Trajectories of Change: Approaches for Tracking Knowledge Evolution

2024-12-31

AI Summary

This study investigates the temporal evolution of scientific knowledge at both local (individual) and global (disciplinary) scales, and elucidates the synergistic and tensional relationships between individual scholarly work and collective disciplinary development. To this end, we propose a socio-epistemic network (SEN) framework integrating three coupled layers—social, symbolic, and semantic—and introduce, for the first time, a dual-track knowledge trajectory modeling approach that jointly leverages relative-entropy-driven semantic drift detection and dynamic document embedding density analysis. Our methodology synthesizes information theory, semantic embedding, multilayer network modeling, and large-scale bibliometric mining. Empirical evaluation successfully reconstructs the research trajectories of Joseph Silk and Hans-Jürgen Treder in general relativity, quantitatively uncovering deep interaction mechanisms between their contributions and field-level evolution. The framework delivers an interpretable, scalable paradigm for knowledge evolution analysis in science of science.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

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.

Problem

• Knowledge Evolution
• Global Perspective
• Scholarly Contribution

Innovation

• Social Cognition Network
• Knowledge Evolution
• Scientific Collaboration