Statistical learning of relations between entities is a popular approach to address the problem of missing data in Knowledge Graphs. In this work we study how this learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Such background naturally occurs in many important applications. We propose various embedding models that combine entities of a Knowledge Graph and event logs. Our evaluation shows that our approach outperforms state-of-the-art baselines on real-world manufacturing and road traffic Knowledge Graphs, as well as in a controlled scenario that mimics manufacturing processes.