KBLRN : End-to-End Learning of Knowledge Base Representations with Latent, Relational, and Numerical Features
This addresses knowledge base completion for AI applications, but appears incremental as it combines existing techniques with numerical feature integration.
The authors tackled the problem of knowledge base completion by developing KBLRN, an end-to-end framework that integrates latent, relational, and numerical features, which outperformed existing methods on various tasks.
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.