Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference
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 co...
| Authors | Lorenzo Lamazzi, Aldo Gangemi, Alessio Giberti et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 May 2026 |
| Last updated at source | 8 May 2026 |
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