Journal of the Korean Society for information Management (정보관리학회지)

Korean Society for Information Management (한국정보관리학회)
권호 표지
DOI QR코드

DOI QR Code

Introducing Keyword Bibliographic Coupling Analysis (KBCA) for Identifying the Intellectual Structure

지적구조 규명을 위한 키워드서지결합분석 기법에 관한 연구

  • 이재윤 (명지대학교 인문대학 문헌정보학과) ;
  • 정은경 (이화여자대학교 사회과학대학 문헌정보학과)
  • Received : 2022.02.24
  • Accepted : 2022.03.10
  • Published : 2022.03.30
Download PDF
⟨ Previous Next ⟩

Abstract

Intellectual structure analysis, which quantitatively identifies the structure, characteristics, and sub-domains of fields, has rapidly increased in recent years. Analysis techniques traditionally used to conduct intellectual structure analysis research include bibliographic coupling analysis, co-citation analysis, co-occurrence analysis, and author bibliographic coupling analysis. This study proposes a novel intellectual structure analysis method, Keyword Bibliographic Coupling Analysis (KBCA). The Keyword Bibliographic Coupling Analysis (KBCA) is a variation of the author bibliographic coupling analysis, which targets keywords instead of authors. It calculates the number of references shared by two keywords to the degree of coupling between the two keywords. A set of 1,366 articles in the field of 'Open Data' searched in the Web of Science were collected using the proposed KBCA technique. A total of 63 keywords that appeared more than 7 times, extracted from 1,366 article sets, were selected as core keywords in the open data field. The intellectual structure presented by the KBCA technique with 63 key keywords identified the main areas of open government and open science and 10 sub-areas. On the other hand, the intellectual structure network of co-occurrence word analysis was found to be insufficient in the overall structure and detailed domain structure. This result can be considered because the KBCA sufficiently measures the relationship between keywords using the degree of bibliographic coupling.

학문의 구조, 특성, 하위 분야 등을 계량적으로 규명하는 지적구조 분석 연구가 최근 급격히 증가하는 추세이다. 지적구조 분석 연구를 수행하기 위하여 전통적으로 사용되는 분석기법은 서지결합분석, 동시인용분석, 단어동시출현분석, 저자서지결합분석 등이다. 이 연구의 목적은 키워드서지결합분석(KBCA, Keyword Bibliographic Coupling Analysis)을 새로운 지적구조 분석 방식으로 제안하고자 한다. 키워드서지결합분석 기법은 저자서지결합분석의 변형으로 저자 대신에 키워드를 표지로 하여 키워드가 공유한 참고문헌의 수를 두 키워드의 주제적 결합 정도로 산정한다. 제안된 키워드서지결합분석 기법을 사용하여 Web of Science에서 검색된 'Open Data' 분야의 1,366건의 논문집합을 대상으로 분석하였다. 1,366건의 논문집합에서 추출된 7회 이상 출현한 63종의 키워드를 오픈데이터 분야의 핵심 키워드로 선정하였다. 63종의 핵심 키워드를 대상으로 키워드서지결합분석 기법으로 제시된 지적구조는 열린정부와 오픈사이언스라는 주된 영역과 10개의 소주제로 규명되었다. 이에 반해 단어동시출현분석의 지적구조 네트워크는 전체 구성과 세부 영역 구조 규명에 있어 미진한 것으로 나타났다. 이러한 결과는 키워드서지결합분석이 키워드 간의 서지결합도를 사용하여 키워드 간의 관계를 풍부하게 측정하기 때문이라고 볼 수 있다.

