mlsanityData to 7 Oct 2026

Improving Inductive Link Prediction Using Hyper-Relational Facts

arXiv 2107.04894 ↗First posted 10 Jul 2021cs.LGcited by 21

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Abstract (arXiv)

For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities. Still, all these approaches only consider triple-based glsplkg, whereas their richer counterparts, hyper-relational KGs (e.g., Wikidata), have not yet been properly studied. In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks. Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines. Our code is available at urlhttps://github.com/mali-git/hyperrelationalilp.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 19 of its 36 references that are in the map. First posted on arXiv 10 Jul 2021.

Builds on

19 of 36 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
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  4. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  5. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
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7 more
  1. 2020PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddingscited by 77
  2. 2020Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Frameworkcited by 70
  3. 2020Inductive Entity Representations from Text via Link Predictioncited by 61
  4. 2020Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Predictioncited by 46
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 10; 2024: 3; 2025: 1; 2026: 1.

21 papers.

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  1. 2023InGram: Inductive Knowledge Graph Embedding via Relation Graphscited by 50
  2. 2022Relational Message Passing for Fully Inductive Knowledge Graph Completioncited by 29
  3. 2022Weisfeiler and Leman Go Relationalcited by 28
  4. 2022An Open Challenge for Inductive Link Prediction on Knowledge Graphscited by 26
  5. 2021Zero-shot and Few-shot Learning with Knowledge Graphs: A Comprehensive Surveycited by 23
  6. 2023A Multi-Task Perspective for Link Prediction with New Relation Types and Nodescited by 23
  7. 2023Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformerscited by 16
  8. 2023Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphscited by 14
  9. 2024Actionable Cyber Threat Intelligence using Knowledge Graphs and Large Language Modelscited by 14
  10. 2022RAILD: Towards Leveraging Relation Features for Inductive Link Prediction In Knowledge Graphscited by 12
  11. 2024Link Prediction with Relational Hypergraphscited by 9
  12. 2022Facing Changes: Continual Entity Alignment for Growing Knowledge Graphscited by 8
9 more
  1. 2023A Markov Random Field model for Hypergraph-based Machine Learningcited by 5
  2. 2025A Survey of Link Prediction in N-ary Knowledge Graphscited by 4
  3. 2023Towards Semantically Enriched Embeddings for Knowledge Graph Completioncited by 3
  4. 2023Event Prediction using Case-Based Reasoning over Knowledge Graphscited by 2
  5. 2023Beyond Transduction: A Survey on Inductive, Few Shot, and Zero Shot Link Prediction in Knowledge Graphscited by 2
  6. 2024Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modelingcited by 2
  7. 2023Do as I can, not as I getcited by 1
  8. 2023IRT2: Inductive Linking and Ranking in Knowledge Graphs of Varying Scalecited by 0
  9. 2026THOR: Inductive Link Prediction over Hyper-Relational Knowledge Graphscited by 0