Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/4506
Title: Domain specific ontology based semantic knowledge representation for efficient intelligent information retrieval
Researcher: Goudarm, Rayangouda
Guide(s): Sreenivasa Rao, M
Keywords: Web Intelligence
World Wide Web
Meta Data
Ontologies
Inference Engines
RDF
OWL
Indexing Web Documents
Semantic Meta Data
Upload Date: 3-Sep-2012
University: Jawaharlal Nehru Technological University
Completed Date: February 2012
Abstract: solutions. 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.
Pagination: xv, 140p.
URI: http://hdl.handle.net/10603/4506
Appears in Departments:Faculty of Computer Science & Engineering

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01_title.pdfAttached File71.23 kBAdobe PDFView/Open
02_certificate.pdf74.07 kBAdobe PDFView/Open
03_declaration.pdf72.37 kBAdobe PDFView/Open
04_acknowledgements.pdf59.09 kBAdobe PDFView/Open
05_abstract.pdf137.81 kBAdobe PDFView/Open
06_table of contents.pdf126.09 kBAdobe PDFView/Open
07_list of tables.pdf105.72 kBAdobe PDFView/Open
08_list of figures.pdf112.97 kBAdobe PDFView/Open
09_abbreviations.pdf117.62 kBAdobe PDFView/Open
10_chapter 1.pdf207.88 kBAdobe PDFView/Open
11_chapter 2.pdf340.35 kBAdobe PDFView/Open
12_chapter 3.pdf540.56 kBAdobe PDFView/Open
13_chapter 4.pdf466.95 kBAdobe PDFView/Open
14_chapter 5.pdf263.19 kBAdobe PDFView/Open
15_chapter 6.pdf110.71 kBAdobe PDFView/Open
16_references.pdf192.52 kBAdobe PDFView/Open


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