Event-Enhanced Learning for Knowledge Graph Completion

author: Martin Ringsquandl, Ludwig-Maximilians Universität
published: July 10, 2018,   recorded: June 2018,   views: 4
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Description

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.

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Download slides icon Download slides: eswc2018_ringsquandl_graph_completion_01.pdf (1.7¬†MB)


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