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dc.contributor.authorWu, Zixin
dc.date.accessioned2014-03-04T02:49:00Z
dc.date.available2014-03-04T02:49:00Z
dc.date.issued2007-08
dc.identifier.otherwu_zixin_200708_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/wu_zixin_200708_ms
dc.identifier.urihttp://hdl.handle.net/10724/24329
dc.description.abstractTagging communities is a featured Web 2.0 phenomenon, where users describe a Web resource by using keywords (called tags). This behavior can be viewed as cooperative meta-data extraction and annotation. While these tagging communities become more and more popular, their information retrieval mechanism is still keyword based, thus it is difficult to achieve high precision and recall rates because of word ambiguity and lack of semantics. In this thesis, we combine the approaches of folksonomy and ontology to improve recall rate and ranking of query results. We index Web resources by the meanings of tags instead of strings, and we provide context-aware multi-ontologies semantic search capability which utilizes relationships in ontologies. The evaluations performed on a subset of Flickr’s photos indicate that our users spent significantly less time and effort in finding what they want on our system than on Google Desktop on the same datasets.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectFolksonomy
dc.subjectOntology
dc.subjectSemantic Web
dc.subjectSemantic Query
dc.subjectInformation Retrieval
dc.subjectPrecision
dc.subjectRecall
dc.subjectSynonym
dc.subjectPolysemy
dc.subjectTF-IDF
dc.subjectContext
dc.titleTagsense
dc.title.alternativemarrying folksonomy and ontology
dc.typeThesis
dc.description.degreeMS
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorAmit P. Sheth
dc.description.committeeAmit P. Sheth
dc.description.committeePrashant Doshi
dc.description.committeeJohn A. Miller


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