Hybrid filtering in Semantic Query Processing

dc.contributor.advisorKerschberg, Larry
dc.contributor.authorJeong, Hanjo
dc.creatorJeong, Hanjo
dc.date2011-03-08
dc.date.accessioned2011-05-12T15:49:08Z
dc.date.availableNO_RESTRICTION
dc.date.available2011-05-12T15:49:08Z
dc.date.issued2011-05-12
dc.description.abstractThis 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.
dc.identifier.urihttps://hdl.handle.net/1920/6313
dc.language.isoen_US
dc.subjectHybrid filtering
dc.subjectCollaborative filtering
dc.subjectContent-based filtering
dc.subjectSemantic web
dc.subjectInformation search
dc.subjectOntology
dc.titleHybrid filtering in Semantic Query Processing
dc.typeDissertation
thesis.degree.disciplineInformation Technology
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral
thesis.degree.namePhD in Information Technology

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Jeong_dissertation_2011.pdf
Size:
4.04 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.65 KB
Format:
Item-specific license agreed upon to submission
Description: