Knowledge-transfer analysis based on co-citation clustering

Authors:
Xuezhao Wang;Yajuan Zhao;Rui Liu;Jing Zhang
Affiliations:
National Science Library, Chinese Academy of Sciences, Beijing, China;National Science Library, Chinese Academy of Sciences, Beijing, China;Institute of Physics, Chinese Academy of Sciences, Beijing, China;National Science Library, Chinese Academy of Sciences, Beijing, China
Venue:
Scientometrics
Year:
2013

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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.