基于概念格的文献共引分析

IF 1.5 0 ENGINEERING, MULTIDISCIPLINARY

Engineering, Technology & Applied Science Research Pub Date : 2023-10-13 DOI:10.48084/etasr.6201

Anamika Gupta, Shikha Gupta, Mukul Bisht, Prestha Hooda, Md Salik

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引用次数: 0

摘要

文献共被引分析(DCA)是一种识别和分析共被引文献之间关系的方法。在本文中，我们尝试在DCA中使用概念格。概念格是形式概念分析(FCA)中给出的一种图结构，是基于概念及其层次的数学分支。实验是在从DBLP、ACM、MAG (Microsoft Academic Graph)和其他来源提取的广泛的引文库上进行的，共有5,354,309篇论文和48,227,950个引文关系。本文证明了概念格支持DCA，并有助于识别一组共被引文献及其共被引强度。它还提供了导航，以反映共引的子集-超集关系。此外，概念格有助于识别文档之间的层次结构，并回答与DCA相关的最相关查询。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Document Co-citation Analysis using Concept Lattice

Document Co-citation Analysis (DCA) is a method to identify and analyze the relationships between co-cited documents. In this paper, we attempt to use concept lattice for DCA. Concept lattice is a graph structure given in Formal Concept Analysis (FCA), a branch of mathematics based on the concept and its hierarchy. The experiments are conducted on an extensive repository of citations extracted from DBLP, ACM, MAG (Microsoft Academic Graph), and other sources, having a total of 5,354,309 papers and 48,227,950 citation relationships. In this paper, it is established that the concept lattice supports DCA and helps to identify a set of co-cited documents and their co-citation strength. It also provides navigation to reflect the subset-superset relationship of the co-citations. Further, the concept lattice helps identify the hierarchy among the documents and answers the most relevant queries related to DCA.

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来源期刊

Engineering, Technology & Applied Science Research ENGINEERING, MULTIDISCIPLINARY-

CiteScore

3.00

自引率

46.70%

发文量

222

审稿时长

11 weeks