arXiv AI August 25, 2026 By Trung Hoang Le, Tran Cao Son, Ishtiaq Ahmed, Huiping Cao

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

SLogic introduces a subgraph-informed approach to logical rule learning for knowledge graph completion, assigning query-dependent scores to rules instead of a single global weight. The framework uses a context-aware scoring function that evaluates the importance of a rule based on the local subgraph around the query’s head entity, aligning with the specificity principle of commonsense reasoning. Experiments on benchmark datasets demonstrate that SLogic performs competitively with other rule-based methods while producing human-readable, query-specific explanations.

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benchmarks

arXiv AI

Jun 6

Generating Graph-Like Logical Rules for Knowledge Graph Reasoning via Diffusion Models

arXiv:2605. 30747v2 Announce Type: replace Abstract: Logical rules constitute a cornerstone of knowledge graph (KG) reasoning, valued for their interpretability and ability to model relational patterns.

By Haoxiang Cheng, Yunfei Wang, Chao Chen, Kewei Cheng, Zhipeng Lin, Haoxuan Li, Changjun Fan, Shixuan Liu

diffusion reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Aug 11

Probabilistic Circuits for Knowledge Graph Completion with Reduced Rule Sets

arXiv:2508. 06706v2 Announce Type: replace Abstract: Rule-based methods for knowledge graph completion provide explainable results, but often require tens of thousands of rules to achieve competitive performance.

By Jaikrishna Manojkumar Patil, Nathaniel Lee, Al Mehdi Saadat Chowdhury, YooJung Choi, Paulo Shakarian

benchmarks

arXiv Machine Learning

Jun 5

Q-GNN: Query-Conditioned Graph Neural Networks with Type Awareness for Knowledge Graph Completion

arXiv:2606. 05639v1 Announce Type: new Abstract: Knowledge Graph Completion (KGC) aims at predicting missing triplets from incomplete knowledge graphs, which is crucial for downstream applications.

By Dongxiao He, Ruqiong Zhang, Zhizhi Yu, Ling Ding, Di Jin, Guangquan Xu, Zhiyong Feng

llms benchmarks

arXiv AI

Jun 24

Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules

arXiv:2508. 10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs.

By Nasim Shirvani-Mahdavi, Chengkai Li

llms fine-tuning

arXiv AI

Aug 19

From Abductive Explanations to Global Logical Rules for Node Classification in SGCs

The paper introduces a logic-based framework that extracts global logical rules for node classification in Simple Graph Convolution (SGC) networks. It uses minimal abductive explanations—small sets of node-feature pairs that preserve a node’s predicted class—as an intermediate step. Decision trees trained on these explanations yield compact global rules that retain high fidelity to the original SGC model, as demonstrated on benchmark datasets.

By Bryan Lima Cavalcante, Thiago Alves Rocha

benchmarks

arXiv AI

Aug 19

Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning

The paper introduces the Structure-Internalized Rule Language Model (SIRLM) to improve Knowledge Graph Reasoning (KGR) by addressing the mismatch between KG structural context and Large Language Model (LLM) parametric knowledge. SIRLM centers on a Structure-Internalized Rule Generator (SIRG) that uses in-context learning, a structural relation memory, a KG tokenizer, and a neuro-symbolic reasoner to generate structural rules and provide faithful rule-execution feedback. Experiments on 36 datasets against 17 state‑of‑the‑art KGR methods show that SIRLM achieves significant performance gains.

By Xingrui Zhuo, Jiapu Wang, Manzong Huang, Gongqing Wu, Xindong Wu

llms nlp fine-tuning benchmarks

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