Full-text citation analysis: enhancing bibliometric and scientific publication ranking

Proceedings of the 21st ACM international conference on Information and knowledge management Pub Date : 2012-10-29 DOI:10.1145/2396761.2398555

Xiaozhong Liu, Jinsong Zhang, Chun Guo

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引用次数: 21

Abstract

The goal of this paper is to use innovative text and graph mining algorithms along with full-text citation analysis and topic modeling to enhance classical bibliometric analysis and publication ranking. By utilizing citation contexts extracted from a large number of full-text publications, each citation or publication is represented by a probability distribution over a set of predefined topics, where each topic is labeled by an author contributed keyword. We then used publication/citation topic distribution to generate a citation graph with vertex prior and edge transitioning probability distributions. The publication importance score for each given topic is calculated by PageRank with edge and vertex prior distributions. Based on 104 topics (labeled with keywords) and their review papers, the cited publications of each review paper are assumed as "important publications" for ranking evaluation. The result shows that full text citation and publication content prior topic distribution along with the PageRank algorithm can significantly enhance bibliometric analysis and scientific publication ranking performance for academic IR system.

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全文引文分析:提高文献计量学和科学出版物排名

本文的目标是利用创新的文本和图形挖掘算法以及全文引文分析和主题建模来增强经典文献计量分析和出版物排名。通过利用从大量全文出版物中提取的引文上下文，每个引文或出版物由一组预定义主题的概率分布表示，其中每个主题由作者贡献的关键字标记。然后，我们使用出版物/引文主题分布来生成具有顶点先验和边缘转移概率分布的引文图。每个给定主题的发表重要性分数由PageRank计算，并具有边和顶点先验分布。基于104个主题(标注关键词)及其综述论文，假设每篇综述论文的被引出版物为“重要出版物”进行排名评价。结果表明，全文引用和出版物内容优先主题分布结合PageRank算法可以显著提高学术IR系统的文献计量分析和科学出版物排名性能。

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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Proceedings of the 21st ACM international conference on Information and knowledge management

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