Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

2026-05-08

AI Summary

This work addresses the challenge of formalizing human tacit knowledge—such as implicit assumptions, contextual constraints, and experiential judgments—in procedural tasks into machine-reasonable representations. To this end, the authors propose a novel neuro-symbolic framework that integrates logic-augmented generation with active inference to automatically extract tacit knowledge from instructional videos depicting assembly and maintenance procedures, and to construct an ontology-aligned knowledge graph. By synergistically combining neural perception with symbolic reasoning, the approach significantly enhances the semantic completeness and reusability of the resulting knowledge graph. Empirical validation in manufacturing and maintenance scenarios demonstrates its effectiveness, thereby advancing the application of neuro-symbolic systems in industrial knowledge engineering.

Abstract

Tacit knowledge plays a central role in human expertise, yet it remains difficult to capture, formalize, and reuse in machine-interpretable form. This challenge is especially relevant in procedural domains, where successful execution depends not only on explicit instructions, but also on implicit assumptions, contextual constraints, embodied skills, and experience-based judgments rarely documented. As a result, current knowledge engineering pipelines struggle to transform tacit and process-centric knowledge into formally specified, machine-interpretable representations that can be queried, validated, reasoned over, and reused. In this paper, we introduce a neuro-symbolic framework that combines Logic-Augmented Generation and an Active-Inference-inspired approach for ontology-grounded Knowledge Graph construction. We evaluate the approach in a knowledge transfer case study in manufacturing, using assembly-like repair procedures from instructional videos as a reproducible proxy domain. Results show that the proposed solution improves completeness and semantic quality, advancing neuro-symbolic knowledge engineering for industrial domains.

Problem

• Tacit Knowledge
• Knowledge Extraction
• Machine-interpretable Representation
• Procedural Domains
• Knowledge Engineering

Innovation

• Tacit Knowledge Extraction
• Logic-Augmented Generation
• Active Inference
• Neuro-Symbolic Framework
• Ontology-Grounded Knowledge Graph