  Research on link prediction in knowledge graphs has mainly focused on static
multi-relational data. In this work we consider temporal knowledge graphs where
relations between entities may only hold for a time interval or a specific
point in time. In line with previous work on static knowledge graphs, we
propose to address this problem by learning latent entity and relation type
representations. To incorporate temporal information, we utilize recurrent
neural networks to learn time-aware representations of relation types which can
be used in conjunction with existing latent factorization methods. The proposed
approach is shown to be robust to common challenges in real-world KGs: the
sparsity and heterogeneity of temporal expressions. Experiments show the
benefits of our approach on four temporal KGs. The data sets are available
under a permissive BSD-3 license 1.
