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

提出了 Temporal Message Passing (TeMP) 框架，通过结合图神经网络、时间动力学模型、数据填充和基于频率的门控技术来解决时态知识图中缺失事实的问题，实验表明我们的方法相较于现有的方法提高了 10.7％的平均相对精度，并提出了几个简单却强大的基线模型在某些情况下可以超越现有的最佳结果。