[2605.07639] Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

Computer Science > Artificial Intelligence arXiv:2605.07639 (cs) [Submitted on 8 May 2026] Title:Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference Authors:Lorenzo Lamazzi, Aldo Gangemi, Alessio Giberti, Andrea Giovanni Nuzzolese, Vittorio Andrea Rocca, Mattia Torta, Francesco Poggi View a PDF of the paper titled Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference, by Lorenzo Lamazzi and 6 other authors View PDF HTML (experimental) 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...