Task: Summarization
Input (English): Knowledge Graph Embedding (KGE) has attracted more attention in recent years. Most of KGE 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 LiTSE, a temporal KGE model which incorporates time information
 into entity/relation representations by using linear 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 stationary over time) represents
 its temporal uncertainty. Experiments show that LiTSE not only achieves the state-of- the-art on link prediction in temporal KGs, but also has the ability to 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.
Output (English): Submitted in EMNLP