Learning Typed Rules over Knowledge Graphs

Abstract

Rule learning from large datasets has regained extensive interest as rules are useful for developing explainable approaches to many applications in knowledge graphs. However, existing methods for rule learning are still limited in terms of rule expressivity and rule quality. This paper presents a new method for learning typed rules by employing type information. Our experimental evaluation shows the superiority of our system compared to state-of-the-art rule learners. In particular, we demonstrate the usefulness of typed rules in reasoning for link prediction.