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

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 set enriching commonly used knowledge base completion benchmarks with numerical features. We also investigate the impact numerical features have on KB completion performance.