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Descriptor Profiling for Research Domain Analysis

연구영역분석을 위한 디스크립터 프로파일링에 관한 연구

  • Published : 2007.12.31

Abstract

This study aims to explore a new technique making complementary linkage between controlled vocabularies and uncontrolled vocabularies for analyzing a research domain. Co-word analysis can be largely divided into two based on the types of vocabulary used: controlled and uncontrolled. In the case of using controlled vocabulary, data sparseness and indexer effect are inherent drawbacks. On the other case, word selection by the author's perspective and word ambiguity. To complement each other, we suggest a descriptor profiling that represents descriptors(controlled vocabulary) as the co-occurrence with words from the text(uncontrolled vocabulary). Applying the profiling to the domain of information science implies that this method can complement each other by reducing the inherent shortcoming of the controlled and uncontrolled vocabulary.

본 연구는 연구 영역 분석을 위하여 통제어휘와 비통제어휘를 연계해서 사용하는 새로운 방법을 모색하기 위한 것이다. 동시출현단어분석은 크게 통제어휘와 비통제어휘를 사용하는 경우의 두 가지 유형으로 구분할 수 있는데, 통제어휘를 사용할 경우에는 자료 희귀성 및 색인자 효과가 단점이며, 비통제어휘를 사용할 경우에는 저자의 주관에 따른 단어 선택 및 단어의 중의성이 문제가 된다. 이 연구에서는 양자를 보완할 수 있는 방법으로, 통제어휘인 디스크립터를 비통제어휘인 단어와의 동시출현 정보로 표현하는 디스크립터 프로파일링을 제안하였다. 정보학분야에 적용해본 결과, 디스크립터 프로파일링은 특정 영역의 최신 동향을 파악하는데 있어 통제어휘와 비통제어휘가 갖는 본질적인 문제점을 어느 정도 보완할 수 있는 것으로 나타났다.

Keywords

References

  1. 김판준. 2006. 로치오 알고리즘을 이용한 학술지 논문의 디스크립터 자동부여에 관한 연구. 정보관리학회지, 23(3): 69-90 https://doi.org/10.3743/KOSIM.2006.23.3.069
  2. 이재윤. 2005. 계량서지적 동시출현 분석에 관한 방법론적 고찰. 한국과학기술 정보연구원 내부세미나 발표자료
  3. 이재윤. 2007. 클러스터링 기반 네트워크 생성 알고리즘. 제14회 한국정보관리학회 학술대회 논문집, 147-154
  4. Bhattacharya, Sujit, and Prajit K. Basu. 1998.' Mapping a research area at the micro level using co-word analysis.' Sciento metrics, 43(3): 359-372 https://doi.org/10.1007/BF02457404
  5. Buter, R.K. and E.C.M. Noyons. 2002. 'Using bibliometric maps to visualize term distribution in scientific papers.' In IEEE Proceedings of the 6th International Conference on Information Visualisation, London, July 2002, pp. 697-705
  6. Callon, M., J. Law, and A. Rip.1986. Mapping the Dynamics of Science and Technology: Sociology of Science in the Real World. London: The Macmillan Press Ltd
  7. Callon, M., Jean-Pierre Courtial, W. A. Turner, and S. Bauin. 1983. 'From translations to problematic networks: an introduction to co-word analysis.' Social Science Information, 22(2): 191-235 https://doi.org/10.1177/053901883022002003
  8. Courtial, J.-P., M. Callon, and M. Sigogneau. 1984. 'Is indexing trustworthy? Classification of articles through co-word analys is.' Journal of Information Science, 9: 47-56 https://doi.org/10.1177/016555158400900201
  9. de Nooy, W., A. Mrvar, and V. Batagelj. 2005. Exploratory Social Network Analysis with Pajek. New York: Cambridge University Press
  10. Ding, Y., Gobinda G. Chowdhury, and Schubert Foo. 2001'. Bibliometric cartography of information retrieval research by using coword analysis.' Information Processing & Management, 37(6): 817-842 https://doi.org/10.1016/S0306-4573(00)00051-0
  11. He, Qin. 1999. 'Knowledge discovery through co-word analysis.' Library Trends, 48(1): 133-159
  12. Jacobs, N. 2002. 'Co-term network analysis as a means of describing the information landscapes of knowledge communities across sectors.' Journal of Documentation, 58(5): 548-562 https://doi.org/10.1108/00220410210441577
  13. Jassens, F., J. Leta, W. Glazel, and B. DeMoor. 2006. 'Towards mapping library and information science.' Information Processing & Management, 42(6): 1614-1642 https://doi.org/10.1016/j.ipm.2006.03.025
  14. Kostoff, R. N. 1993. 'Database tomography for technical intelligence.'Competitive Intelligence Review, 4(1): 38?43 https://doi.org/10.1002/cir.3880040109
  15. Kostoff, R. N., R. Tshiteya, K. M. Pfeil, J. Humenik, and G. Karipis. 2005. 'Power source road maps using bibliometrics and database tomography.' Energy, 30(5): 709-730 https://doi.org/10.1016/j.energy.2004.04.058
  16. McCain, Katherine W. 1995. 'R & D themes in information science : Apreliminary co-descriptor analysis.' In M. Koenig and A. Bookstein (eds.), Proceedings of the Fifth Biennial Conference of the International Society for Scientometrics and Informetrics, Rosary College, Pine Forest, IL, June 7-10, 1995, (Medford, N.J.: Learned Information, 1995), pp. 275?282
  17. Noyons, E.C.M., and A.F.J. van Raan. 1998. 'Monitoring scientific developments from a dynamic perspective: self - organizied structuring to map neural network research.' JASIS, 49(1): 68-81
  18. Porter, Alan L., and Scott W. Cunningham. 2005. Tech Mining: Exploiting New Technologies for Competitive Advantage. Hoboken, NJ: John Wiley & Sons, Inc
  19. Spasser, M. A. 1997. 'Mapping the terrain of pharmacy: co-classification analysis of the international pharmaceutical abstracts database.' Scientometrics, 39(1): 77-97 https://doi.org/10.1007/BF02457431
  20. Tijssen, R.J.W. 1992.' A Quan titative assessment of interdisciplinary structures in science and technology: co-classification analysis of energy research.' Research Policy, 21: 27-44 https://doi.org/10.1016/0048-7333(92)90025-Y
  21. Todorov, R. 1989. 'Co-classification analysis for science mapping : an example from superconductivity.' In A. F. J. van Raan, A. J. Nederhof, and H. F. Moed. (eds.) Science and Technology Indicators. Leiden: DSOW Pr., University of Leiden, pp. 263-270
  22. Todorov, R., and M. Winterhager. 1990. 'Mapping Australian geophysics : a co-heading analysis.' Scientometrics, 19(1-2): 35-56 https://doi.org/10.1007/BF02130464
  23. Voutilainen, A. 1993. ' NPtool. adetector of English noun phrases.'In Proceedings of the workshop on very large corpora, Columbus Ohio, Ohio University, June 1993
  24. Watts, R. J., A, Porter, and B. Minsk. 2004.' Automated text mining comparison of Japanese and USA multi-robot research.' Ed. by A. Zanasi, N. Ebcken & C. Brebbia. Data Mining. WIT Press, pp. 61-74
  25. Widhalm, Clemens, Ute Gigler, Alexander Kopcsa, and Edgar Schiebel. 2001'. Co-occurrence and knowledge mapping to identify hot topics and key players in the field of mobility and transport.' In Proceedings of the 8th Conference of the International Society for Scientometrics and Informetrics, Sydney, July 2001, pp. 751- 758

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