  Large knowledge graphs often grow to store temporal facts that model the
dynamic relations or interactions of entities along the timeline. Since such
temporal knowledge graphs often suffer from incompleteness, it is important to
develop time-aware representation learning models that help to infer the
missing temporal facts. While the temporal facts are typically evolving, it is
observed that many facts often show a repeated pattern along the timeline, such
as economic crises and diplomatic activities. This observation indicates that a
model could potentially learn much from the known facts appeared in history. To
this end, we propose a new representation learning model for temporal knowledge
graphs, namely CyGNet, based on a novel timeaware copy-generation mechanism.
CyGNet is not only able to predict future facts from the whole entity
vocabulary, but also capable of identifying facts with repetition and
accordingly predicting such future facts with reference to the known facts in
the past. We evaluate the proposed method on the knowledge graph completion
task using five benchmark datasets. Extensive experiments demonstrate the
effectiveness of CyGNet for predicting future facts with repetition as well as
de novo fact prediction.
