OAK -- Onboarding with Actionable Knowledge
This addresses a critical issue for companies in manufacturing by enabling better decision-making on the shop floor, though it appears incremental as it builds on existing techniques like knowledge graphs and LLMs.
The paper tackles the problem of losing unstructured expertise when skilled operators leave by proposing a method that combines knowledge graph embeddings and multi-modal interfaces to collect and retrieve actionable knowledge, with a proof-of-concept developed for quality control in high precision manufacturing.
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.