Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

Lorenzo Lamazzi; Aldo Gangemi; Alessio Giberti; Andrea Giovanni Nuzzolese; Vittorio Andrea Rocca; Mattia Torta; Francesco Poggi

arXiv · 2605.07639

Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

Lorenzo Lamazzi·Aldo Gangemi·Alessio Giberti·Andrea Giovanni Nuzzolese·Vittorio Andrea Rocca·Mattia Torta·Francesco Poggi

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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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Lorenzo Lamazzi, Aldo Gangemi, Alessio Giberti, Andrea Giovanni Nuzzolese, Vittorio Andrea Rocca, Mattia Torta, Francesco Poggi. 2026-05-08. Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference. https://arxiv.org/abs/2605.07639

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