Reasoning over Temporal Knowledge Graphs (TKGs) aims to predict future facts based on given history. One of the key challenges for prediction is to learn the evolution of facts. Most existing works focus on exploring evolutionary information in history to obtain effective temporal embeddings for entities and relations, but they ignore the variation in evolution patterns of facts, which makes them struggle to adapt to future data with different evolution patterns. Moreover, new entities continue to emerge along with the evolution of facts over time. Since existing models highly rely on historical information to learn embeddings for entities, they perform poorly on such entities with little historical information. To tackle these issues, we propose a novel Temporal Meta-learning framework for TKG reasoning, MetaTKG for brevity. Specifically, our method regards TKG prediction as many temporal meta-tasks, and utilizes the designed Temporal Meta-learner to learn evolutionary meta-knowledge from these meta-tasks. The proposed method aims to guide the backbones to learn to adapt quickly to future data and deal with entities with little historical information by the learned meta-knowledge. Specially, in temporal meta-learner, we design a Gating Integration module to adaptively establish temporal correlations between meta-tasks. Extensive experiments on four widely-used datasets and three backbones demonstrate that our method can greatly improve the performance.

翻译：时序知识图谱（TKG）推理旨在基于给定历史预测未来事实。其中的关键挑战之一在于学习事实的演化规律。现有工作大多聚焦于探索历史中的演化信息以获取实体和关系的有效时序表示，却忽略了事实演化模式的多样性，导致难以适应具有不同演化模式的未来数据。此外，随着时间推移，新实体不断伴随事实演化而涌现。由于现有模型高度依赖历史信息学习实体表示，它们在历史信息匮乏的实体上表现不佳。为解决这些问题，我们提出了一种面向TKG推理的新型时序元学习框架，简称MetaTKG。具体而言，我们将TKG预测视为多个时序元任务，并利用所设计的时序元学习器从这些元任务中学习演化元知识。该方法旨在引导主模型通过学习所得元知识快速适应未来数据，并处理历史信息匮乏的实体。特别地，在时序元学习器中，我们设计了门控集成模块以自适应建立元任务间的时序关联。在四个广泛使用的数据集和三个主模型上的大量实验表明，本文方法能显著提升性能。