Knowledge Tectonics: A Geodynamic Inspired Framework for Modeling Epistemic Changes

2026-07-14

AI Summary

This work proposes a knowledge evolution modeling approach inspired by Earth’s plate tectonics to characterize the long-term migration and recombination of concepts embodied in creative artifacts—such as artworks—within semantic networks. By analogizing knowledge systems to a “cognitive lithosphere,” the study introduces, for the first time, a geodynamic metaphor to construct a manifold-based modeling framework that integrates dynamic knowledge graphs, Poisson potential fields, and gradient vector fields, thereby visualizing the driving forces behind the co-evolution of semantic and pragmatic metadata. Applied to the WikiArt Emotions dataset, the method successfully visualizes affective and stylistic drift across 4,105 paintings spanning six centuries, effectively revealing the dynamic trajectories of human cognitive evolution.

Abstract

Emerging knowledge can be envisioned as protruding magma. This paper describes how such a metaphoric view can be turned into a model to capture the 'continental drift' of concepts in an epistemic lithosphere. We call this new approach Knowledge Tectonics. We detail conceptual, mathematical and engineering operations to create such a scalable framework which allows us to interpret and manage knowledge evolution within Semantic Web environments. We use the WikiArt Emotions dataset which contains information on 4,105 paintings spanning 600 years to construct a proof--of--concept interface which enables visual analytics of where artworks are situated in a specific landscape of features. We demonstrate how fused semantic and pragmatic metadata can be modelled as evolving pressure zones. This way we are making 'forces' behind the evolution of artifacts of creativity visible. Core elements of our new workflow are gradient vector fields derived from Poisson potential surfaces applied onto dynamic knowledge graphs, eventually capturing stylistic and emotional shifts as directed intensity flows. By treating artefacts of creative processes as a dynamic manifold, we provide a novel methodology for quantifying the 'drift' of human inquiry. We argue that this approach is applicable also to other areas of creative human actions, including scientific knowledge production.

Problem

• knowledge evolution
• conceptual drift
• semantic dynamics
• creative artifacts
• epistemic change

Innovation

• Knowledge Tectonics
• dynamic knowledge graphs
• gradient vector fields
• Poisson potential surfaces
• semantic-pragmatic metadata