Abstract:
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.
