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| Semantic- and Contextual-based Author Bibliographic Coupling Analysis |
| Zhang Ruhao1,2, Yuan Junpeng1,2 |
1.Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049
2.National Science Library, Chinese Academy of Sciences, Beijing 100190 |
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Abstract Author bibliographic coupling analysis (ABCA) is an important tool for detecting active research communities and mapping domain knowledge structures. ABCA only uses the citation count to naively represent the bibliographic coupling relevance strength between authors, ignoring their similarities at a deep internal level. To enhance the reliability and insight of ABCA, this study attempts to use rich full-text resources to mine key information contained in the citation content, and proposes an innovative method, named semantic- and contextual-based author bibliographic coupling analysis (SC-ABCA), which aims at enhancing the strength of the similarity of citing motives from the essential level. By mining the full-text of scientific literature, the proposed method extracts the features of semantics and context of citations to calculate the enhanced bibliographic coupling strength and assign a different similarity value to each bibliographic couple. Through the “paper-topic-author” aggregation mapping, which considers each author's interests toward multiple current topics, this method shows the topic similarity relationship between authors. This research also performs an empirical study using 13,562 full-text papers in the field of Chinese LIS. The result shows that the proposed method performs better than ABCA on author community discovery and maps a more detailed knowledge structure. It has a higher content coherence gain and the probability of mutual citation inside clusters; additionally, the method is more robust when facing large-scale author data and has potential for further expansion and application.
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Received: 05 September 2021
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