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Scientometrics

, Volume 104, Issue 3, pp 737–762 | Cite as

Detecting the intellectual structure of library and information science based on formal concept analysis

  • Ping Liu
  • Qiong Wu
  • Xiangming Mu
  • Kaipeng Yu
  • Yiting Guo
Article

Abstract

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.

Keywords

Formal concept analysis Intellectual structure Library and information science 

Notes

Acknowledgments

This project is supported by National Natural Science Foundation of China (71203164) and the Fundamental Research Funds for the Central Universities (2012GSP058, Wuhan University).

Supplementary material

11192_2015_1629_MOESM1_ESM.docx (889 kb)
Supplementary material 1 (DOCX 888 kb)

References

  1. Ahlgren, P., & Jarneving, B. (2008). Bibliographic coupling, common abstract stems and clustering: A comparison of two document–document similarity approaches in the context of science mapping. Scientometrics, 76(2), 273–290. doi: 10.1007/s11192-007-1935-1.CrossRefGoogle Scholar
  2. Assefa, S. G., & Rorissa, A. (2013). A bibliometric mapping of the structure of STEM education using co-word analysis. Journal of the American Society for Information Science and Technology, 64(12), 2513–2536. doi: 10.1002/asi.22917.CrossRefGoogle Scholar
  3. Åström, F. (2007). Changes in the LIS research front: Time-sliced cocitation analyses of LIS journal articles, 1990–2004. Journal of the American Society for Information Science and Technology, 58(7), 947–957. doi: 10.1002/asi.20567.CrossRefGoogle Scholar
  4. Bagley, P. R. (1968). Extent of programming language concepts. University City Science Center technical report, November 1968.Google Scholar
  5. Bates, M. J., & Maack, M. N. (2010). Encyclopedia of library and information sciences. Boca Raton: CRC Press.MATHGoogle Scholar
  6. Chang, Y. W., & Huang, M. H. (2012). A study of the evolution of interdisciplinarity in library and information science: Using three bibliometric methods. Journal of American Society for Information Science and Technology, 63(1), 22–33.CrossRefGoogle Scholar
  7. Chen, L. C., & Lien, Y. H. (2011). Using author co-citation analysis to examine the intellectual structure of e-learning: A MIS perspective. Scientometrics, 89(3), 867–886. doi: 10.1007/s11192-011-0458-y.CrossRefGoogle Scholar
  8. Ganter, B., & Wille, R. (2012). Formal concept analysis: Mathematical foundations[M] (p. 2012). Berlin: Springer.Google Scholar
  9. Gao, J. P., Ding, K., Teng, L., & Pang, J. (2012). Hybrid documents co-citation analysis: Making sense of the interaction between science and technology in technology diffusion. Scientometrics, 93(2), 459–471. doi: 10.1007/s11192-012-0691-z.CrossRefGoogle Scholar
  10. Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30(2), 119–145. doi: 10.1177/0165551504042802.CrossRefGoogle Scholar
  11. Godin, R., Missaoui, R., & Alaoui, H. (1995). Incremental concept formation algorithms based on Galois (concept) lattices. Computational Intelligence, 11(2), 246–267. doi: 10.1111/j.1467-8640.1995.tb00031.x.CrossRefGoogle Scholar
  12. Hood, W. W., & Wilson, C. S. (2001). The literature of bibliometrics, scientometrics, and informetrics. Scientometrics, 52(2), 291–314. doi: 10.1023/A:1017919924342.CrossRefGoogle Scholar
  13. Hu, C. P., Hu, J. M., Deng, S. L., & Liu, Y. (2013). A co-word analysis of library and information science in China. Scientometrics, 97(2), 369–382. doi: 10.1007/s11192-013-1076-7.CrossRefGoogle Scholar
  14. Hu, C. P., Hu, J. M., Gao, Y., & Zhang, Y. K. (2011). A journal co-citation analysis of library and information science in China. Scientometrics, 86(3), 657–670. doi: 10.1007/s11192-010-0313-6.CrossRefGoogle Scholar
  15. Janssens, F., Leta, J., Glanzel, W., & Moor, B. D. (2006). Towards mapping library and information science. Information Processing and Management, 42(6), 1614–1642. doi: 10.1016/j.ipm.2006.03.025.CrossRefGoogle Scholar
