Ranking of Web Documents using Semantic Similarity

In recent years, semantic search for relevant documents on web has been an important topic of research. Many semantic web search engines have been developed like Ontolook, Swoogle, etc that helps in searching meaningful documents presented on semantic web. The concept of semantic similarity has been widely used in many fields like artificial intelligence, cognitive science, natural language processing, psychology. To relate entities/texts/documents having same meaning, semantic similarity approach is used based on matching of the keywords which are extracted from the documents using syntactic parsing. The simple lexical matching usually used by semantic search engine does not extract web documents to the user expectations. In this paper we have proposed a 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 novel approach proposed in this paper not only relies on the syntactic structure of the document but also considers the semantic structure of the document and the query. The approach used here includes the lexical as well as the conceptual matching. The combined use of conceptual, linguistic and ontology based matching has significantly improved the performance of the proposed ranking scheme. We explore all relevant relations between the keywords exploring the user's intention and then calculate the fraction of these relations on each web page to determine their relevance with respect to the query provided by the user. We have found that this semantic similarity based ranking scheme gives much better results than those by the prevailing methods.