  We present KBLRN, a framework for end-to-end learning of knowledge base
representations from latent, relational, and numerical features. KBLRN
integrates feature types with a novel combination of neural representation
learning and probabilistic product of experts models. To the best of our
knowledge, KBLRN is the first approach that learns representations of knowledge
bases by integrating latent, relational, and numerical features. We show that
instances of KBLRN outperform existing methods on a range of knowledge base
completion tasks. We contribute a novel data sets enriching commonly used
knowledge base completion benchmarks with numerical features. The data sets are
available under a permissive BSD-3 license. We also investigate the impact
numerical features have on the KB completion performance of KBLRN.
