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

Abstract:Logical rule-based methods offer an interpretable approach to knowledge graph completion (KGC) by capturing compositional relationships in the form of human-readable inference rules. While existing logical rule-based methods learn rule confidence scores, they typically assign a global weight to each rule schema, applied uniformly across the graph. This is a significant limitation, as a rule's importance often varies depending on the specific query instance. 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 context-aware scoring function. This framework is in line with the specificity principle in commonsense reasoning. This function determines the importance of a rule by analyzing the subgraph locally defined by the query's head entity, thereby enabling a differentiated weighting of rules specific to their local query contexts. Extensive experiments on benchmark datasets show that SLogic achieves competitive performance among rule-based methods. It also generates query-dependent, human-readable logical rules that serve as explicit explanations for its inferences.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2510.00279 [cs.LG]

(or arXiv:2510.00279v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2510.00279 Focus to learn more arXiv-issued DOI via DataCite

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