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Large knowledge graphs often grow to store temporal facts that model thedynamic relations or interactions of entities along the timeline. Since suchtemporal knowledge graphs often suffer from incompleteness, it is important todevelop time-aware representation learning models that help to infer themissing temporal facts. While the temporal facts are typically evolving, it isobserved that many facts often show a repeated pattern along the timeline, suchas economic crises and diplomatic activities. This observation indicates that amodel could potentially learn much from the known facts appeared in history. Tothis end, we propose a new representation learning model for temporal knowledgegraphs, namely CyGNet, based on a novel timeaware copy-generation mechanism.CyGNet is not only able to predict future facts from the whole entityvocabulary, but also capable of identifying facts with repetition andaccordingly predicting such future facts with reference to the known facts inthe past. We evaluate the proposed method on the knowledge graph completiontask using five benchmark datasets. Extensive experiments demonstrate theeffectiveness of CyGNet for predicting future facts with repetition as well asde novo fact prediction.

Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks