MeSH概念相关性与知识演化：一种数据驱动的视角
MeSH Concept Relevance and Knowledge Evolution: A Data-driven Perspective
AI总结：
本研究提出基于信息论和网络分析的数据驱动方法，从信息量、有用性、颠覆性和影响力四维度量化MeSH概念相关性，并通过引文网络和层级聚合评估概念演化，验证了其在术语变更和撤稿检测中的有效性。
摘要：
医学主题词表（MeSH）是生物医学领域主要的知识组织系统之一，它不断演化以反映健康与生命科学领域的最新科学发现。以往的研究主要通过对MeSH的层级结构进行量化来评估其信息量。在本工作中，我们提出了一种基于信息论和网络分析的数据驱动方法，用于量化MeSH概念的相关性。我们的方法利用文章标注及其引文网络，从四个维度计算相关性——信息量、有用性、颠覆性和影响力——并随时间追踪这些维度。我们结合引文网络和MeSH层级结构来计算这些相关性方面，并应用一种聚合算法将得分传播至父节点。我们在MeSH术语变更上评估了我们的方法，结果表明该方法能有效捕捉概念的演化。演化中的概念的平均相关性高于未变化的概念（$2.09E-03$ 对比 $8.46E-04$）。此外，我们通过分析被撤稿文章验证了该框架，发现用于标注被撤稿文章的概念（平均相关性：0.17）与标注未被撤稿文章的概念（平均相关性：0.15）存在显著差异。总体而言，所提出的框架为概念相关性排序提供了一种有效方法，并能支持不断演化的知识组织系统的维护。
英文摘要：
The Medical Subject Headings (MeSH), one of the main knowledge organization systems in the biomedical domain, continuously evolves to reflect the latest scientific discoveries in health and life sciences. Previous research has focused on quantifying information in MeSH primarily through its hierarchical structure. In this work, we propose a data-driven approach based on information theory and network analysis to quantify the relevance of MeSH concepts. Our method leverages article annotations and their citation networks to compute four aspects of relevance -- informativeness, usefulness, disruptiveness, and influence -- over time. Using both the citation network and the MeSH hierarchy, we compute these relevance aspects and apply an aggregation algorithm to propagate scores to parent nodes. We evaluated our approach on MeSH terminology changes and showed that it effectively captures the evolution of concepts. The mean relevance of evolving concepts is higher compared to concepts that remained unchanged ($2.09E-03$ vs. $8.46E-04$). Moreover, we validated the framework by analyzing retracted articles and found that concepts used to annotate retracted articles (mean relevance: 0.17) differ substantially from those annotating non-retracted ones (mean relevance: 0.15). Overall, the proposed framework provides an effective method for ranking concept relevance and can support the maintenance of evolving knowledge organization systems.
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