Building Hierarchical Knowledge Base of Research Interests and Learning Topics for Social Computing Support

소셜 컴퓨팅을 위한 연구·학습 주제의 계층적 지식기반 구축

  • 김선호 (한국과학기술정보연구원) ;
  • 김강회 (한국과학기술정보연구원) ;
  • 여운동 (한국과학기술정보연구원)
  • Received : 2012.10.22
  • Accepted : 2012.11.29
  • Published : 2012.12.28


This paper consists of two parts: In the first part, we describe our work to build hierarchical knowledge base of digital library patron's research interests and learning topics in various scholarly areas through analyzing well classified Electronic Theses and Dissertations (ETDs) of NDLTD Union catalog. Journal articles from ACM Transactions and conference web sites of computing areas also are added in the analysis to specialize computing fields. This hierarchical knowledge base would be a useful tool for many social computing and information service applications, such as personalization, recommender system, text mining, technology opportunity mining, information visualization, and so on. In the second part, we compare four grouping algorithms to select best one for our data mining researches by testing each one with the hierarchical knowledge base we described in the first part. From these two studies, we intent to show traditional verification methods for social community miming researches, based on interviewing and answering questionnaires, which are expensive, slow, and privacy threatening, can be replaced with systematic, consistent, fast, and privacy protecting methods by using our suggested hierarchical knowledge base.


Hierarchical Knowledge Base;Research Interests;Learning Topics;Classification;Social Computing


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