SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion

• URL: http://arxiv.org/abs/2510.00279v1
• Date: Tue, 30 Sep 2025 20:59:22 GMT
• Title: SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion
• Authors: Trung Hoang Le, Tran Cao Son, Huiping Cao,
• Abstract summary: We introduce SLogic, a framework that assigns query-dependent scores to logical rules.<n>The core of SLogic is a scoring function that utilizes the subgraph centered on a query's head entity.<n>By leveraging local subgraph context, SLogic consistently outperforms state-of-the-art baselines.
• Score: 2.8845104295670017
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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