  Temporal knowledge graph completion (TKGC) aims to predict the missing links
among the entities in a temporal knwoledge graph (TKG). Most previous TKGC
methods only consider predicting the missing links among the entities seen in
the training set, while they are unable to achieve great performance in link
prediction concerning newly-emerged unseen entities. Recently, a new task,
i.e., TKG few-shot out-of-graph (OOG) link prediction, is proposed, where TKGC
models are required to achieve great link prediction performance concerning
newly-emerged entities that only have few-shot observed examples. In this work,
we propose a TKGC method FITCARL that combines few-shot learning with
reinforcement learning to solve this task. In FITCARL, an agent traverses
through the whole TKG to search for the prediction answer. A policy network is
designed to guide the search process based on the traversed path. To better
address the data scarcity problem in the few-shot setting, we introduce a
module that computes the confidence of each candidate action and integrate it
into the policy for action selection. We also exploit the entity concept
information with a novel concept regularizer to boost model performance.
Experimental results show that FITCARL achieves stat-of-the-art performance on
TKG few-shot OOG link prediction.
