用多层网络追踪科学知识演变，揭示学者研究如何随领域发展而变化。
Trajectories of Change: Approaches for Tracking Knowledge Evolution
- 构建社会-符号-语义三层框架，分析知识系统的局部与全局演化。
- 通过相对熵检测语义漂移，发现文献聚类密度变化反映主题集中或分散。
- 案例展示个人研究如何映射学科变迁，适合从事科学史与知识图谱研究者。
我们基于社会认识论网络（SEN）框架，通过两种互补方法分析科学文本语料库中知识系统的局部与全局演化。该框架包含社会、符号（物质）和语义三个相互关联的层次，提出一种多层理解知识结构演化的路径。为分析语义层的历时性变化，首先采用基于相对熵的信息论度量，检测语义转变、评估其显著性，并识别关键驱动特征；其次，通过文档嵌入密度的变化，追踪语义邻域的演化——即相似文档的聚集程度是增强、保持稳定还是分散。由此可依据内容（主题）或元数据（作者、机构）描绘文献轨迹。以约瑟夫·西尔克和汉斯-于尔根·特雷德的研究为例，展示个体学术工作如何呼应广义相对论与引力研究领域的整体演变，验证了该方法的应用价值、局限性及进一步潜力。
原文摘要 · Abstract (English)
We explore local vs. global evolution of knowledge systems through the framework of socio-epistemic networks (SEN), applying two complementary methods to a corpus of scientific texts. The framework comprises three interconnected layers-social, semiotic (material), and semantic-proposing a multilayered approach to understanding structural developments of knowledge. To analyse diachronic changes on the semantic layer, we first use information-theoretic measures based on relative entropy to detect semantic shifts, assess their significance, and identify key driving features. Second, variations in document embedding densities reveal changes in semantic neighbourhoods, tracking how concentration of similar documents increase, remain stable, or disperse. This enables us to trace document trajectories based on content (topics) or metadata (authorship, institution). Case studies of Joseph Silk and Hans-Jürgen Treder illustrate how individual scholar's work aligns with broader disciplinary shifts in general relativity and gravitation research, demonstrating the applications, limitations, and further potential of this approach.
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