  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.
