  Smart factories are equipped with machines that can sense their manufacturing
environments, interact with each other, and control production processes.
Smooth operation of such factories requires that the machines and engineering
personnel that conduct their monitoring and diagnostics share a detailed common
industrial knowledge about the factory, e.g., in the form of knowledge graphs.
Creation and maintenance of such knowledge is expensive and requires
automation. In this work we show how machine learning that is specifically
tailored towards industrial applications can help in knowledge graph
completion. In particular, we show how knowledge completion can benefit from
event logs that are common in smart factories. We evaluate this on the
knowledge graph from a real world-inspired smart factory with encouraging
results.
