Modeling Changing Scientific Concepts with Complex Networks: A Case Study on the Chemical Revolution
This work addresses the need for interpretable and time-aware models of conceptual evolution in the Digital Humanities, though it appears incremental as it builds on existing methods for analyzing historical data.
The researchers tackled the problem of modeling conceptual change in scientific literature by developing a complex network framework to represent prototypical concepts, using the Chemical Revolution as a case study to show that onomasiological change correlates with higher entropy and topological density.
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.