Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding

Author: C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann
Journal: ISWC 2020: The Semantic Web – ISWC 2020
Year: 2020

Citation information

C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, J. Lehmann,
ISWC 2020: The Semantic Web – ISWC 2020,
Springer, Cham,

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 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.