  Temporal knowledge prediction is a crucial task for the event early warning
that has gained increasing attention in recent years, which aims to predict the
future facts by using relevant historical facts on the temporal knowledge
graphs. There are two main difficulties in this prediction task. First, from
the historical facts point of view, how to model the evolutionary patterns of
the facts to predict the query accurately. Second, from the query perspective,
how to handle the two cases where the query contains seen and unseen entities
in a unified framework. Driven by the two problems, we propose a novel adaptive
pseudo-siamese policy network for temporal knowledge prediction based on
reinforcement learning. Specifically, we design the policy network in our model
as a pseudo-siamese policy network that consists of two sub-policy networks. In
sub-policy network I, the agent searches for the answer for the query along the
entity-relation paths to capture the static evolutionary patterns. And in
sub-policy network II, the agent searches for the answer for the query along
the relation-time paths to deal with unseen entities. Moreover, we develop a
temporal relation encoder to capture the temporal evolutionary patterns.
Finally, we design a gating mechanism to adaptively integrate the results of
the two sub-policy networks to help the agent focus on the destination answer.
To assess our model performance, we conduct link prediction on four benchmark
datasets, the experimental results demonstrate that our method obtains
considerable performance compared with existing methods.
