Detecting the intellectual structure of library and information science based on formal concept analysis
- 673 Downloads
Detecting intellectual structure of a knowledge domain is valuable to track the dynamics of scientific research. Formal concept analysis (FCA) provides a new perspective for knowledge discovery and data mining. In this paper we introduce a FCA-based approach to detect intellectual structure of library and information science (LIS). Our approach relies on the mathematical theory which formulates the understanding of “concept” as a unit of extension (scholars) and intension (keywords) as a way of modelling the intellectual structure of a domain. By analyzing the papers published in sixteen prominent journals of LIS domain from 2001 to 2013, the intellectual structure of LIS in the new century has been identified and visualized. Nine major research themes of LIS were detected together with the core keywords and authors to describe each theme. The significant advantage of our approach is that the mathematical formulae produce a conceptual structure which automatically provides generalization and specialization relationships among the concepts. This provides additional information not available from other methods, especially when shared interests of authors from different granularities are also visualized in concept lattice.
KeywordsFormal concept analysis Intellectual structure Library and information science
This project is supported by National Natural Science Foundation of China (71203164) and the Fundamental Research Funds for the Central Universities (2012GSP058, Wuhan University).
- Bagley, P. R. (1968). Extent of programming language concepts. University City Science Center technical report, November 1968.Google Scholar
- Ganter, B., & Wille, R. (2012). Formal concept analysis: Mathematical foundations[M] (p. 2012). Berlin: Springer.Google Scholar
- Li, H. Y., Cui, L., Cui, M., & Tong, Y. Y. (2010). Active research fields of acupuncture research: A document co-citation clustering analysis of acupuncture literature. Alternative Therapies in Health and Medicine, 16(6), 38–45.Google Scholar
- Liu, Z. H., & Zhang, Z. Q. (2010). Author keyword coupling analysis: An empirical research. Journal of the China Society for Scientific and Technical Information, 29(2), 268–275. (in Chinese).Google Scholar
- Moya-Anégon, F., Herrero-Solana, V., & Jimenez-Contreras, E. (2006). A connectionist and multivariate approach to science maps: The SOM, clustering and MDS applied to library and information science research. Journal of Information Science, 32(1), 63–77. doi: 10.1177/0165551506059226.CrossRefGoogle Scholar
- Nicolai, J. F., & Torben, P. (2003). The MNC as knowledge structure: The roles of knowledge sources and organizational instruments in MNC knowledge management. Danish Research Unit for Industrial Dynamics, 2003(5), 1–33.Google Scholar
- White, H. D., & McCain, K. W. (1998). Visualizing a discipline: An author co-citation analysis of information science, 1972–1995. Journal of the American society for information science, 49(4), 327–355. doi: 10.1002/(SICI)1097-4571(19980401)49:4<327:AID-ASI4>3.0.CO;2-W.Google Scholar
- Wille, R. (1982). Restructuring lattice theory: An approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered sets (pp. 445–470). Dordrecht (Vol. 83 of NATO Advanced Studies Institute), Boston: Reidel.Google Scholar
- Zhao, D., & Strotmann, A. (2008a). 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. doi: 10.1002/asi.20910.CrossRefGoogle Scholar
- Zhu, Q. H., & Li, L. (2008). Social network analysis and the application achievement of SNA in information science. Information Studies: Theory and Application, 31(2), 179–183. (in Chinese).Google Scholar