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The organization of scientific knowledge: the structural characteristics of keyword networks

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  • Sangyoon Yi

    (University of Southern Denmark)

  • Jinho Choi

    (Sejong University)

Abstract

The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks.

Suggested Citation

  • Sangyoon Yi & Jinho Choi, 2012. "The organization of scientific knowledge: the structural characteristics of keyword networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 1015-1026, March.
    • Handle: RePEc:spr:scient:v:90:y:2012:i:3:d:10.1007_s11192-011-0560-1
    DOI: 10.1007/s11192-011-0560-1
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    References listed on IDEAS

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    Cited by:

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    2. Behrouzi, Saman & Shafaeipour Sarmoor, Zahra & Hajsadeghi, Khosrow & Kavousi, Kaveh, 2020. "Predicting scientific research trends based on link prediction in keyword networks," Journal of Informetrics, Elsevier, vol. 14(4).
    3. Ziyun Xu & Éric Archambault, 2015. "Chinese interpreting studies: structural determinants of MA students’ career choices," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1041-1058, November.
    4. Ba, Zhichao & Liang, Zhentao, 2021. "A novel approach to measuring science-technology linkage: From the perspective of knowledge network coupling," Journal of Informetrics, Elsevier, vol. 15(3).
    5. Guo Chen & Lu Xiao & Chang-ping Hu & Xue-qin Zhao, 2015. "Identifying the research focus of Library and Information Science institutions in China with institution-specific keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 707-724, May.
    6. Lu Xiao & Guo Chen & Jianjun Sun & Shuguang Han & Chengzhi Zhang, 2016. "Exploring the topic hierarchy of digital library research in China using keyword networks: a K-core decomposition approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1085-1101, September.
    7. Anke Piepenbrink & Elkin Nurmammadov, 2015. "Topics in the literature of transition economies and emerging markets," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2107-2130, March.
    8. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    9. Katchanov, Yurij L. & Markova, Yulia V., 2022. "Dynamics of senses of new physics discourse: Co-keywords analysis," Journal of Informetrics, Elsevier, vol. 16(1).

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