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Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamentaland challenging task. Previous works have approached this problem by augmentingmethods for static knowledge graphs to leverage time-dependent representations.However, these methods do not explicitly leverage multi-hop structuralinformation and temporal facts from recent time steps to enhance theirpredictions. Additionally, prior work does not explicitly address the temporalsparsity and variability of entity distributions in TKGs. We propose theTemporal Message Passing (TeMP) framework to address these challenges bycombining graph neural networks, temporal dynamics models, data imputation andfrequency-based gating techniques. Experiments on standard TKG tasks show thatour approach provides substantial gains compared to the previous state of theart, achieving a 10.7% average relative improvement in Hits@10 across threestandard benchmarks. Our analysis also reveals important sources of variabilityboth within and across TKG datasets, and we introduce several simple but strongbaselines that outperform the prior state of the art in certain settings.

TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion