CRule: Category-Aware Symbolic Multihop Reasoning on Knowledge Graphs

Multihop reasoning is essential in knowledge graph (KG) research and applications. Current methods rely on specific KG entities, while human cognition operates at a more abstract level. This article proposes a category-aware rule-based (CRule) approach for symbolic multihop reasoning. Specifically, given a KG, CRule first categorizes entities and constructs a category-aware KG; it then uses rules retrieved from the categorized KG to perform multihop reasoning on the original KG. Experiments on five datasets show that CRule is simple, is effective, and combines the advantages of symbolic and neural network methods. It overcomes symbolic reasoning's complexity limitations, can perform reasoning on KGs of more than 300,000 edges, and can be three times more efficient than neural network models.