  Forecasting future events is a fundamental challenge for temporal knowledge
graphs (tKG). As in real life predicting a mean function is most of the time
not sufficient, but the question remains how confident can we be about our
prediction? Thus, in this work, we will introduce a novel graph neural network
architecture (WGP-NN) employing (weighted) Gaussian processes (GP) to jointly
model the temporal evolution of the occurrence probability of events and their
time-dependent uncertainty. Especially we employ Gaussian processes to model
the uncertainty of future links by their ability to predict predictive
variance. This is in contrast to existing works, which are only able to express
uncertainties in the learned entity representations. Moreover, WGP-NN can model
parameter-free complex temporal and structural dynamics of tKGs in continuous
time. We further demonstrate the model's state-of-the-art performance on two
real-world benchmark datasets.
