用复杂网络建模科学概念演变，揭示化学革命中理论更替的内在机制。
Modeling Changing Scientific Concepts with Complex Networks: A Case Study on the Chemical Revolution
- 基于主题构建概念复杂网络，捕捉科学思想演化路径。
- 发现术语变化越剧烈，网络熵与拓扑密度越高，体现思想多样性提升。
- 适合数字人文、科学史研究者，可规避历史数据偏见风险。
尽管大语言模型生成的上下文嵌入可用于估算概念变迁，但其表示往往不可解释且缺乏时间感知。此外，历史数据中的偏差可能给数字人文研究带来显著风险。为此，本文提出一种基于主题的复杂网络框架，以表征典型科学概念。利用皇家学会语料库，以化学革命中两种竞争理论（燃素说与氧气说）为例，分析表明：术语学变迁（onomasiological change）与更高熵和更强拓扑密度相关，反映出思想多样性的增加与连接强度的提升。
原文摘要 · Abstract (English)
While context embeddings produced by LLMs can be used to estimate conceptual change, these representations are often not interpretable nor time-aware. Moreover, bias augmentation in historical data poses a non-trivial risk to researchers in the Digital Humanities. Hence, to model reliable concept trajectories in evolving scholarship, in this work we develop a framework that represents prototypical concepts through complex networks based on topics. Utilizing the Royal Society Corpus, we analyzed two competing theories from the Chemical Revolution (phlogiston vs. oxygen) as a case study to show that onomasiological change is linked to higher entropy and topological density, indicating increased diversity of ideas and connectivity effort.
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