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 |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 71.23 kB | Adobe PDF | View/Open |
02_certificate.pdf | 74.07 kB | Adobe PDF | View/Open | |
03_declaration.pdf | 72.37 kB | Adobe PDF | View/Open | |
04_acknowledgements.pdf | 59.09 kB | Adobe PDF | View/Open | |
05_abstract.pdf | 137.81 kB | Adobe PDF | View/Open | |
06_table of contents.pdf | 126.09 kB | Adobe PDF | View/Open | |
07_list of tables.pdf | 105.72 kB | Adobe PDF | View/Open | |
08_list of figures.pdf | 112.97 kB | Adobe PDF | View/Open | |
09_abbreviations.pdf | 117.62 kB | Adobe PDF | View/Open | |
10_chapter 1.pdf | 207.88 kB | Adobe PDF | View/Open | |
11_chapter 2.pdf | 340.35 kB | Adobe PDF | View/Open | |
12_chapter 3.pdf | 540.56 kB | Adobe PDF | View/Open | |
13_chapter 4.pdf | 466.95 kB | Adobe PDF | View/Open | |
14_chapter 5.pdf | 263.19 kB | Adobe PDF | View/Open | |
15_chapter 6.pdf | 110.71 kB | Adobe PDF | View/Open | |
16_references.pdf | 192.52 kB | Adobe PDF | View/Open |
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