基于引文重要性的知识流动主路径分析

Tracing the Knowledge Flow Main Path Based on Important Citations

传统基于引文网络的主路径分析方法没有考虑引文对施引文献的相对价值,认为一篇学术文献的所有引文对该文献具有同等程度的知识贡献。本文从引用行为的统计学层面和语义信息层面综合区分引文重要性,探讨引文对施引文献的重要性对构造主路径的影响。构建了引文重要度指标对主路径分析方法中的链接遍历计数进行调节,并通过实例验证了改进后的主路径分析方法在提取知识流方面的性能。实验结果显示,经过引文重要度加权调节后的关键主路径和全局主路径取得了实验中最高的精确值和F1值。研究结果表明,通过引文重要度加权调节可以增加主路径链接在时间上的连续性,提高节点间的相关性,提升主路径分析方法找到关键节点的能力和链接溯源能力。

The traditional main path method based on citation network analysis does not consider the knowledge contribution of the cited literature to citations.It is considered that all citations of an academic document are of equal importance to the document,even though there can be various reasons and different levels of importance from one document to another.This paper applied main path analysis,taking different important citation levels into consideration,and constructed a citation importance index as a reference variable for adjusting traversal counts.The experimental results showed that the key main path and global main path after weighted adjustment of citation importance achieved the highest Precision and F1scores in this experiment.This research proves that the weighted adjustment with citation importance can increase the continuity of the main path link in time,improving the correlation between nodes.This improves the traceability of the main path analysis method and the ability to find key nodes.