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We present KBLRN, a framework for end-to-end learning of knowledge baserepresentations from latent, relational, and numerical features. KBLRNintegrates feature types with a novel combination of neural representationlearning and probabilistic product of experts models. To the best of ourknowledge, KBLRN is the first approach that learns representations of knowledgebases by integrating latent, relational, and numerical features. We show thatinstances of KBLRN outperform existing methods on a range of knowledge basecompletion tasks. We contribute a novel data sets enriching commonly usedknowledge base completion benchmarks with numerical features. The data sets areavailable under a permissive BSD-3 license. We also investigate the impactnumerical features have on the KB completion performance of KBLRN.

KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features