  In this paper, we investigate a realistic but underexplored problem, called
few-shot temporal knowledge graph reasoning, that aims to predict future facts
for newly emerging entities based on extremely limited observations in evolving
graphs. It offers practical value in applications that need to derive instant
new knowledge about new entities in temporal knowledge graphs (TKGs) with
minimal supervision. The challenges mainly come from the few-shot and time
shift properties of new entities. First, the limited observations associated
with them are insufficient for training a model from scratch. Second, the
potentially dynamic distributions from the initially observable facts to the
future facts ask for explicitly modeling the evolving characteristics of new
entities. We correspondingly propose a novel Meta Temporal Knowledge Graph
Reasoning (MetaTKGR) framework. Unlike prior work that relies on rigid
neighborhood aggregation schemes to enhance low-data entity representation,
MetaTKGR dynamically adjusts the strategies of sampling and aggregating
neighbors from recent facts for new entities, through temporally supervised
signals on future facts as instant feedback. Besides, such a meta temporal
reasoning procedure goes beyond existing meta-learning paradigms on static
knowledge graphs that fail to handle temporal adaptation with large entity
variance. We further provide a theoretical analysis and propose a temporal
adaptation regularizer to stabilize the meta temporal reasoning over time.
Empirically, extensive experiments on three real-world TKGs demonstrate the
superiority of MetaTKGR over state-of-the-art baselines by a large margin.
