  Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs
has been widely explored. However, reasoning over Temporal KG (TKG) that
predicts facts in the future is still far from resolved. The key to predict
future facts is to thoroughly understand the historical facts. A TKG is
actually a sequence of KGs corresponding to different timestamps, where all
concurrent facts in each KG exhibit structural dependencies and temporally
adjacent facts carry informative sequential patterns. To capture these
properties effectively and efficiently, we propose a novel Recurrent Evolution
network based on Graph Convolution Network (GCN), called RE-GCN, which learns
the evolutional representations of entities and relations at each timestamp by
modeling the KG sequence recurrently. Specifically, for the evolution unit, a
relation-aware GCN is leveraged to capture the structural dependencies within
the KG at each timestamp. In order to capture the sequential patterns of all
facts in parallel, the historical KG sequence is modeled auto-regressively by
the gate recurrent components. Moreover, the static properties of entities such
as entity types, are also incorporated via a static graph constraint component
to obtain better entity representations. Fact prediction at future timestamps
can then be realized based on the evolutional entity and relation
representations. Extensive experiments demonstrate that the RE-GCN model
obtains substantial performance and efficiency improvement for the temporal
reasoning tasks on six benchmark datasets. Especially, it achieves up to
11.46\% improvement in MRR for entity prediction with up to 82 times speedup
comparing to the state-of-the-art baseline.
