  Knowledge Graph (KG) embedding has attracted more attention in recent years.
Most KG embedding models learn from time-unaware triples. However, the
inclusion of temporal information beside triples would further improve the
performance of a KGE model. In this regard, we propose ATiSE, a temporal KG
embedding model which incorporates time information into entity/relation
representations by using Additive Time Series decomposition. Moreover,
considering the temporal uncertainty during the evolution of entity/relation
representations over time, we map the representations of temporal KGs into the
space of multi-dimensional Gaussian distributions. The mean of each
entity/relation embedding at a time step shows the current expected position,
whereas its covariance (which is temporally stationary) represents its temporal
uncertainty. Experimental results show that ATiSE chieves the state-of-the-art
on link prediction over four temporal KGs.
