  Temporal Knowledge Graphs (TKGs) have been developed and used in many
different areas. Reasoning on TKGs that predicts potential facts (events) in
the future brings great challenges to existing models. When facing a prediction
task, human beings usually search useful historical information (i.e., clues)
in their memories and then reason for future meticulously. Inspired by this
mechanism, we propose CluSTeR to predict future facts in a two-stage manner,
Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue
searching stage, CluSTeR learns a beam search policy via reinforcement learning
(RL) to induce multiple clues from historical facts. At the temporal reasoning
stage, it adopts a graph convolution network based sequence method to deduce
answers from clues. Experiments on four datasets demonstrate the substantial
advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the
clues found by CluSTeR further provide interpretability for the results.
