Semantic Relation Based Page Ranking Using Genetic Algorithm and Conceptual Graph

With the massive growth and large volume of the web it is the function of search engine to recover results based on the user preferences. But most of the time the user gets useless pages. The next generation web architecture, semantic web reduces the burden of the user by performing search based on semantics instead of keywords. A conceptual graph (CG) is a notation for logic based on the existential graphs and the semantic networks of artificial intelligence. Conceptual graphs are formally defined in an abstract syntax that is independent of any notation, but the formalism can be represented in several different concrete notations. Conceptual graph representation is used in the conservation of text to semantic relations in order to minimize the edges. In this paper, we propose a relation based page ranking algorithm using genetic algorithm and conceptual graph which can be used along with semantic search engine. The proposed method uses Jena API and GATE tool API and the documents can be recovered based on their annotation features and relations. A preliminary experiment shows that the proposed method generates relevant documents in higher ranking.