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[目的 /意义]为改善作者共被引分析(author co-citation analysis,ACA)在识别学科领域知识结构中缺乏内容信息的不足,将文献内容信息(题名、摘要、关键词)引入到作者共被引分析中,提出一种新的作者共被引分析方法,即“内容与ACA融合的方法(content and author co-citation analysis,C-ACA)”。[方法 /过程]以“学科服务”主题领域为例,分别建立ACA作者相似矩阵Aij、作者-内容矩阵并转换为作者相似矩阵Bij;通过构建线性融合函数实现作者文献内容与ACA的融合;最后通过提取作者主题因子成分并在Net Draw环境下进行2-模图可视化,挖掘并呈现学科服务研究领域的知识结构。[结果 /结论]与传统ACA方法比较,C-ACA方法能够更准确、更细致地挖掘和揭示学科领域知识结构。
[Purpose / Significance] To improve the lack of content information in the author’s co-citation analysis (ACA) knowledge structure in the field of subject recognition, the author introduces the content information (title, abstract, key words) to the author In citation analysis, a new author-citation analysis method is proposed, that is, “content and author co-citation analysis (C-ACA)”. [Methods / Processes] Taking the subject area of “Subject Services” as an example, the similarities matrixes Aij, author-content matrices and authors’ similarity matrices Bij are established respectively; the fusion of author’s literature contents and ACA is constructed by constructing a linear fusion function Finally, by extracting the subject matter factor and visualizing the 2-module graph in the environment of Net Draw, the knowledge structure in the field of discipline service research is tapped and presented. [Results / Conclusion] Compared with the traditional ACA method, C-ACA method can more accurately and more meticulously excavate and reveal the knowledge structure in the field of disciplines.