22-09-2012, 01:58 PM
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 Webbased
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.