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A Review of Relational Machine Learning for Knowledge Graphs

arXiv 1503.00759 ↗First posted 2 Mar 2015stat.ML cs.LGcited by 264

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Abstract (arXiv)

Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be "trained" on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two fundamentally different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on latent feature models such as tensor factorization and multiway neural networks. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. To this end, we also discuss Google's Knowledge Vault project as an example of such combination.

Key facts. Cited by 264 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Mar 2015.

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Citing papers per year: 2021: 70; 2022: 59; 2023: 41; 2024: 39; 2025: 36; 2026: 19.

264 papers.

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  1. 2021Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Predictioncited by 273
  2. 2021Graph Learning: A Surveycited by 140
  3. 2022Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questionscited by 116
  4. 2021From Statistical Relational to Neurosymbolic Artificial Intelligence: a Surveycited by 72
  5. 2021NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphscited by 71
  6. 2021A Review of Graph Neural Networks and Their Applications in Power Systemscited by 62
  7. 2021A Survey on Embedding Dynamic Graphscited by 60
  8. 2022Neural-Symbolic Models for Logical Queries on Knowledge Graphscited by 60
  9. 2021Differentially Private Federated Knowledge Graphs Embeddingcited by 45
  10. 2022Equivariant Hypergraph Diffusion Neural Operatorscited by 45
  11. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  12. 2022A Decade of Knowledge Graphs in Natural Language Processing: A Surveycited by 40
38 more
  1. 2021Modular Design Patterns for Hybrid Learning and Reasoning Systems: a taxonomy, patterns and use casescited by 39
  2. 2022OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphscited by 38
  3. 2021Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representationscited by 37
  4. 2021Neural Link Prediction with Walk Poolingcited by 36
  5. 2021Continual Learning of Knowledge Graph Embeddingscited by 35
  6. 2024TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphscited by 35
  7. 2023GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Modelscited by 30
  8. 2021Role-Aware Modeling for N-ary Relational Knowledge Basescited by 29
  9. 2021Link Prediction on N-ary Relational Facts: A Graph-based Approachcited by 29
  10. 2023Revisiting Link Prediction: A Data Perspectivecited by 29
  11. 2023IMF: Interactive Multimodal Fusion Model for Link Predictioncited by 28
  12. 2021Inductive Relation Prediction by BERTcited by 27
  13. 2022Looking Beyond IoCs: Automatically Extracting Attack Patterns from External CTIcited by 27
  14. 2023PaGE-Link: Path-based Graph Neural Network Explanation for Heterogeneous Link Predictioncited by 25
  15. 2021An Overview and Experimental Study of Learning-based Optimization Algorithms for Vehicle Routing Problemcited by 22
  16. 2024What is the Relationship between Tensor Factorizations and Circuits (and How Can We Exploit it)?cited by 22
  17. 2023Neurosymbolic AI for Reasoning over Knowledge Graphs: A Surveycited by 21
  18. 2023How to Turn Your Knowledge Graph Embeddings into Generative Modelscited by 20
  19. 2021A Survey of Knowledge Graph Embedding and Their Applicationscited by 17
  20. 2021Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methodscited by 16
  21. 2021Link Prediction on N-ary Relational Data Based on Relatedness Evaluationcited by 15
  22. 2021Convolutional Hypercomplex Embeddings for Link Predictioncited by 15
  23. 2022KGLM: Integrating Knowledge Graph Structure in Language Models for Link Predictioncited by 15
  24. 2021Hashing-Accelerated Graph Neural Networks for Link Predictioncited by 14
  25. 2022A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphscited by 14
  26. 2024PageRank Bandits for Link Predictioncited by 14
  27. 2021Searching to Sparsify Tensor Decomposition for N-ary Relational Datacited by 13
  28. 2021Trans4E: Link Prediction on Scholarly Knowledge Graphscited by 13
  29. 2021Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answeringcited by 13
  30. 2022Concept Graph Neural Networks for Surgical Video Understandingcited by 13
  31. 2022Link Prediction on Heterophilic Graphs via Disentangled Representation Learningcited by 13
  32. 2022A Dataset for Hyper-Relational Extraction and a Cube-Filling Approachcited by 13
  33. 2023Adapting Neural Link Predictors for Data-Efficient Complex Query Answeringcited by 13
  34. 2024APEX2: Adaptive and Extreme Summarization for Personalized Knowledge Graphscited by 13
  35. 2022Collaboration-Aware Graph Convolutional Network for Recommender Systemscited by 12
  36. 2022ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspectivecited by 12
  37. 2023Construction of Knowledge Graphs: State and Challengescited by 12
  38. 2023Learning Differentiable Logic Programs for Abstract Visual Reasoningcited by 12

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