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Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGshas been widely explored. However, reasoning over Temporal KG (TKG) thatpredicts facts in the future is still far from resolved. The key to predictfuture facts is to thoroughly understand the historical facts. A TKG isactually a sequence of KGs corresponding to different timestamps, where allconcurrent facts in each KG exhibit structural dependencies and temporallyadjacent facts carry informative sequential patterns. To capture theseproperties effectively and efficiently, we propose a novel Recurrent Evolutionnetwork based on Graph Convolution Network (GCN), called RE-GCN, which learnsthe evolutional representations of entities and relations at each timestamp bymodeling the KG sequence recurrently. Specifically, for the evolution unit, arelation-aware GCN is leveraged to capture the structural dependencies withinthe KG at each timestamp. In order to capture the sequential patterns of allfacts in parallel, the historical KG sequence is modeled auto-regressively bythe gate recurrent components. Moreover, the static properties of entities suchas entity types, are also incorporated via a static graph constraint componentto obtain better entity representations. Fact prediction at future timestampscan then be realized based on the evolutional entity and relationrepresentations. Extensive experiments demonstrate that the RE-GCN modelobtains substantial performance and efficiency improvement for the temporalreasoning tasks on six benchmark datasets. Especially, it achieves up to11.46\% improvement in MRR for entity prediction with up to 82 times speedupcomparing to the state-of-the-art baseline.

Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning