Paper 77 (Research track)

Event-Enhanced Learning for Knowledge Graph Completion

Author(s): Martin Ringsquandl, Evgeny Kharlamov, Daria Stepanova, Marcel Hildebrandt, Steffen Lamparter, Raffaello Lepratti, Ian Horrocks, Peer Kroeger

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 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.

Keywords: Knowledge Graph; Representation Learning; Event Logs

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