WebSifter II: A personalizable meta-search agent based on weighted semantic taxonomy tree

This paper addresses the problem of specifying Web searches and, retrieving, filtering and rating Web pages so as to improve the relevance and quality of hits, based on the users search intent and preferences. We present a methodology, and architecture for an agent-based system, called WebSifter II, that captures the semantics Of a user's decision-oriented search intent, transforms the semantic query into target queries for existing search engines, and then ranks the resulting page hits according to a user-specified weighted-rating scheme. Users create personalized search taxonomies via our Weighted Semantic-Taxonomy Tree. Consulting a Web taxonomy agent such as Wordnet can refine the terms in the tree. The concepts represented in the tree are then transformed into a collection of queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity, and authority/hub rating.