Comparison and evaluation of approaches for automatic extraction of knowledge structure from learning materials학습자료에서의 지식구조 자동 추출 방식의 비교 및 검증

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dc.contributor.advisorYi, Mun-Yong-
dc.contributor.advisor이문용-
dc.contributor.authorJo, Eugene-
dc.contributor.author조유진-
dc.date.accessioned2015-04-23T06:46:32Z-
dc.date.available2015-04-23T06:46:32Z-
dc.date.issued2013-
dc.identifier.urihttp://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=567099&flag=dissertation-
dc.identifier.urihttp://hdl.handle.net/10203/197085-
dc.description학위논문(석사) - 한국과학기술원 : 지식서비스공학과, 2013.8, [ vi, 37 p. ]-
dc.description.abstractGiven a semi-structured learning material, can we infer which keywords are important and how they are connected? We formalize this question as the automatic extraction of knowledge structure and develop approaches based on measures for analyzing the relationship of keywords in the learning material. The amount of learning materials increases exponentially owing to the advance of the IT technology, and the size of the online education market gets larger because of the prevalence of the Internet. However, many people experience difficulties in exactly finding the proper learning material for them. The extraction of knowledge structure can facilitate this task since it finds or recommends proper learning materials in terms of knowledge structure. Along this direction, location based approaches have been widely used. However, most learning materials are semi-structured so that it is hard to find exact semantic separation. There are two major contributions in this thesis: (a) We use SVM(Singular Vector Decomposition) and a flexible edit distance matching algorithm to find semantic separation of leaning materials and compare proposed methods and location based methods (b) We conduct user study with domain experts for about a hundred learning materials to measure actual expert’s knowledge structure. Results demonstrate 20% superior performance of the proposed methods over the location based methods.eng
dc.languageeng-
dc.publisher한국과학기술원-
dc.subjectKnowledge Structure-
dc.subject패스파인터네트워크-
dc.subject추천시스템-
dc.subject키워드추출-
dc.subject지식표현-
dc.subject지식구조-
dc.subjectKnowledge Representation-
dc.subjectKeyword Extraction-
dc.subjectRecommender System-
dc.subjectPathfinder Network-
dc.titleComparison and evaluation of approaches for automatic extraction of knowledge structure from learning materials-
dc.title.alternative학습자료에서의 지식구조 자동 추출 방식의 비교 및 검증-
dc.typeThesis(Master)-
dc.identifier.CNRN567099/325007 -
dc.description.department한국과학기술원 : 지식서비스공학과, -
dc.identifier.uid020113607-
dc.contributor.localauthorYi, Mun-Yong-
dc.contributor.localauthor이문용-
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