  Knowledge graphs have been demonstrated to be an effective tool for numerous
intelligent applications. However, a large amount of valuable knowledge still
exists implicitly in the knowledge graphs. To enrich the existing knowledge
graphs, recent years witness that many algorithms for link prediction and
knowledge graphs embedding have been designed to infer new facts. But most of
these studies focus on the static knowledge graphs and ignore the temporal
information that reflects the validity of knowledge. Developing the model for
temporal knowledge graphs completion is an increasingly important task. In this
paper, we build a new tensor decomposition model for temporal knowledge graphs
completion inspired by the Tucker decomposition of order 4 tensor. We
demonstrate that the proposed model is fully expressive and report
state-of-the-art results for several public benchmarks. Additionally, we
present several regularization schemes to improve the strategy and study their
impact on the proposed model. Experimental studies on three temporal datasets
(i.e. ICEWS2014, ICEWS2005-15, GDELT) justify our design and demonstrate that
our model outperforms baselines with an explicit margin on link prediction
task.
