Xu, ChengjinChengjinXuNayyeri, MojtabaMojtabaNayyeriAlkhoury, FouadFouadAlkhouryYazdi, Hamed ShariatHamed ShariatYazdiLehmann, JensJensLehmann2022-03-142022-03-142020https://publica.fraunhofer.de/handle/publica/40976010.1007/978-3-030-62419-4_37Knowledge 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 besides 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 significantly outperforms the state-of-the-art KGE models and the existing temporal KGE models on link prediction over four temporal KGs.entemporal knowledge graphknowledge representation and reasoningtime series decomposition005006629conference paper