Executable Knowledge Graphs for Machine Learning: A Bosch Case of Welding Monitoring

Zhuoxun Zheng, Baifan Zhou, Dongzhuoran Zhou, Xianda Zheng, Gong Cheng, Ahmet Soylu, Evgeny Kharlamov

Abstract: Data analysis including ML are essential to extract insights from production data in modern industries. However, industrial ML is affected by: the low transparency of ML towards non-ML experts; poor and non-unified descriptions of ML practices for reviewing or comprehension; ad hoc fashion of ML solutions tailored to specific applications, which affects their re-usability. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG). It relies on semantic technologies to formally encode ML knowledge and solutions in KGs, which can be translated to executable scripts in a reusable and modularised fashion. In addition, the executable KGs also serve as common language between ML experts and non-ML experts, and facilitate their communication. We evaluated our system extensively with an impactful industrial use case at Bosch, including a user study, workshops and scalability evaluation. The evaluation demonstrates the system offers a user-friendly way for even non-ML experts to discuss, customise, and reuse ML methods.