Regularizing relation representations by first-order implications

Abstract

Methods for automated knowledge base construction often rely on trained fixed-length vector representations of relations and entities to predict facts. Recent work showed that such representations can be regularized to inject first-order logic formulae. This enables to incorporate domain-knowledge for improved prediction of facts, especially for uncommon relations. However, current approaches rely on propositionalization of formulae and thus do not scale to large sets of formulae or knowledge bases with many facts. Here we propose a method that imposes first-order constraints directly on relation representations, avoiding costly grounding of formulae. We show that our approach works well for implications between pairs of relations on artificial datasets.