Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference

2026-05-08 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

A new neuro-symbolic framework has been introduced to address the challenge of capturing, formalizing, and reusing tacit knowledge, particularly in procedural domains like manufacturing. This framework integrates Logic-Augmented Generation with an Active-Inference-inspired approach to construct ontology-grounded Knowledge Graphs. The solution was evaluated in a knowledge transfer case study involving assembly-like repair procedures from instructional videos. Results indicate that the proposed method significantly enhances the completeness and semantic quality of extracted knowledge, marking an advancement in neuro-symbolic knowledge engineering for industrial applications where implicit assumptions and experience-based judgments are critical for successful execution.

Key takeaway

For AI Engineers developing knowledge systems in industrial or procedural domains, this framework offers a robust method to capture previously elusive tacit knowledge. You should consider integrating Logic-Augmented Generation and Active Inference principles to enhance the completeness and semantic quality of your knowledge graphs, especially when dealing with complex, experience-driven processes like manufacturing assembly or repair.

Key insights

A neuro-symbolic framework improves tacit knowledge extraction for machine-interpretable knowledge graphs.

Principles

• Tacit knowledge is critical for human expertise.
• Neuro-symbolic AI enhances knowledge engineering.

Method

The framework combines Logic-Augmented Generation with an Active-Inference-inspired approach to build ontology-grounded Knowledge Graphs, evaluated using manufacturing repair procedures from videos.

In practice

• Formalize implicit assumptions in procedures.
• Improve knowledge transfer in manufacturing.

Topics

• Tacit Knowledge Extraction
• Logic-Augmented Generation
• Active Inference
• Knowledge Graph Construction
• Neuro-Symbolic AI

Best for: AI Scientist, Research Scientist, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.