Learning interpretable relationships between entities, relations and concepts via Bayesian structure learning on open domain facts

Abstract

Concept graphs are created as universal taxonomies for text understanding in the open domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both English and Chinese datasets.