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Efficient Probabilistic Logic Reasoning with Graph Neural Networks

arXiv 2001.11850 ↗First posted 29 Jan 2020cs.AI cs.LGcited by 50

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Abstract (arXiv)

Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems. However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult. In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems. Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task. In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN. We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model. Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

Key facts. Cited by 50 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Jan 2020.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 12; 2024: 8; 2025: 7; 2026: 3.

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  1. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,131
  2. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  3. 2022Decoupling the Depth and Scope of Graph Neural Networkscited by 94
  4. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  5. 2022Explaining the Explainers in Graph Neural Networks: a Comparative Studycited by 34
  6. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 34
  7. 2021Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewritingcited by 30
  8. 2024Entity Alignment with Noisy Annotations from Large Language Modelscited by 22
  9. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  10. 2021Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networkscited by 18
  11. 2023Improved Logical Reasoning of Language Models via Differentiable Symbolic Programmingcited by 18
  12. 2023Data Scarcity in Recommendation Systems: A Surveycited by 17
38 more
  1. 2021Learning Logic Rules for Document-level Relation Extractioncited by 15
  2. 2023Relational Concept Bottleneck Modelscited by 15
  3. 2021Graph Collaborative Reasoningcited by 14
  4. 2022Neural Structured Prediction for Inductive Node Classificationcited by 13
  5. 2025Bottom-up Domain-specific Superintelligence: A Reliable Knowledge Graph is What We Needcited by 12
  6. 2022CARE: Certifiably Robust Learning with Reasoning via Variational Inferencecited by 10
  7. 2021Topological Regularization for Graph Neural Networks Augmentationcited by 8
  8. 2023Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learningcited by 8
  9. 2024Neuro-Symbolic Entity Alignment via Variational Inferencecited by 8
  10. 2022Calibrating Agent-based Models to Microdata with Graph Neural Networkscited by 7
  11. 2022RulE: Knowledge Graph Reasoning with Rule Embeddingcited by 5
  12. 2023Adaptive Dependency Learning Graph Neural Networkscited by 5
  13. 2021Relational Reasoning Networkscited by 4
  14. 2023Exploring & Exploiting High-Order Graph Structure for Sparse Knowledge Graph Completioncited by 4
  15. 2024Systematic Relational Reasoning With Epistemic Graph Neural Networkscited by 4
  16. 2024RuAG: Learned-rule-augmented Generation for Large Language Modelscited by 4
  17. 2023LogicMP: A Neuro-symbolic Approach for Encoding First-order Logic Constraintscited by 3
  18. 2025A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perceptioncited by 3
  19. 2025Nature's Insight: A Novel Framework and Comprehensive Analysis of Agentic Reasoning Through the Lens of Neurosciencecited by 3
  20. 2025Grounding Methods for Neural-Symbolic AIcited by 3
  21. 2022SpaceE: Knowledge Graph Embedding by Relational Linear Transformation in the Entity Spacecited by 1
  22. 2022Brain-inspired Graph Spiking Neural Networks for Commonsense Knowledge Representation and Reasoningcited by 1
  23. 2023Enhancing Embedding Representations of Biomedical Data using Logic Knowledgecited by 1
  24. 2023A Novel Neural-symbolic System under Statistical Relational Learningcited by 1
  25. 2023Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagerycited by 1
  26. 2024Relations Prediction for Knowledge Graph Completion using Large Language Modelscited by 1
  27. 2024Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Surveycited by 1
  28. 2025A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Datacited by 1
  29. 2025DeepGraphLog for Layered Neurosymbolic AIcited by 1
  30. 2026A formal definition and meta-model for a machine theory of mindcited by 1
  31. 2021An original model for multi-target learning of logical rules for knowledge graph reasoningcited by 0
  32. 2021DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoningcited by 0
  33. 2023Stability and Generalization of lp-Regularized Stochastic Learning for GCNcited by 0
  34. 2024Simple Augmentations of Logical Rules for Neuro-Symbolic Knowledge Graph Completioncited by 0
  35. 2024Neural Probabilistic Logic Learning for Knowledge Graph Reasoningcited by 0
  36. 2025ParaGate: Parasitic-Driven Domain Adaptation Transfer Learning for Netlist Performance Predictioncited by 0
  37. 2026Neuro-Relational Programs: Unifying Queries and Neural Computation over Structured Datacited by 0
  38. 2026Beyond Accuracy: Measuring Logical Compliance of Predictive Modelscited by 0