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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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References

  • Banjade, R., Maharjan, N., Niraula, N. B., Rus, V., & Gautam, D. (2015). Lemon and tea are not similar: Measuring word-to-word similarity by combining different methods. In International conference on intelligent text processing and computational linguistics (pp. 335–346). Cham: Springer.

  • Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.

    Article  Google Scholar 

  • Castanedo, F. (2013). A review of data fusion techniques. The Scientific World Journal,2013, 1–19.

    Article  Google Scholar 

  • Choudhury, N., & Uddin, S. (2016). Time-aware link prediction to explore network effects on temporal knowledge evolution. Scientometrics,108(2), 745–776.

    Article  Google Scholar 

  • Feng, J., Zhang, Y. Q., & Zhang, H. (2017). Improving the co-word analysis method based on semantic distance. Scientometrics,111(3), 1521–1531.

    Article  Google Scholar 

  • Fu, G., Ding, Y., Seal, A., Chen, B., Sun, Y., & Bolton, E. (2016). Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinformatics,17(1), 160.

    Article  Google Scholar 

  • Gu, D. L. (2008). On discipline network. Journal of Information,2008(9), 50–55.

    Google Scholar 

  • Holmberg, K., & Hellqvist, B. (2009). The Nordic landscape of LIS research: A new approach for co-word analysis of research in three Nordic countries. In 12th ISSI conference, 14–17 July, 2009, Rio de Janeiro, Brazil (pp. 942–943).

  • Hu, K., Wu, H., Qi, K., Yu, J., Yang, S., Yu, T., et al. (2018). A domain keyword analysis approach extending term frequency-keyword active index with Google Word2Vec model. Scientometrics,114(3), 1031–1068.

    Article  Google Scholar 

  • Li, M. (2017). An exploration to visualize the emerging trends of technology foresight based on an improved technique of co-word analysis and relevant literature data of WOS. Technology Analysis & Strategic Management,29(6), 655–671.

    Article  Google Scholar 

  • Li, M., & Chu, Y. (2017). Explore the research front of a specific research theme based on a novel technique of enhanced co-word analysis. Journal of Information Science,43(6), 725–741.

    Article  Google Scholar 

  • Li, G., Li, Y. Y., Xie, Z. L., & Ba, Z. C. (2017). Research on the influence of mixed keyword selection strategy on co-word analysis results. Information Studies: Theory & Application,40(11), 110–116.

    Google Scholar 

  • Li, X., & Shao, Z. (2016). Comparative study on the network characteristics of three kinds of analysis approaches about academic journals: Taking the core journals of library and information science as an example. Chinese Journal of Scientific and Technical Periodicals,27(9), 981–989.

    Google Scholar 

  • Liu, C., Li, Y., Zhou, G., & Shen, W. (2018). A sensor fusion and support vector machine based approach for recognition of complex machining conditions. Journal of Intelligent Manufacturing,29(8), 1739–1752.

    Article  Google Scholar 

  • Markazi-Moghaddam, N., Mohammad, A. R. A. B., Ravaghi, H., Rashidian, A., Khatibi, T., & Jame, S. Z. B. (2016). A knowledge map for hospital performance concept: Extraction and analysis: A narrative review article. Iranian Journal of Public Health,45(7), 843.

    Google Scholar 

  • Sasson, E., Ravid, G., & Pliskin, N. (2015). Improving similarity measures of relatedness proximity: Toward augmented concept maps. Journal of Informetrics,9(3), 618–628.

    Article  Google Scholar 

  • Shi, C., Kong, X., Yu, P. S., Xie, S., & Wu, B. (2012). Relevance search in heterogeneous networks. In Proceedings of the 15th international conference on extending database technology (pp. 180–191). ACM.

  • Wang, F., Hu, L., Zhou, J., Hu, J., & Zhao, K. (2017). A semantics-based approach to multi-source heterogeneous information fusion in the internet of things. Soft Computing,21(8), 2005–2013.

    Article  Google Scholar 

  • Wang, Z. Y., Li, G., Li, C. Y., & Li, A. (2012). Research on the semantic-based co-word analysis. Scientometrics,90(3), 855–875.

    Article  Google Scholar 

  • Zhang, J. Z., Han, T., & Wang, X. M. (2012). Overview of complex network research and its application in library and information science. Journal of the China Society for Scientific and Technical Information,33(9), 907–914.

    Google Scholar 

  • Zhang, X. Y., Zhang, P., & Zhang, Q. S. (2018). Research on information fusion method for mobile electronic commerce based on improved Monte Carlo algorithm under big data environment. In 2018 Chinese control and decision conference (CCDC) (pp. 3671–3675). IEEE.

  • Zhou, L., Ba, Z., Fan, H., & Zhang, B. (2018). Research on the semantic measurement in co-word analysis. In International conference on information (pp. 409–419). Cham: Springer.

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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

Authors
  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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  • DOI: https://doi.org/10.1007/s11192-020-03400-0

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