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Co-word analysis method based on meta-path of subject knowledge network

Author

Listed:
  • Xiang Zhu

    (Jilin University)

  • Yunqiu Zhang

    (Jilin University)

Abstract

We propose a method of co-word analysis based on the subject knowledge network meta-path to overcome limitations with the current co-word analysis method. First, we construct a subject knowledge network to find the word-to-word meta-path. Second, we use the HeteSim algorithm to calculate the semantic relevance between words based on each meta-path. Then, through matrix operations, standardization, and matrix fusion, we construct a word-to-word semantic relevance matrix (WSRM). We conduct an empirical evaluation to test the proposed method. The results indicate that the WSRM formed by this method is superior to the word-to-word similarity matrix used in traditional co-word analysis in terms of both macro-evaluation indicators (viz., network density, network centralization, network average degree, and cohesive subgroups) and micro-evaluation indicators (viz., core-periphery class, point centrality, and cluster analysis). The method overcomes limitations to the traditional co-word analysis method, and combines multiple semantic relations between words, to reflect the relationship between words more realistically.

Suggested Citation

  • Xiang Zhu & Yunqiu Zhang, 2020. "Co-word analysis method based on meta-path of subject knowledge network," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 753-766, May.
    • Handle: RePEc:spr:scient:v:123:y:2020:i:2:d:10.1007_s11192-020-03400-0
    DOI: 10.1007/s11192-020-03400-0
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    References listed on IDEAS

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    1. Jia Feng & Yun Qiu Zhang & Hao Zhang, 2017. "Improving the co-word analysis method based on semantic distance," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1521-1531, June.
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