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Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis
Scientometrics ( IF 3.9 ) Pub Date : 2020-06-25 , DOI: 10.1007/s11192-020-03576-5
Qikai Cheng , Jiamin Wang , Wei Lu , Yong Huang , Yi Bu

This paper proposes keyword-citation-keyword (KCK) network to analyze the knowledge structure of a discipline. Different from traditional co-word network analysis, KCK network highlights the importance of keywords assigned in different articles, as well as the semantic relationship between keywords in various articles. In this study, we select computer science domain as an example to illustrate the proposed method. Meanwhile, the results of network analysis, PageRank analysis, and research topic analysis are compared with those of traditional co-word analysis. A total of 110,360 articles with 164,146 unique keywords and 1,615,030 references collected from ACM digital library have been used for this empirical study. The results demonstrate that KCK network outperforms in detecting indirect links between keywords with higher semantic relationship, identifying important knowledge units, as well as discovering the topics with greater significance. Findings from this study contribute to a new perspective and understanding for elucidating discipline knowledge structures, and provide guidance for applying this method in various disciplines.

中文翻译:

关键词-引文-关键词网络:学科知识结构分析的新视角

本文提出了关键词-引文-关键词(KCK)网络来分析一门学科的知识结构。与传统的共词网络分析不同,KCK网络突出了不同文章中分配的关键词的重要性,以及各种文章中关键词之间的语义关系。在本研究中,我们选择计算机科学领域作为示例来说明所提出的方法。同时,将网络分析、PageRank分析、研究主题分析的结果与传统的共词分析进行比较。本次实证研究共使用了从 ACM 数字图书馆收集的 110,360 篇文章,164,146 个唯一关键字和 1,615,030 篇参考文献。结果表明,KCK 网络在检测具有较高语义关系的关键字之间的间接链接方面表现出色,识别重要的知识单元,以及发现更有意义的主题。本研究的发现有助于对阐明学科知识结构提供新的视角和理解,并为将这种方法应用于各个学科提供指导。
更新日期:2020-06-25
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