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

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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.

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Authors and Affiliations

  1. Department of Medical Informatics, School of Public Health, Jilin University, Changchun, 130021, China

    Xiang Zhu & Yunqiu Zhang

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  1. Xiang Zhu
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  2. Yunqiu Zhang
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Contributions

XZ proposed research ideas, planned and designed the outline, carried out data collection and data analysis, and wrote the first draft. YZ (yunqiu@jlu.edu.cn, corresponding) revised the plan and outline, discussed the findings, and contributed to writing and revising the manuscript.

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Correspondence to Yunqiu Zhang.

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Zhu, X., Zhang, Y. Co-word analysis method based on meta-path of subject knowledge network. Scientometrics 123, 753–766 (2020). https://doi.org/10.1007/s11192-020-03400-0

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