mlsanityData to 14 Oct 2026

Neural Markov Logic Networks

arXiv 1905.13462 ↗First posted 31 May 2019cs.LG stat.MLcited by 27

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Abstract (arXiv)

We introduce neural Markov logic networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic. Like Markov logic networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules. Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure. Similarly to many neural symbolic methods, NMLNs can exploit embeddings of constants but, unlike them, NMLNs work well also in their absence. This is extremely important for predicting in settings other than the transductive one. We showcase the potential of NMLNs on knowledge-base completion, triple classification and on generation of molecular (graph) data.

Key facts. Cited by 27 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 May 2019.

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Citing papers per year: 2021: 3; 2022: 1; 2023: 6; 2024: 5; 2025: 9; 2026: 3.

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  1. 2021From Statistical Relational to Neurosymbolic Artificial Intelligence: a Surveycited by 72
  2. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  3. 2024BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcutscited by 37
  4. 2022Towards Data-and Knowledge-Driven Artificial Intelligence: A Survey on Neuro-Symbolic Computingcited by 35
  5. 2023Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learningcited by 22
  6. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  7. 2024A Neuro-Symbolic Benchmark Suite for Concept Quality and Reasoning Shortcutscited by 19
  8. 2024Neuro-Symbolic AI: Explainability, Challenges, and Future Trendscited by 10
  9. 2024ULLER: A Unified Language for Learning and Reasoningcited by 9
  10. 2023Logical Entity Representation in Knowledge-Graphs for Differentiable Rule Learningcited by 8
  11. 2025Defining neurosymbolic AIcited by 8
  12. 2024Systematic Relational Reasoning With Epistemic Graph Neural Networkscited by 4
15 more
  1. 2025Online inductive learning from answer sets for efficient reinforcement learning explorationcited by 4
  2. 2025Right for the Right Reasons: Avoiding Reasoning Shortcuts via Prototypical Neurosymbolic AIcited by 3
  3. 2025A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Datacited by 2
  4. 2025Logical GANs: Adversarial Learning through Ehrenfeucht Fraisse Gamescited by 2
  5. 2023Current and Future Challenges in Knowledge Representation and Reasoningcited by 1
  6. 2023A Novel Neural-symbolic System under Statistical Relational Learningcited by 1
  7. 2023Spatial Knowledge-Infused Hierarchical Learning: An Application in Flood Mapping on Earth Imagerycited by 1
  8. 2021Relational Neural Markov Random Fieldscited by 0
  9. 2025Neurosymbolic Decision Treescited by 0
  10. 2025Symbolic Neural Generation with Applications to Lead Discovery in Drug Designcited by 0
  11. 2025Beyond Prompt Engineering: Neuro-Symbolic-Causal Architecture for Robust Multi-Objective AI Agentscited by 0
  12. 2025DeepProofLog: Efficient Proving in Deep Stochastic Logic Programscited by 0
  13. 2026Lifted Relational Probabilistic Inference via Implicit Learningcited by 0
  14. 2026EM-NeSy: Expectation Maximization for Neurosymbolic Learningcited by 0
  15. 2026Parallel Noising in Neural Markov Logic Networkscited by 0