mlsanityData to 14 Oct 2026

DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs

arXiv 1911.00055 ↗First posted 31 Oct 2019cs.LG cs.LO stat.MLcited by 175

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Abstract (arXiv)

In this paper, we study the problem of learning probabilistic logical rules for inductive and interpretable link prediction. Despite the importance of inductive link prediction, most previous works focused on transductive link prediction and cannot manage previously unseen entities. Moreover, they are black-box models that are not easily explainable for humans. We propose DRUM, a scalable and differentiable approach for mining first-order logical rules from knowledge graphs which resolves these problems. We motivate our method by making a connection between learning confidence scores for each rule and low-rank tensor approximation. DRUM uses bidirectional RNNs to share useful information across the tasks of learning rules for different relations. We also empirically demonstrate the efficiency of DRUM over existing rule mining methods for inductive link prediction on a variety of benchmark datasets.

Key facts. Cited by 175 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 Oct 2019.

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Citing papers per year: 2021: 25; 2022: 29; 2023: 44; 2024: 41; 2025: 23; 2026: 13.

175 papers.

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  1. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 275
  2. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  3. 2021Knowledge Graph Reasoning with Relational Digraphcited by 92
  4. 2021NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphscited by 72
  5. 2022Neural-Symbolic Models for Logical Queries on Knowledge Graphscited by 61
  6. 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphscited by 52
  7. 2022A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphscited by 49
  8. 2021Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphscited by 48
  9. 2021Neuro-Symbolic Inductive Logic Programming with Logical Neural Networkscited by 36
  10. 2024KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completioncited by 36
  11. 2021ChronoR: Rotation Based Temporal Knowledge Graph Embeddingcited by 34
  12. 2021Meta-Knowledge Transfer for Inductive Knowledge Graph Embeddingcited by 32
38 more
  1. 2023A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphscited by 32
  2. 2022Relational Message Passing for Fully Inductive Knowledge Graph Completioncited by 30
  3. 2021Role-Aware Modeling for N-ary Relational Knowledge Basescited by 29
  4. 2022AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoningcited by 28
  5. 2021Inductive Relation Prediction by BERTcited by 27
  6. 2022Subgraph Neighboring Relations Infomax for Inductive Link Prediction on Knowledge Graphscited by 27
  7. 2022Inductive Logical Query Answering in Knowledge Graphscited by 26
  8. 2023ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoningcited by 26
  9. 2024A Prompt-Based Knowledge Graph Foundation Model for Universal In-Context Reasoningcited by 25
  10. 2023A Multi-Task Perspective for Link Prediction with New Relation Types and Nodescited by 24
  11. 2025TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphscited by 24
  12. 2021Cycle Representation Learning for Inductive Relation Predictioncited by 23
  13. 2021Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Surveycited by 23
  14. 2023Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Typescited by 21
  15. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  16. 2023Neural Compositional Rule Learning for Knowledge Graph Reasoningcited by 20
  17. 2022Geodesic Graph Neural Network for Efficient Graph Representation Learningcited by 19
  18. 2022Knowledge Graph Reasoning with Logics and Embeddings: Survey and Perspectivecited by 18
  19. 2022Neural-Symbolic Entangled Framework for Complex Query Answeringcited by 18
  20. 2022Rethinking Knowledge Graph Evaluation Under the Open-World Assumptioncited by 17
  21. 2024MKGL: Mastery of a Three-Word Languagecited by 17
  22. 2021SAFRAN: An interpretable, rule-based link prediction method outperforming embedding modelscited by 15
  23. 2021EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generationcited by 14
  24. 2023Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphscited by 14
  25. 2023A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospectscited by 14
  26. 2022CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completioncited by 13
  27. 2023Adapting Neural Link Predictors for Data-Efficient Complex Query Answeringcited by 13
  28. 2023Knowledge Graph Completion with Counterfactual Augmentationcited by 13
  29. 2022RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphscited by 12
  30. 2023On the Correspondence Between Monotonic Max-Sum GNNs and Datalogcited by 12
  31. 2021LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embeddingcited by 11
  32. 2021Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretabilitycited by 10
  33. 2023On the Evolution of Knowledge Graphs: A Survey and Perspectivecited by 10
  34. 2021Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learningcited by 9
  35. 2023Learning Representations of Bi-level Knowledge Graphs for Reasoning beyond Link Predictioncited by 9
  36. 2023Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoningcited by 9
  37. 2023Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learningcited by 9
  38. 2024Link Prediction with Relational Hypergraphscited by 9

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