Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Problem: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.
Solution:
Submitted in EMNLP