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2020 , Vol. 64 >Issue 8: 111 - 124

DOI: https://doi.org/10.13266/j.issn.0252-3116.2020.08.013

Empirical Study of a Semantic and Proximity-based Author Co-citation Analysis Method

  • Zhang Ruhao
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  • Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041 Department of Library and Information Science, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100049;National Science Library, Chinese Academy of Sciences, Beijing 100190

Received date: 2019-09-27

  Revised date: 2019-12-17

  Online published: 2020-04-20

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Abstract

[Purpose/significance] The author co-citation analysis is an vital method to explore the domain knowledge structure. In the context of complex development of disciplines, the author’s relevance measure based on the co-citation frequency is quite controversial. The study proposed an improved method for author co-citation analysis based on the similarity of content semantics and the proximity of locations. [Method/process] Based on the introduction of its basic principles, the field of LIS was used as an example to demonstrate the effect of the method, a full-text mining of citations for CNKI Chinese journals was conducted, and the citing sentences and reference positions were then extracted. Combined with pre-trained domain word embedding models, the deep correlation between the co-cited literature and the strength of the connection between the authors were measured. A network analysis and a factor analysis were then used to compare the differences on effects between the method and the traditional method. [Result/conclusion] The results show that the method can more accurately identify the correlation strength between authors, and find more detailed subject knowledge structure, and has a certain scalability and applicability.

Key words： author co-citation analysis; citation content analysis; co-citation proximity analysis; citation in full-text; knowledge structure

Cite this article

Zhang Ruhao . Empirical Study of a Semantic and Proximity-based Author Co-citation Analysis Method[J]. Library and Information Service, 2020 , 64(8) : 111 -124 . DOI: 10.13266/j.issn.0252-3116.2020.08.013

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