WebSifter II: A Personalizable Meta-Search Agent Based on Weighted Semantic Taxonomy Tree (original) (raw)

This paper addresses the problem of specifying, retrieving, filtering and rating Web searches so as to improve the relevance and quality of hits, based on the user's 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 decisionoriented 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. The terms in the tree can be refined by consulting a web taxonomy agent such as Wordnet. 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 userspecified preferences such as semantic relevance, syntactic relevance, categorical match, page popularity and authority/hub rating.

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