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Knowledge-transfer analysis based on co-citation clustering

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Abstract

Based on co-citation cluster analysis, we propose a knowledge-transfer analysis model for any technology field. In this model, patent data with backward citations to non-patent literature and forward citations by later patents would be analyzed. Co-citation clustering of the cited articles defines scientific knowledge sources, while that of the patents themselves defines technology fronts. According to the citation between the article and patent clusters, the landscape of knowledge-transfer including route and strength between scientific knowledge sources and technology fronts can be mapped out. The model has been applied to the field of transgenic rice. As a result of the analysis, ten scientific knowledge sources and eight technology fronts have emerged, and reasonable links between them have been established, which clearly show how knowledge has been transferred in this field.

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Acknowledgments

The research was supported by Youth Talent Frontier Project of Knowledge Innovation Project of National Science Library of Chinese Academy of Sciences (Grant No. Q110031). The authors would thank Dr. B. Zhang for helpful identification and discussion on the details of article and patent clusters, and Dr. T. Han for helpful discussion on the analysis model.

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Authors and Affiliations

  1. National Science Library, Chinese Academy of Sciences, Beijing, China

    Xuezhao Wang, Yajuan Zhao & Jing Zhang

  2. Institute of Physics, Chinese Academy of Sciences, Beijing, China

    Rui Liu

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  1. Xuezhao Wang
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  2. Yajuan Zhao
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  3. Rui Liu
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  4. Jing Zhang
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Correspondence to Xuezhao Wang.

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Wang, X., Zhao, Y., Liu, R. et al. Knowledge-transfer analysis based on co-citation clustering. Scientometrics 97, 859–869 (2013). https://doi.org/10.1007/s11192-013-1077-6

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  • DOI: https://doi.org/10.1007/s11192-013-1077-6

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