RuleTopo: Neuro-Symbolic Synergy for Inductive Knowledge Graph Completion

Knowledge Graph Completion (KGC) infers missing links to enrich incomplete knowledge graphs, categorized into transductive and inductive scenarios. Inductive KGC poses significant challenges, particularly in fully inductive settings with entirely new relations, and still lacks effective modeling paradigms. Currently, rule-based approaches rely on known relation semantics and struggle to generalize to new relations, while relation-graph methods mainly model statistical co-occurrence structures and overlook transferable logical constraints between relations, thus performing poorly when entities and relations are completely unseen. To tackle these issues, we propose RuleTopo, a novel Graph Neural Network-based approach that fuses rule learning with a global topology relation graph, supporting fully, semi-, and traditional inductive KGC. Our approach extracts Horn rules and maps them into continuous embeddings, explicitly integrating symbolic rules into the attention mechanism of a relational graph neural network to enable rule-guided topological message passing. Furthermore, we introduce a rule-consistency loss to constrain the logical structure of relation embeddings during training continuously. Experiments show that on the -100/-50/-0 inductive splits of the FB15K-237 and NELL995 datasets, RuleTopo demonstrates higher efficiency and interpretability. This approach demonstrates a scalable, highly generalizable neuro-symbolic cooperative paradigm, offering a new direction for inductive KGC.