Domain specific ontology based semantic knowledge representation for efficient intelligent information retrieval

dc.contributor.guideSreenivasa Rao, Men_US
dc.coverage.spatialComputer Scienceen_US
dc.creator.researcherGoudarm, Rayangoudaen_US
dc.date.accessioned2012-09-03T08:37:31Z
dc.date.available2012-09-03T08:37:31Z
dc.date.awarded2012en_US
dc.date.completedFebruary 2012en_US
dc.date.issued2012-09-03
dc.date.registeredn.d.en_US
dc.description.abstractsolutions. When specifying a search, users enter a small number of terms in the query. Yet the query describes the information need and is commonly based on the words that people expect to occur in the types of document they seek. This gives rise to a fundamental problem, in that not all documents will use the same words to refer to the same concept. Therefore, not all the documents that discuss the concept will be retrieved by a simple keyword-based search.Search engines today are based on decades old technology patched with new Furthermore, query terms may of course have multiple meanings (query term polysemy). As conventional search engines cannot interpret the sense of the user?s search, the ambiguity of the query leads to the retrieval of irrelevant information. Converse to the problem of polysemy, is the fact that conventional search engines that match query terms against a keyword based index will fail to match relevant information when the keywords used in the query are different from those used in the index, despite having the same meaning (index term synonymy). Although this problem can be overcome to some extent through thesaurus-based expansion of the query, the resultant increased level of document recall may result in the search engine returning too many results for the user to be able to process realistically. In addition to aninability to handle synonymy and polysemy, conventional search engines are unaware of any other semantic links between concepts.Many search engines fail to take into consideration aspects of the user?s context to help disambiguate their queries. User context would include information such as a person?s role, department, experience, interests, project work etc.The results returned from a conventional search engine are usually presented to the user as a simple ranked list. The sheer number of results returned from a basic keyword search means that results navigation can be difficult and time consuming.en_US
dc.description.noteBibliography p.131-140en_US
dc.format.accompanyingmaterialNoneen_US
dc.format.dimensions-en_US
dc.format.extentxv, 140p.en_US
dc.identifier.urihttp://hdl.handle.net/10603/4506
dc.languageEnglishen_US
dc.publisher.institutionFaculty of Computer Science and Engineeringen_US
dc.publisher.placeKukatpallyen_US
dc.publisher.universityJawaharlal Nehru Technological Universityen_US
dc.relation74en_US
dc.rightsuniversityen_US
dc.source.inflibnetINFLIBNETen_US
dc.subject.keywordWeb Intelligenceen_US
dc.subject.keywordWorld Wide Weben_US
dc.subject.keywordMeta Dataen_US
dc.subject.keywordOntologiesen_US
dc.subject.keywordInference Enginesen_US
dc.subject.keywordRDFen_US
dc.subject.keywordOWLen_US
dc.subject.keywordIndexing Web Documentsen_US
dc.subject.keywordSemantic Meta Dataen_US
dc.titleDomain specific ontology based semantic knowledge representation for efficient intelligent information retrievalen_US
dc.title.alternative-en_US
dc.type.degreePh.D.en_US

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