SLogic: Subgraph-Informed Logical Rule Learning for Knowledge Graph Completion
This work improves interpretable knowledge graph completion for AI applications by making rule importance dynamic, though it is incremental as it builds on existing rule-based methods.
The paper tackled the problem of logical rule-based knowledge graph completion by addressing the limitation of fixed confidence scores for rules, introducing SLogic which assigns query-dependent scores using subgraph context, resulting in consistent outperformance of state-of-the-art baselines in experiments.
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.