Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

• URL: http://arxiv.org/abs/2605.07639v1
• Date: Fri, 08 May 2026 12:06:02 GMT
• Title: Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference
• Abstract summary: We introduce a neuro-symbolic framework that combines Logic-Augmented Generation and an Active-Inference-inspired approach for tacit-grounded Knowledge Graph construction.<n>Results show that the proposed solution improves completeness and semantic quality, advancing neuro-symbolic knowledge engineering for industrial domains.
• Score: 1.368117423972661
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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