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Assessment of ontology-based knowledge network formation by Vector-Space Model

Author

Listed:
  • Pei-Chun Lee

    (University of Sussex
    National Applied Research Laboratories
    National Chengchi University)

  • Hsin-Ning Su

    (National Applied Research Laboratories)

  • Te-Yi Chan

    (National Applied Research Laboratories)

Abstract

This study proposes an empirical way for determining probability of network tie formation between network actors. In social network analysis, it is a usual problem that information for determining whether or not a network tie should be formed is missing for some network actors, and thus network can only be partially constructed due to unavailability of information. This methodology proposed in this study is based on network actors’ similarities calculations by Vector-Space Model to calculate how possible network ties can be formed. Also, a threshold value of similarity for deciding whether or not a network tie should be generated is suggested in this study. Four ontology-based knowledge networks, with journal paper or research project as network actors, constructed previously are selected as the targets of this empirical study: (1) Technology Foresight Paper Network: 181 papers and 547 keywords, (2) Regional Innovation System Paper Network: 431 papers and 1165 keywords, (3) Global Sci-Tech Policy Paper Network: 548 papers and 1705 keywords, (4) Taiwan’s Sci-Tech Policy Project Network: 143 research projects and 213 keywords. The four empirical investigations allow a cut-off threshold value calculated by Vector-Space Model to be suggested for deciding the formation of network ties when network linkage information is unavailable.

Suggested Citation

  • Pei-Chun Lee & Hsin-Ning Su & Te-Yi Chan, 2010. "Assessment of ontology-based knowledge network formation by Vector-Space Model," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(3), pages 689-703, December.
    • Handle: RePEc:spr:scient:v:85:y:2010:i:3:d:10.1007_s11192-010-0267-8
    DOI: 10.1007/s11192-010-0267-8
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    References listed on IDEAS

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    Cited by:

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    2. Jan Lampe & Priscilla Sarai Kraft & Andreas Bausch, 2020. "Mapping the Field of Research on Entrepreneurial Organizations (1937–2016): A Bibliometric Analysis and Research Agenda," Entrepreneurship Theory and Practice, , vol. 44(4), pages 784-816, July.
    3. Jiancheng Guan & Lanxin Pang, 2018. "Bidirectional relationship between network position and knowledge creation in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 201-222, April.
    4. Zhichao Ba & Yujie Cao & Jin Mao & Gang Li, 2019. "A hierarchical approach to analyzing knowledge integration between two fields—a case study on medical informatics and computer science," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(3), pages 1455-1486, June.
    5. Xinyuan Zhang & Qing Xie & Chaemin Song & Min Song, 2022. "Mining the evolutionary process of knowledge through multiple relationships between keywords," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 2023-2053, April.
    6. 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).
    7. Florian Kreuchauff & Vladimir Korzinov, 2017. "A patent search strategy based on machine learning for the emerging field of service robotics," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 743-772, May.
    8. 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.
    9. 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.

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