DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Lee, Yoon-Joon | - |
dc.contributor.advisor | 이윤준 | - |
dc.contributor.author | Kim, Hang-Kyu | - |
dc.contributor.author | 김항규 | - |
dc.date.accessioned | 2011-12-13T05:27:54Z | - |
dc.date.available | 2011-12-13T05:27:54Z | - |
dc.date.issued | 2011 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=466469&flag=dissertation | - |
dc.identifier.uri | http://hdl.handle.net/10203/33329 | - |
dc.description | 학위논문(박사) - 한국과학기술원 : 전산학과, 2011.2, [ vi, 70 p. ] | - |
dc.description.abstract | Semantic Web was proposed by Tim Berners Lee as an alternative to current Web. Keyword search approaches for complex data such as relational database, XML, and graph data, have been spotlighted to stand for complex query languages. A keyword search approach in Semantic Web is defined as semantic search. Providing as results semantically relevant objects as well as objects containing the given query terms, semantic search is different from current document search and proposed to overcome the limitations of current document search. Observing the literatures of semantic search, it is composed of three phases: keyword matching, semantic expansion, and resource mapping. Keyword matching is a phase to collect an initial set for the following phases, retrieving the objects directly related to the keyword terms entered by users. Semantic expansion is a phase distinguishing semantic search from current document search, adding the semantically related objects to the initial set constructed in keyword matching phase. In resource mapping phase, the actual resources are returned to the user based on the result set of semantic expansion. Relevance measuring methods, one approach of IR techniques, which scores relevance values between keyword terms and objects, are used to be exploited in keyword matching of semantic search. There are two main approaches to exploit IR techniques in keyword matching. The first approach is to merge all the texts linked to an object, searches through the merged texts, and returns the linked object. Though the approach is simple to exploit IR techniques in keyword matching, it returns unwanted results or ranks less relevant objects highly ignoring the semantics between texts and the linked object. The second previous approach for keyword matching searches texts independently without merging process, and returns the linked objects. Although the method preserves the semantics between a text and the linked object, the result does not include or rank... | eng |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | semantic expansion | - |
dc.subject | keyword matching | - |
dc.subject | object node oriented | - |
dc.subject | semantic search | - |
dc.subject | URI compression | - |
dc.subject | URI 압축 | - |
dc.subject | 시맨틱확장 | - |
dc.subject | 키워드매칭 | - |
dc.subject | 객체노드기반 | - |
dc.subject | 시맨틱검색 | - |
dc.title | (An) efficient searching method for object nodes in ontology to improve semantic search | - |
dc.title.alternative | 시맨틱 검색 향상을 위한 온톨로지 내의 효율적인 객체 노드 검색 기법 | - |
dc.type | Thesis(Ph.D) | - |
dc.identifier.CNRN | 466469/325007 | - |
dc.description.department | 한국과학기술원 : 전산학과, | - |
dc.identifier.uid | 020037178 | - |
dc.contributor.localauthor | Lee, Yoon-Joon | - |
dc.contributor.localauthor | 이윤준 | - |
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