Hybrid filtering in Semantic Query Processing




Jeong, Hanjo

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This dissertation presents a hybrid filtering method and a case-based reasoning framework for enhancing the effectiveness of Web search. Web search may not reflect user needs, intent, context, and preferences, because today’s keyword-based search is lacking semantic information to capture the user’s context and intent in posing the search query. Also, many users have difficulty in representing such intent and preferences in posing a semantic query due to lack of domain knowledge and different schemas used by data providers. This dissertation introduces a hybrid filtering method, query-to-query hybrid filtering, which combines semantic content-based filtering with collaborative filtering to refine user queries based not only on an active user’s search history, but also on other users’ search histories. Thus, previous search experience not only of an active user, but also of the other users is used to assist the active user in formulating a query. In addition, a case-based reasoning framework with Semantic Web technologies is introduced to systematically/semantically manage and reuse user search histories for query refinement. Finally, ontologies are used for the hybrid filtering to mine preferable content patterns based on semantic match rather than just a keyword match. Validation of the query-to-query hybrid filtering method is performed on the GroupLens movie data sets.



Hybrid filtering, Collaborative filtering, Content-based filtering, Semantic web, Information search, Ontology