Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

TL;DR

This paper presents a neuro-symbolic framework combining Logic-Augmented Generation and Active Inference to extract and formalize tacit knowledge from procedural domains, demonstrated through manufacturing case studies.

Contribution

It introduces a novel neuro-symbolic approach for ontology-grounded knowledge graph construction that enhances the completeness and semantic quality of tacit knowledge extraction.

Findings

Improved completeness of knowledge graphs in manufacturing domain.

Enhanced semantic quality of extracted tacit knowledge.

Effective use of instructional videos as proxy domain for evaluation.

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.