  A Temporal Knowledge Graph (TKG) is a sequence of KGs corresponding to
different timestamps. TKG reasoning aims to predict potential facts in the
future given the historical KG sequences. One key of this task is to mine and
understand evolutional patterns of facts from these sequences. The evolutional
patterns are complex in two aspects, length-diversity and time-variability.
Existing models for TKG reasoning focus on modeling fact sequences of a fixed
length, which cannot discover complex evolutional patterns that vary in length.
Furthermore, these models are all trained offline, which cannot well adapt to
the changes of evolutional patterns from then on. Thus, we propose a new model,
called Complex Evolutional Network (CEN), which uses a length-aware
Convolutional Neural Network (CNN) to handle evolutional patterns of different
lengths via an easy-to-difficult curriculum learning strategy. Besides, we
propose to learn the model under the online setting so that it can adapt to the
changes of evolutional patterns over time. Extensive experiments demonstrate
that CEN obtains substantial performance improvement under both the traditional
offline and the proposed online settings.
