文献计量分析一词及其与科学专题中其他高频关键词的交互作用

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS Pub Date : 2023-11-24 DOI:10.3103/S0005105523050060
Yu. V. Mokhnacheva
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引用次数: 0

摘要

本文介绍了一项关于SciVal (Scopus)主题高频关键词的研究结果,重点是“文献计量学分析”。从2022年5月至11月从181个SciVal主题中收集的高频关键词阵列,其中“文献计量学分析”作为高频关键词出现,通过三组近似相等的共词进行分析,这些共词由主题中的总交集数量组成。高频关键词的这种划分非常符合S. Bradford定律,因此高频关键词的“核心”是在研究的主题上形成的。结果,确定了内容上最接近的主题。我们的研究结果通过分析关键词的动态,有助于理解不同研究主题之间的关系,证实了使用不同学科的关键词网络可以识别它们之间的共同特征以及关键词之间的匹配数量影响主题之间关系强度的假设。
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The Term “Bibliometric Analysis” and Its Interaction with Other High-Frequency Keywords in the Topics of SciVal

The results of a study of high-frequency key terms in the subject of SciVal (Scopus) are presented, focusing on the term “bibliometric analysis.” The array of high-frequency key terms collected for May to November 2022 from 181 SciVal topics, in which the term “Bibliometric Analysis” appeared as the high-frequency key term, was analyzed by three approximately equal groups of co-words, formed by the number of total intersections in the topics. This division of the high-frequency key terms fits well into S. Bradford’s law, due to which the “core” of the high-frequency key terms was formed on the topics under study. As a result, the topics closest in content were identified. The results of our study form a contribution to the understanding of the relationships between different research topics by analyzing the dynamics of keywords, confirming the hypothesis that using networks of keywords from different disciplines, you can identify common features between them and the number of matches between keywords affects the strength of relationships between topics.

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来源期刊
AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS
AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
18
期刊介绍: Automatic Documentation and Mathematical Linguistics  is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.
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