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An Evaluation of Applying Knowledge Base to Academic Information Service

  • Lee, Seok-Hyoung (Department of Overseas Information, Korea Institute of Science and Technology Information, Department of Library and Information Science, Konkuk University) ;
  • Kim, Hwan-Min (Department of Overseas Information, Korea Institute of Science and Technology Information) ;
  • Choe, Ho-Seop (Department of New Museum Planning & Construction, National Museum of Contemporary)
  • Published : 2013.06.30

Abstract

Through a series of precise text handling processes, including automatic extraction of information from documents with knowledge from various fields, recognition of entity names, detection of core topics, analysis of the relations between the extracted information and topics, and automatic inference of new knowledge, the most efficient knowledge base of the relevant field is created, and plans to apply these to the information knowledge management and service are the core requirements necessary for intellectualization of information. In this paper, the knowledge base, which is a necessary core resource and comprehensive technology for intellectualization of science and technology information, is described and the usability of academic information services using it is evaluated. The knowledge base proposed in this article is an amalgamation of information expression and knowledge storage, composed of identifying code systems from terms to documents, by integrating terminologies, word intelligent networks, topic networks, classification systems, and authority data.

Keywords

References

  1. Arampatzis, A. T., T. Tsoris, & C.H.A. Koster. (1997). IRENA: Information Retrieval Engine based on Natural Language Analysis, In Proceedings of RIAO97 Computer Assisted Information Searching on Internet, 159-175.
  2. Brachman, Ronald J., & Levesque, Hector J. (2004). Knowledge Representation and Reasoning. Elsevier, Inc.
  3. Choe, Ho-Seop. (2006). Construction Method of Large-scale 'Urimal(Korean)-Word Intelligent Network'. Hangul, 273, 125-151.
  4. Choe, Ho-Seop, & Ok, Chul-Young. (2004). Information Retrieval System and Ontology. Communications of the Korean Institute of Information Scientists and Engineers, 22(4), 62-71.
  5. Evans, D., & Zhai, C. (1996). Noun-Phrase Analysis in Unrestircted Text for Information Retrieval. Proceedings of the 34th Annual meeting of Association for Computer Linguistics, 17-24.
  6. Im, Ji-Hui, Choi, Ho-Seop, Bae, Yeong-Jun, Ok, Cheol-Yeong, Choi, Seong-Pil, Seong, Won-Gyeong, & Park, Dong-In. (2005). Construction of immunology thesaurus and ontology. Annual Conference on Human and Language Technology, 2005, 21-27.
  7. Park, Jung-Oh, & Hwang, Do-Sam. (2000). A terminology extraction system. Proceedings of The 27th KISS Spring Conference 2002, 27(1-B), 381-383.
  8. Rowley, J., & R. Hartley. (2008). Organizing Knowledge: and Introduction to managing access to information. Burlington.VT: Ashgate.
  9. Seung, Hyon-Woo, & Park, Mi-Young. (2003). A clustering technique using association rules for the library and information science terminology. Journal of the Korean Society for Library and Information Science, 37(2), 89-105. https://doi.org/10.4275/KSLIS.2003.37.2.089
  10. What is CYC? Cyccorp, INC. [cited 2013.03.05].
  11. Zins, Chaim. (2007). Conceptual approaches for defining data, information, and knowledge. Journal of the American Society for Information Science and Technology, 58(4), 479-493. https://doi.org/10.1002/asi.20508

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  2. A Study on the Recognition of Users and Librarians of Obstructive Factors in Online Reference Services vol.50, pp.1, 2016, https://doi.org/10.4275/KSLIS.2016.50.1.133