Event-Enhanced Learning for KG Completion

Martin Ringsquandl, Evgeny Kharlamov, Daria Stepanova, Marcel Hildebrandt, Steffen Lamparter, Raffaello Lepratti, Ian Horrocks, Peer Kröger

Abstract: 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 relational learning can be enhanced with background of a special kind: event logs, that are sequences of entities that may occur in the graph. Events naturally appear in many important applications as background. 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.