Title: SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion
Primary area: neurosymbolic & hybrid AI systems (physics-informed, logic & formal reasoning, etc.)
Keywords: knowledge graph, logical rules learning, knowledge graph completion, knowledge graph reasoning, neuro-symbolic

Abstract: Logical rule-based methods offer an interpretable approach to knowledge graph completion by capturing compositional relationships in the form of human-readable inference rules. However, current approaches typically treat logical rules as universal, assigning each rule a fixed confidence score that ignores query-specific context. This is a significant limitation, as a rule's importance can vary depending on the query. To address this, we introduce SLogic (Subgraph-Informed Logical Rule learning), a novel framework that assigns query-dependent scores to logical rules. The core of SLogic is a scoring function that utilizes the subgraph centered on a query's head entity, allowing the significance of each rule to be assessed dynamically. Extensive experiments on benchmark datasets show that by leveraging local subgraph context, SLogic consistently outperforms state-of-the-art baselines, including both embedding-based and rule-based methods.

TL;DR: SLogic is a novel neuro-symbolic framework that improves knowledge graph completion by using a GNN on local subgraphs to learn dynamic, context-aware scores for logical rules.