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Learn to Explain Efficiently via Neural Logic Inductive Learning

arXiv 1910.02481 ↗First posted 6 Oct 2019cs.AIcited by 38

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Abstract (arXiv)

The capability of making interpretable and self-explanatory decisions is essential for developing responsible machine learning systems. In this work, we study the learning to explain problem in the scope of inductive logic programming (ILP). We propose Neural Logic Inductive Learning (NLIL), an efficient differentiable ILP framework that learns first-order logic rules that can explain the patterns in the data. In experiments, compared with the state-of-the-art methods, we find NLIL can search for rules that are x10 times longer while remaining x3 times faster. We also show that NLIL can scale to large image datasets, i.e. Visual Genome, with 1M entities.

Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2019.

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Citing papers per year: 2021: 9; 2022: 5; 2023: 10; 2024: 7; 2025: 7.

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  1. 2022A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainabilitycited by 118
  2. 2024Large Language Models Can Learn Temporal Reasoningcited by 110
  3. 2021A Survey on Interpretable Reinforcement Learningcited by 70
  4. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  5. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  6. 2021EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use casecited by 30
  7. 2024Can LLMs Reason with Rules? Logic Scaffolding for Stress-Testing and Improving LLMscited by 28
  8. 2023ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoningcited by 26
  9. 2023Neural-Logic Human-Object Interaction Detectioncited by 25
  10. 2021Neuro-Symbolic Hierarchical Rule Inductioncited by 23
  11. 2023Neural Compositional Rule Learning for Knowledge Graph Reasoningcited by 20
  12. 2021Differentiable Logic Machinescited by 18
26 more
  1. 2024Imperative Learning: A Self-supervised Neuro-Symbolic Learning Framework for Robot Autonomycited by 16
  2. 2021Learning Symbolic Rules for Interpretable Deep Reinforcement Learningcited by 9
  3. 2023On the Evolution of Knowledge Graphs: A Survey and Perspectivecited by 9
  4. 2023Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learningcited by 8
  5. 2024LogiCity: Advancing Neuro-Symbolic AI with Abstract Urban Simulationcited by 8
  6. 2025Synthesizing Visual Concepts as Vision-Language Programscited by 6
  7. 2022Rule Mining over Knowledge Graphs via Reinforcement Learningcited by 5
  8. 2021Rule Induction in Knowledge Graphs Using Linear Programmingcited by 4
  9. 2022Temporal Inductive Logic Reasoning over Hypergraphscited by 4
  10. 2023Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node Clusteringcited by 4
  11. 2025Learning Concept-Driven Logical Rules for Interpretable and Generalizable Medical Image Classificationcited by 4
  12. 2023Do Machine Learning Models Learn Statistical Rules Inferred from Data?cited by 3
  13. 2023Learning Reliable Logical Rules with SATNetcited by 3
  14. 2025Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neurosciencecited by 3
  15. 2025The Road to Generalizable Neuro-Symbolic Learning Should be Paved with Foundation Modelscited by 3
  16. 2025LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrievalcited by 3
  17. 2024DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimationcited by 2
  18. 2025Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completioncited by 2
  19. 2023Deep Inductive Logic Programming meets Reinforcement Learningcited by 1
  20. 2024From Chain to Tree: Refining Chain-like Rules into Tree-like Rules on Knowledge Graphscited by 1
  21. 2025GLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoningcited by 1
  22. 2021An original model for multi-target learning of logical rules for knowledge graph reasoningcited by 0
  23. 2021DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoningcited by 0
  24. 2022A Noise-tolerant Differentiable Learning Approach for Single Occurrence Regular Expression with Interleavingcited by 0
  25. 2023Reinforcement Logic Rule Learning for Temporal Point Processescited by 0
  26. 2024Prompt-fused framework for Inductive Logical Query Answeringcited by 0