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Intelligent Web Search via Personalizable Meta-search Agents


Larry Kerschberg, Wooju Kim, and Anthony Scime

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Abstract

This paper addresses several problems associated with the specification of Web searches, and the retrieval, filtering, and rating of Web pages in order to improve the relevance, precision and quality of search results. A methodology and architecture for an agent-based system, WebSifter is presented, that captures the semantics of a user's search intent, transforms the semantic query into target queries for existing search engines, and ranks resulting page hits according to a user-specified, weighted-rating scheme. Users create personalized search taxonomies, in the form of a Weighted Semantic-Taxonomy Tree. Consultation with a Web-based ontology agent refines the terms in the tree with positively- and negatively-related terms. The concepts represented in the tree are then transformed into queries processed by existing search engines. Each returned page is rated according to user-specified preferences such as semantic relevance, syntactic relevance, categorical match, and page popularity. Experimental results indicate that WebSifter improves the precision of Web searches, thereby leading to better information.


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