Trajectories of Change: Approaches for Tracking Knowledge Evolution

TL;DR

This paper introduces a multilayered framework using socio-epistemic networks and information-theoretic measures to analyze how scientific knowledge evolves over time, focusing on semantic shifts and document trajectories.

Contribution

It presents a novel multilayered approach combining semantic, social, and semiotic analysis to track knowledge evolution in scientific texts, with case studies demonstrating its application.

Findings

Semantic shifts can be detected using relative entropy measures.

Document embedding densities reveal changes in research focus.

Individual scholar trajectories reflect broader disciplinary trends.

Abstract

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-J\"urgen 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.