Keywords

References

  1. Byun, Ji-Hye & Chung, Eun-Kyung (2011). Domain analysis on electrical engineering in Korea by author bibliographic coupling analysis. Journal of Information Science Theory and Practice, 42(4), 75-94. https://doi.org/10.1633/JIM.2011.42.4.075
  2. Choi, Hyung Wook & Chung, Eunkyung (2017). An investigation on characteristics and intellectual structure of sociology by analyzing cited data. Journal of the Korean Society for Information Management, 34(3), 109-124. http://doi.org/10.3743/KOSIM.2017.34.3.109
  3. Choi, Hyung Wook, Choi, Ye Jin, & Nam, So-Yeon (2018). Time series analysis of intellectual structure and research trend changes in the field of library and information science: 2003 to 2017. Journal of the Korean Society for Information Management, 35(2), 89-114. http://doi.org/10.3743/KOSIM.2018.35.2.08
  4. Choi, Ye-Jin & Chung, Yeon-Kyoung (2016). A study on the intellectual structure of metadata research by using co-word analysis. Journal of the Korean Society for Information Management, 33(3), 63-83. https://doi.org/10.3743/KOSIM.2016.33.3.063
  5. Chung, Eunkyung (2021). An investigation on digital humanities research trend by analyzing the papers of Digital Humanities Conferences. Journal of the Korean Society for Library and Information Science, 55(1), 393-413. https://doi.org/10.4275/KSLIS.2021.55.1.393
  6. Kang, Beomil & Park, Ji-Hong (2013). Profiling and co-word analysis of teaching Korean as a foreign language domain. Journal of the Korean Society for Information Management, 30(4), 195-213. http://doi.org/10.3743/KOSIM.2013.30.4.195
  7. Kim, Heejeon & Cho, Hyun Yang (2010). A study on intellectual structure using author co-citation analysis and author bibliographic coupling analysis in the field of social welfare science. Journal of the Korean Society for Information Management, 27(3), 283-306. http://doi.org/10.3743/KOSIM.2010.27.3.283
  8. Kim, Sun-Kyum, Kim, Wan-Jong, Seo, Tae-Sul, & Choi, Hyun-Jin Choi (2019). Domain analysis on the field of open access by co-word analysis: based on published journals of library and information science during 2013 to 2018. Journal of Korean Library and Information Science Society, 50(1), 333-356. http://doi.org/10.16981/kliss.50.1.201903.333
  9. Lee, Jae Yun (2006a). Towards a new method for examining current domestic intellectual structure of knowledge domains. Proceedings of the 13th Annual Conference of the Korean Society for Information Management, 145-152.
  10. Lee, Jae Yun (2006b). A novel clustering method for examining and analyzing the intellectual structure of a scholarly field. Journal of the Korean Society for Information Management, 23(4), 215-231. https://doi.org/10.3743/KOSIM.2006.23.4.215
  11. Lee, Jae Yun (2008). Bibliographic author coupling analysis: a new methodological approach for identifying research trends. Journal of the Korean Society for Information Management, 25(1), 173-190. http://doi.org/10.3743/KOSIM.2008.25.1.173
  12. Lee, Jae Yun (2013). A comparison study on the weighted network centrality measures of tnet and WNET. Journal of the Korean Society for Information Management, 30(4), 241-264. http://doi.org/10.3743/KOSIM.2013.30.4.241
  13. Lee, Jae Yun (2021). Considerations on some decision criteria in the intellectual structure analysis process. Proceedings of the 28th Annual Conference of the Korean Society for Information Management, 91-100.
  14. Lee, Ji-Won (2019). A study on analysis of research trends and intellectual structure of cataloging field. Journal of the Korean Society for Information Management, 36(4), 279-300. http://doi.org/10.3743/kosim.pub.36.4.279001
  15. Park, Ji Yeon & Jeong, Dong Youl (2013). A study on the Intellectual structure of library and information science in Korea by author bibliographic coupling analysis. Journal of the Korean Society for Information Management, 30(4), 31-59. http://doi.org/10.3743/KOSIM.2013.30.4.031
  16. Park, SeonHee, Kang, ChangWook, & Yang, HyunKieu (2017). An analysis on the intellectual structure of studies related to the hearing impairments in Korea: focusing on co-word analysis. Journal of Special Education, 24(1), 167-187. http://doi.org/10.34249/jse.2017.24.1.167
  17. Seo, SunKyung & Chung, EunKyung (2013). Domain analysis on the field of open access by co-word analysis. Journal of the Korean Biblia Society for Library and Information Science, 24(1), 207-228. https://doi.org/10.14699/kbiblia.2013.24.1.207
  18. Shin, You Mi & Park, Oknam (2019). An analytical study on research trends of collection development and management. Journal of the Korean Society for Information Management, 36(2), 105-131. http://doi.org/10.3743/KOSIM.2019.36.2.105
