Yet another way of Ranking web Documents Based On Semantic Similarity

Abstract: In today’s web enabled world, searching for relevant information on web has been an important topic of research. Semantic similarity aims at providing robust tools for standardizing the content and delivery of relevant information across communicating information sources. Most of the times the user gets lots of irrelevant data as a result of poorly implemented search process. To avoid this, a ranking scheme is proposed, which provides the search result- set according to the better understood and correctly interpreted user query. This is done by considering the relevance of the query by keeping the user view in mind and also the semantics of the document and the user query. The simple lexical and/or syntactical matching usually used by search engines does not extract web documents to the user expectations. The proposed solution provides the most relevant data to user ranked in their relevance. The proposed ranking scheme for the semantic web search engine functions by finding the semantic similarity between the information available on the web and the query which is specified by the user. This approach considers both the syntactic structure of the document and the semantic structure of the document and the query. The objective of this paper is to demonstrate that a semantic similarity based ranking scheme will provide much better results than those by the prevailing methods. In this Paper an algorithm will be implemented that provides ranking scheme for the semantic web documents by finding the semantic similarity between the documents and the query which is specified by the user. The algorithm considers both syntactical and semantic similarities of the query and categorizes the search results based on the most probable and most appropriate interpretation of the query based on various interpretations taking into account all the words and their combinations in the query.

Keywords: semantic similarity, Ontology, IDFT