  16. Kedrov, B. M. (1980). On modern classification of sciences. Philosophic Issues, 10, 85–103.MathSciNetGoogle Scholar
  17. Kuznetsov, S. O., & Obiedkov, S. A. (2002). Comparing performance of algorithms for generating concept lattices. Journal of Experimental and Theoretical Artificial Intelligence, 14(2–3), 189–216. doi: 10.1080/09528130210164170.CrossRefMATHGoogle Scholar
  18. Larivière, V., Sugimoto, C. R., & Cronin, B. (2012). A bibliometric chronicling of library and information science’s first hundred years. Journal of the American Society for Information Science and Technology, 63(5), 997–1016. doi: 10.1002/asi.22645.CrossRefGoogle Scholar
  19. 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
  20. 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
  21. Ma, R. M. (2012). Author bibliographic coupling analysis: A test based on a Chinese academic database. Journal of Informetrics, 6(4), 532–542. doi: 10.1016/j.joi.2012.04.006.CrossRefGoogle Scholar
  22. Milojević, S., Sugimoto, C. R., Yan, E., & Ding, Y. (2011). The cognitive structure of library and information science: Analysis of article title words. Journal of the American Society for Information Science and Technology, 62(10), 1933–1953. doi: 10.1002/asi.21602.CrossRefGoogle Scholar
  23. 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
  24. Mustafee, N., Katsaliaki, K., & Fishwick, P. (2014). Exploring the modelling and simulation knowledge base through journal co-citation analysis. Scientometrics, 98(3), 2145–2159. doi: 10.1007/s11192-013-1136-z.CrossRefGoogle Scholar
  25. 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
  26. Price, D. J. S. (1965). The scientific foundations of science policy. Nature, 206(4), 233–238. doi: 10.1038/206233a0.CrossRefGoogle Scholar
  27. Solntseff, N., & Yezerski, A. (1974). A survey of extensible programming languages. Annual review in automatic programming, 7, 267–307. doi: 10.1016/0066-4138(74)90001-9.MathSciNetCrossRefGoogle Scholar
  28. Thelwall, M. (2008). Quantitative comparisons of search engine results. Journal of the American Society for Information Science and Technology, 59(11), 1702–1710. doi: 10.1002/asi.20834.CrossRefGoogle Scholar
  29. 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. doi: 10.1002/asi.4630320302.CrossRefGoogle Scholar
  30. 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
  31. 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
  32. Wille, R. (2005). Formal concept analysis as mathematical theory of concepts and concept hierarchies. In B. Ganter, G. Stumme, & R. Wille (Eds.), Formal concept analysis: Foundations and applications, state-of-the-art survey (pp. 1–33). Berlin: Springer.CrossRefGoogle Scholar
  33. Yang, B., & Sun, Y. (2013). An exploration of link-based knowledge map in academic web space. Scientometrics, 96(1), 239–253. doi: 10.1007/s11192-012-0919-y.CrossRefGoogle Scholar
  34. Yin, Z. M., & Ma, R. M. (2009). Review of the application research of SNA on the library and information science in China. Document, Information and Knowledge, 2009(6), 64–69. (in Chinese).MathSciNetGoogle Scholar
  35. 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
  36. Zhao, D., & Strotmann, A. (2008b). Information science during the first decade of the web: An enriched author cocitation analysis. Journal of the American Society for Information Science and Technology, 59(6), 916–937. doi: 10.1002/asi.20799.CrossRefGoogle Scholar
  37. 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

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2015

Authors and Affiliations

  • Ping Liu
    • 1
  • Qiong Wu
    • 1
  • Xiangming Mu
    • 2
  • Kaipeng Yu
    • 1
  • Yiting Guo
    • 1
  1. 1.School of Information ManagementWuhan UniversityWuhanPeople’s Republic of China
  2. 2.School of Information StudiesUniversity of Wisconsin-MilwaukeeMilwaukeeUSA

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