图书情报工作 ›› 2016, Vol. 60 ›› Issue (1): 98-104,141.DOI: 10.13266/j.issn.0252-3116.2016.01.014

• 情报研究 • 上一篇    下一篇

融入内容信息的作者共被引分析——以学科服务研究主题为例

李秀霞1, 邵作运2   

  1. 1. 曲阜师范大学传媒学院 日照 276826;
    2. 曲阜师范大学日照校区图书馆 日照 276826
  • 收稿日期:2015-11-06 修回日期:2015-12-17 出版日期:2016-01-05 发布日期:2016-01-05
  • 作者简介:李秀霞(ORCID:0000-0002-3492-4768),副教授,硕士生导师,E-mail:zyshao@126.Com;邵作运(ORCID:0000-0003-1818-5587),馆员,硕士。

The Author Co-citation Analysis Based on the Content Information——Taking the Subject Service As an Example

Li Xiuxia1, Shao Zuoyun2   

  1. 1. School of Communication, Qufu Normal University, Rizhao 276826;
    2. Library of Rizhao Campus, Qufu Normal University, Rizhao 276826
  • Received:2015-11-06 Revised:2015-12-17 Online:2016-01-05 Published:2016-01-05

摘要: [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NetDraw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。

关键词: 作者共被引分析, 内容分析, C-ACA方法, 因子分析

Abstract: [Purpose/significance] Author co-citation analysis(ACA) is an effective method for identifying the intellectual structure of a research domain, but it relies on simple co-citation counting, which does not take the citation content into consideration.In this paper, we propose a new method, named content and author co-citation analysis(C-ACA), for measuring the similarity between co-cited authors by considering authors' literature contents.[Method/process] Taking the subject service as an example, firstly, we built ACA author similarity matrix and author-content matrix respectively.Then we converted the author-content matrix to author similarity matrix, and realized the convergence of and by constructing the linear convergence function.Finally, we revealed the knowledge structure by extracting the author theme factor components and realized the 2-model visualization in NetDraw environment.[Result/conclusion] By comparing C-ACA with traditional ACA, it is found that the C-ACA method can be used to explore and reveal more accurate and detailed knowledge structure of the subject area.

Key words: author co-citation analysis, content analysis, content-ACA, factor analysis

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