  19. Zhang, Ling Ling & Hong, Hyun Jin (2014). Examining the intellectual structure of reading studies with co-word analysis based on the importance of journals and sequence of keywords. Journal of the Korean Biblia Society for Library and Information Science, 25(1), 295-318. https://doi.org/10.14699/kbiblia.2014.25.1.295
  20. Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart cities: definitions, dimensions, performance, and initiatives. Journal of Urban Technology, 22(1), 3-21. https://doi.org/10.1080/10630732.2014.942092
  21. Barabasi, A.-L. (2002). Linked: The New Science of Networks. Cambridge, Mass.: Perseus Pub.
  22. Berners-Lee, T. (2006). Linked Data. Available: https://www.w3.org/DesignIssues/LinkedData.html
  23. Bertot, J. C., Jaeger, P. T., & Grimes, J. M. (2010). Using ICTs to create a culture of transparency: e-government and social media as openness and anti-corruption tools for societies. Government Information Quarterly, 27(3), 264-271. https://doi.org/10.1016/j.giq.2010.03.001
  24. Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (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
  25. Chen, C. (2017). Science mapping: a systematic review of the literature. Journal of Data and Information Science, 2(2).
  26. Cortes, C. & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297. https://doi.org/10.1023/A:1022627411411
  27. Haklay, M. (2010). How good is volunteered geographical information? a comparative study of OpenStreetMap and Ordnance Survey Datasets. Environment and Planning B: Planning and Design, 37(4), 682-703. https://doi.org/10.1068/b35097
  28. He, J., Lou, W., & Li, K. (2019). How were science mapping tools applied? the application of science mapping tools in LIS and non-LIS domains. Proceedings of the Association for Information Science & Technology, 56(1), 404-408. https://doi.org/10.1002/pra2.38
  29. Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258-268. https://doi.org/10.1080/10580530.2012.716740
  30. Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14(1), 10-25. https://doi.org/10.1002/asi.5090140103.
  31. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. Communications of the ACM, 60(6), 84-90. https://doi.org/10.1145/3065386
  32. Nativi, S., Craglia, M., & Pearlman, J. (2012). The brokering approach for multidisciplinary interoperability: a position paper. International Journal of Spatial Data Infrastructures Research, 7, 1-15. Available: https://ijsdir.sadl.kuleuven.be/index.php/ijsdir/article/view/281
  33. Parasie, S. & Dagiral, E. (2013). Data-driven journalism and the public good: "computer-assistedreporters" and "programmer-journalists" in Chicago. New Media & Society, 15(6), 853-871. https://doi.org/10.1177/1461444812463345
  34. Ronda-Pupo, G. A. & Guerras-Martin, L. A. (2012). Dynamics of the evolution of the strategy concept 1962-2008: a co-word analysis. Strategic Management Journal, 33(2), 162-188. https://doi.org/10.1002/smj.948
  35. Schvaneveldt, R. W. (1990). Pathfinder Associative Networks: Studies in Knowledge Organization. Norwood, NJ: Ablex.
  36. Silvertown, J. (2009). A new dawn for citizen science. Trends in Ecology & Evolution, 24(9), 467-471. https://doi.org/10.1016/j.tree.2009.03.017
  37. Small, H. (1973). Co-citation in the scientific literature: a new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. https://doi.org/10.1002/asi.4630240406.
  38. Small, H. G. (1976). Structural dynamics of scientific literature. International Classification, 3(2), 67-74.
  39. van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579-2605. Available: https://www.jmlr.org/papers/v9/vandermaaten08a.html
  40. van Raan A. (2019). Measuring Science: Basic Principles and Application of Advanced Bibliometrics. In: Glanzel W., Moed H. F., Schmoch U., Thelwall M. (eds), Springer Handbook of Science and Technology Indicators. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-030-02511-3_10
  41. White, H. D. & Griffith, B. C. (1981). Author cocitation: a literature measure of intellectual structure. Journal of the American Society for Information Science, 32(3), 163-171. https://doi.org/10.1002/asi.4630320302
  42. Wilkinson, M., Dumontier, M., Aalbersberg, I., Appleton, G., Axton, M., Baak, A., ... & Mons, B. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. https://doi.org/10.1038/sdata.2016.18
  43. Zhao, D. & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996-2005: introducing author bibliographic-coupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070-2086. https://doi.org/10.1002/asi.20910