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

MetaTKG: Learning Evolutionary Meta-Knowledge for Temporal Knowledge Graph Reasoning