Modeling Changing Scientific Concepts with Complex Networks: A Case Study on the Chemical Revolution

2026-03-18

AI Summary

Current large language models produce contextual embeddings that lack interpretability and temporal awareness, are susceptible to historical biases, and struggle to reliably capture the evolution of scientific concepts. This work proposes a novel framework integrating topic modeling and complex network analysis, introducing topological density and information entropy into conceptual history research for the first time to dynamically characterize semantic structural shifts in scientific ideas. Using the Royal Society’s historical corpus, we construct a temporal concept network centered on the paradigm shift from phlogiston theory to oxidation theory. Our analysis reveals that conceptual transitions are significantly associated with higher entropy and increased network topological density, indicating concurrent growth in intellectual diversity and efforts toward knowledge integration. These findings demonstrate the method’s effectiveness and robustness in capturing the dynamics of scientific paradigm evolution.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Data Mining & Knowledge Management: Semantic Web
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• conceptual change
• complex networks
• Digital Humanities
• historical bias
• scientific concepts

Innovation

• complex networks
• conceptual change
• topic-based representation
• historical bias mitigation
• Chemical Revolution