mlsanityData to 7 Oct 2026

ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoning

arXiv 2309.01538 ↗First posted 4 Sep 2023cs.AI cs.CLcited by 26

On the map: LLM reasoning → Knowledge- and tool-augmented reasoning

Abstract (arXiv)

Logical rules are essential for uncovering the logical connections between relations, which could improve reasoning performance and provide interpretable results on knowledge graphs (KGs). Although there have been many efforts to mine meaningful logical rules over KGs, existing methods suffer from computationally intensive searches over the rule space and a lack of scalability for large-scale KGs. Besides, they often ignore the semantics of relations which is crucial for uncovering logical connections. Recently, large language models (LLMs) have shown impressive performance in the field of natural language processing and various applications, owing to their emergent ability and generalizability. In this paper, we propose a novel framework, ChatRule, unleashing the power of large language models for mining logical rules over knowledge graphs. Specifically, the framework is initiated with an LLM-based rule generator, leveraging both the semantic and structural information of KGs to prompt LLMs to generate logical rules. To refine the generated rules, a rule ranking module estimates the rule quality by incorporating facts from existing KGs. Last, the ranked rules can be used to conduct reasoning over KGs. ChatRule is evaluated on four large-scale KGs, w.r.t. different rule quality metrics and downstream tasks, showing the effectiveness and scalability of our method.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning. Builds on 22 of its 39 references that are in the map. First posted on arXiv 4 Sep 2023.

Builds on

22 of 39 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  5. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  7. 2023A Survey of Large Language Modelscited by 2,537
  8. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  9. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  10. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  11. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
  12. 2020RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphscited by 117
10 more
  1. 2019Evaluating Commonsense in Pre-trained Language Modelscited by 95
  2. 2019Probabilistic Logic Neural Networks for Reasoningcited by 69
  3. 2018Improving Question Answering by Commonsense-Based Pre-Trainingcited by 66
  4. 2021TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphscited by 59
  5. 2019Learn to Explain Efficiently via Neural Logic Inductive Learningcited by 38
  6. 2021Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphscited by 25
  7. 2023Neural Compositional Rule Learning for Knowledge Graph Reasoningcited by 20
  8. 2023A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospectscited by 14
  9. 2022Ruleformer: Context-aware Differentiable Rule Mining over Knowledge Graphcited by 9
  10. 2022R5: Rule Discovery with Reinforced and Recurrent Relational Reasoningcited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 13; 2025: 6; 2026: 3.

26 papers.

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  1. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  2. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  3. 2023Towards Self-Interpretable Graph-Level Anomaly Detectioncited by 41
  4. 2024Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoningcited by 41
  5. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  6. 2024WALL-E: World Alignment by Rule Learning Improves World Model-based LLM Agentscited by 24
  7. 2024Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learningcited by 17
  8. 2024Research Trends for the Interplay between Large Language Models and Knowledge Graphscited by 14
  9. 2024Online GNN Evaluation Under Test-time Graph Distribution Shiftscited by 12
  10. 2024Evaluating Deep Unlearning in Large Language Modelscited by 12
  11. 2025HiBench: Benchmarking LLMs Capability on Hierarchical Structure Reasoningcited by 12
  12. 2024Improving Complex Reasoning over Knowledge Graph with Logic-Aware Curriculum Tuningcited by 10
14 more
  1. 2025Rethinking Regularization Methods for Knowledge Graph Completioncited by 6
  2. 2025RuleReasoner: Reinforced Rule-based Reasoning via Domain-aware Dynamic Samplingcited by 6
  3. 2024RuAG: Learned-rule-augmented Generation for Large Language Modelscited by 4
  4. 2024Large Language Models Meet Graph Neural Networks: A Perspective of Graph Miningcited by 3
  5. 2023Validating ChatGPT Facts through RDF Knowledge Graphs and Sentence Similaritycited by 1
  6. 2024Neurosymbolic Methods for Rule Miningcited by 1
  7. 2024Is Large Language Model Good at Triple Set Prediction? An Empirical Studycited by 1
  8. 2025SHERLOCK: Towards Dynamic Knowledge Adaptation in LLM-enhanced E-commerce Risk Managementcited by 1
  9. 2024Efficient Relational Context Perception for Knowledge Graph Completioncited by 0
  10. 2025A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospectscited by 0
  11. 2025Driver Assistant: Persuading Drivers to Adjust Secondary Tasks Using Large Language Modelscited by 0
  12. 2026Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoningcited by 0
  13. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0
  14. 2026Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Rule2Text: Natural Language Explanation of Logical Rules in Knowledge Graphs
  2. 2025Rule2Text: A Framework for Generating and Evaluating Natural Language Explanations of Knowledge Graph Rules
  3. 2023Complex Logical Reasoning over Knowledge Graphs using Large Language Models
  4. 2024Chain-of-Knowledge: Integrating Knowledge Reasoning into Large Language Models by Learning from Knowledge Graphs
  5. 2025Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphs
  6. 2025Grounding LLM Reasoning with Knowledge Graphs
  7. 2023KG-GPT: A General Framework for Reasoning on Knowledge Graphs Using Large Language Models
  8. 2024Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphs