用逻辑增强生成与主动推理，把隐性经验转化为可查询的知识图谱。
Tacit Knowledge Extraction via Logic Augmented Generation and Active Inference
- 结合逻辑增强生成与主动推理，构建基于本体的知识图谱。
- 在制造维修场景中提升知识图谱的完整性和语义质量。
- 适合工业领域知识工程与自动化流程建模的研究者。
隐性知识在人类专业能力中起核心作用，但在机器可读形式中仍难以捕捉、形式化和复用。这一挑战在程序性领域尤为突出，因为成功执行不仅依赖显式指令，还依赖于未记录的隐含假设、上下文约束、身体技能及经验判断。当前知识工程流程难以将隐性且以过程为中心的知识转化为可查询、可验证、可推理的正式表示。本文提出一种神经符号框架，结合逻辑增强生成与受主动推理启发的方法，实现基于本体的知识图谱构建。我们在制造领域进行知识迁移案例研究，使用来自操作视频的类装配维修流程作为可复现的代理领域。结果表明，该方法显著提升了知识图谱的完整性与语义质量，推动了工业领域的神经符号知识工程发展。
原文摘要 · Abstract (English)
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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