Learning Temporal Rules from Knowledge Graph Streams

Knowledge Graphs (KGs) are a prevailing data management approach and have found extensive applications in recent years. While several methods have been proposed for learning schema information for KGs in the form of logical rules, they are not suitable for KGs with constantly evolving data. This paper makes the first attempt to address the problem by presenting an approach to learning temporal rules from KG streams. The learned temporal rules can be applied in link prediction and event prediction over KG streams. Based on the proposed method, a system StreamLearner has been implemented. Our experimental results show that StreamLearner is effective and efficient in learning temporal rules on real-life datasets and significantly outperforms some state-ofthe-art systems that do not account for temporal knowledge

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