Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most of 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 A TiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using A dditive Time Se ries 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 multidimensional 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 A TiSE not only achieves the state-of-the-art on link prediction over temporal KGs, but also can predict the occurrence time of facts with missing time annotations, as well as the existence of future events. To the best of our knowledge, no other model is capable to perform all these tasks.