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.
@article{arxiv.1911.07893,
title = {Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition},
author = {Chengjin Xu and Mojtaba Nayyeri and Fouad Alkhoury and Hamed Shariat Yazdi and Jens Lehmann},
journal= {arXiv preprint arXiv:1911.07893},
year = {2020}
}