论文标题
温度：时间知识图完成的时间消息传递
TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
论文作者
论文摘要
在时间知识图（TKGS）中推断缺失的事实是一项基本且具有挑战性的任务。以前的工作通过增强静态知识图的方法来解决这个问题，以利用时间依赖性表示。但是，这些方法并未明确利用最近时间步骤的多跳结构信息和时间事实来增强其预测。此外，先前的工作并未明确解决TKG中实体分布的时间稀疏性和可变性。我们提出了时间消息传递（TEMP）框架，以通过组合图形神经网络，时间动力学模型，数据插补和基于频率的门控技术来应对这些挑战。标准TKG任务的实验表明，与先前的最新状态相比，我们的方法可提供可观的增长，在三个标准基准中，@10的平均命中率平均相对提高了10.7％。我们的分析还揭示了TKG数据集内部和整个TKG数据集的重要可变性来源，我们介绍了几个简单但强大的基线，在某些情况下，在某些情况下的先前状态都优于先前的现状。
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.
