  Learning embeddings of entities and relations existing in knowledge bases
allows the discovery of hidden patterns in data. In this work, we examine the
geometrical space's contribution to the task of knowledge base completion. We
focus on the family of translational models, whose performance has been
lagging, and propose a model, dubbed HyperKG, which exploits the hyperbolic
space in order to better reflect the topological properties of knowledge bases.
We investigate the type of regularities that our model can capture and we show
that it is a prominent candidate for effectively representing a subset of
Datalog rules. We empirically show, using a variety of link prediction
datasets, that hyperbolic space allows to narrow down significantly the
performance gap between translational and bilinear models.
