to the query category. The word tokens which are laying in the close proximity of the query keywords are supposed to be semantically related to the original query. The relative positioning and frequency of the words with respect to the query word is assigned due importance using probabilistic feature of the proposed approach which in turn ensures to have greater probability in reaching to the context of the query.