  Inferring missing facts in temporal knowledge graphs is a critical task and
has been widely explored. Extrapolation in temporal reasoning tasks is more
challenging and gradually attracts the attention of researchers since no direct
history facts for prediction. Previous works attempted to apply evolutionary
representation learning to solve the extrapolation problem. However, these
techniques do not explicitly leverage various time-aware attribute
representations, i.e. the reasoning performance is significantly affected by
the history length. To alleviate the time dependence when reasoning future
missing facts, we propose a memory-triggered decision-making (MTDM) network,
which incorporates transient memories, long-short-term memories, and deep
memories. Specifically, the transient learning network considers transient
memories as a static knowledge graph, and the time-aware recurrent evolution
network learns representations through a sequence of recurrent evolution units
from long-short-term memories. Each evolution unit consists of a structural
encoder to aggregate edge information, a time encoder with a gating unit to
update attribute representations of entities. MTDM utilizes the crafted
residual multi-relational aggregator as the structural encoder to solve the
multi-hop coverage problem. We also introduce the dissolution learning
constraint for better understanding the event dissolution process. Extensive
experiments demonstrate the MTDM alleviates the history dependence and achieves
state-of-the-art prediction performance. Moreover, compared with the most
advanced baseline, MTDM shows a faster convergence speed and training speed.
