Learning Typed Rules over Knowledge Graphs
Hong Lin Wu, Zhe Wang, Kewen Wang, Yi-Dong Shen · 2022
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 scalability and rule quality. This paper presents a new method for learning typed rules by employing entity class 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.