用知识图谱和多模态界面，把老师傅经验变可查可用的决策支持。
OAK -- Onboarding with Actionable Knowledge
- 用知识图谱+多模态界面收集散落的专家经验
- 结合LLM提升查询理解，实现车间实时决策支持
- 已在高精度制造质检场景验证可行性
熟练操作员离职导致的知识流失是企业面临的重大问题，这些经验往往分散且无结构。本文提出一种新方法，结合知识图谱嵌入与多模态接口，实现对专业知识的采集与可操作性检索，支持产线决策。此外，我们利用大语言模型（LLMs）增强查询理解并生成适配答案。通过高精度制造中的质量控制案例，展示了该方法的可行性与有效性。
原文摘要 · Abstract (English)
The loss of knowledge when skilled operators leave poses a critical issue for companies. This know-how is diverse and unstructured. We propose a novel method that combines knowledge graph embeddings and multi-modal interfaces to collect and retrieve expertise, making it actionable. Our approach supports decision-making on the shop floor. Additionally, we leverage LLMs to improve query understanding and provide adapted answers. As application case studies, we developed a proof-of-concept for quality control in high precision manufacturing.
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