mlsanityData to 7 Oct 2026

A Survey of Link Prediction in N-ary Knowledge Graphs

arXiv 2506.08970 ↗First posted 10 Jun 2025cs.AIcited by 4

Not on the map. Why?

Abstract (arXiv)

N-ary Knowledge Graphs (NKGs) are a specialized type of knowledge graph designed to efficiently represent complex real-world facts. Unlike traditional knowledge graphs, where a fact typically involves two entities, NKGs can capture n-ary facts containing more than two entities. Link prediction in NKGs aims to predict missing elements within these n-ary facts, which is essential for completing NKGs and improving the performance of downstream applications. This task has recently gained significant attention. In this paper, we present the first comprehensive survey of link prediction in NKGs, providing an overview of the field, systematically categorizing existing methods, and analyzing their performance and application scenarios. We also outline promising directions for future research.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 46 of its 114 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

46 of 114 references.

References · 46

  1. 2017Attention Is All You Needcited by 59,932
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  4. 2020A Survey on Knowledge Graphs: Representation, Acquisition and Applicationscited by 643
  5. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
  6. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  7. 2019Diachronic Embedding for Temporal Knowledge Graph Completioncited by 104
  8. 2024Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Surveycited by 82
  9. 2020Message Passing for Hyper-Relational Knowledge Graphscited by 78
  10. 2022Multi-Modal Knowledge Graph Construction and Application: A Surveycited by 74
  11. 2019Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphscited by 73
  12. 2021Learning from Counterfactual Links for Link Predictioncited by 69
34 more
  1. 2019Few-Shot Knowledge Graph Completioncited by 63
  2. 2022Temporal Knowledge Graph Completion: A Surveycited by 61
  3. 2016On the representation and embedding of knowledge bases beyond binary relationscited by 55
  4. 2019Document-Level N-ary Relation Extraction with Multiscale Representation Learningcited by 52
  5. 2018Wide Compression: Tensor Ring Netscited by 50
  6. 2020Adaptive Attentional Network for Few-Shot Knowledge Graph Completioncited by 42
  7. 2023LLM-assisted Knowledge Graph Engineering: Experiments with ChatGPTcited by 42
  8. 2025SCORE: Story Coherence and Retrieval Enhancement for AI Narrativescited by 42
  9. 2021Knowledge-aware Zero-Shot Learning: Survey and Perspectivecited by 41
  10. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  11. 2020Generalizing Tensor Decomposition for N-ary Relational Knowledge Basescited by 38
  12. 2023Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Promptingcited by 33
  13. 2021Role-Aware Modeling for N-ary Relational Knowledge Basescited by 29
  14. 2021Link Prediction on N-ary Relational Facts: A Graph-based Approachcited by 29
  15. 2025How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmarkcited by 26
  16. 2024RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructionscited by 25
  17. 2021Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Surveycited by 23
  18. 2021Improving Inductive Link Prediction Using Hyper-Relational Factscited by 21
  19. 2021What is Event Knowledge Graph: A Surveycited by 19
  20. 2021Improving Hyper-Relational Knowledge Graph Completioncited by 18
  21. 2021Link Prediction on N-ary Relational Data Based on Relatedness Evaluationcited by 15
  22. 2023Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphscited by 14
  23. 2021Searching to Sparsify Tensor Decomposition for N-ary Relational Datacited by 13
  24. 2023Shrinking Embeddings for Hyper-Relational Knowledge Graphscited by 12
  25. 2023Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graphcited by 10
  26. 2023On the Evolution of Knowledge Graphs: A Survey and Perspectivecited by 9
  27. 2022Learning Representations for Hyper-Relational Knowledge Graphscited by 8
  28. 2022DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typingcited by 7
  29. 2023A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledgecited by 6
  30. 2024HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embeddingcited by 6
  31. 2024CausalLP: Learning causal relations with weighted knowledge graph link predictioncited by 4
  32. 2025Inductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoningcited by 3
  33. 2023Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphscited by 2
  34. 2024HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representationcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025JanusVLN: Decoupling Semantics and Spatiality with Dual Implicit Memory for Vision-Language Navigationcited by 80
  2. 2026Task-Specific Efficiency Analysis: When Small Language Models Outperform Large Language Modelscited by 15
  3. 2026HEHRGNN: A Unified Embedding Model for Knowledge Graphs with Hyperedges and Hyper-Relational Edgescited by 0
  4. 2026Reification as a Transferable Vocabulary: Zero-Shot Link Prediction with Vanilla GNNscited by 0