OAK -- Onboarding with Actionable Knowledge
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 16 references that are in the map. First posted on arXiv 25 Jun 2025.
Builds on
3 of 16 references.
References · 3
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332
- 2018Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendationcited by 39
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