# Improving Hyper-Relational Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2104.08167 · HTML: https://mlsanity.com/paper/2104.08167/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 20 references that are in the map.
- First posted on arXiv 16 Apr 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning](https://mlsanity.com/paper/1909.02151/) (2019)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Message Passing for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2009.10847/) (2020)
- [Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings](https://mlsanity.com/paper/1704.07130/) (2017)
- [On the representation and embedding of knowledge bases beyond binary relations](https://mlsanity.com/paper/1604.08642/) (2016)
- [Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases](https://mlsanity.com/paper/2007.03988/) (2020)
- [Knowledge Graph Transfer Network for Few-Shot Recognition](https://mlsanity.com/paper/1911.09579/) (2019)

## Built on by (most cited first, 18 of 18)

- [Link Prediction on N-ary Relational Facts: A Graph-based Approach](https://mlsanity.com/paper/2105.08476/) (2021)
- [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023)
- [HEAT: Hyperedge Attention Networks](https://mlsanity.com/paper/2201.12113/) (2022)
- [Shrinking Embeddings for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2306.02199/) (2023)
- [HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level](https://mlsanity.com/paper/2305.06588/) (2023)
- [Learning Representations for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2208.14322/) (2022)
- [HyperFormer: Enhancing Entity and Relation Interaction for Hyper-Relational Knowledge Graph Completion](https://mlsanity.com/paper/2308.06512/) (2023)
- [A Survey of Link Prediction in N-ary Knowledge Graphs](https://mlsanity.com/paper/2506.08970/) (2025)
- [UniHR: Hierarchical Representation Learning for Unified Knowledge Graph Link Prediction](https://mlsanity.com/paper/2411.07019/) (2024)
- [Inductive Link Prediction on N-ary Relational Facts via Semantic Hypergraph Reasoning](https://mlsanity.com/paper/2503.20676/) (2025)
- [HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation](https://mlsanity.com/paper/2404.09848/) (2024)
- [Generalizing Hyperedge Expansion for Hyper-relational Knowledge Graph Modeling](https://mlsanity.com/paper/2411.06191/) (2024)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Understanding the Embedding Models on Hyper-relational Knowledge Graph](https://mlsanity.com/paper/2508.03280/) (2025)
- [VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2505.11803/) (2025)
- [Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs](https://mlsanity.com/paper/2505.12788/) (2025)
- [THOR: Inductive Link Prediction over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2602.05424/) (2026)
- [Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion](https://mlsanity.com/paper/2605.24064/) (2026)

## Abstract (arXiv)

Different from traditional knowledge graphs (KGs) where facts are represented as entity-relation-entity triplets, hyper-relational KGs (HKGs) allow triplets to be associated with additional relation-entity pairs (a.k.a qualifiers) to convey more complex information. How to effectively and efficiently model the triplet-qualifier relationship for prediction tasks such as HKG completion is an open challenge for research. This paper proposes to improve the best-performing method in HKG completion, namely STARE, by introducing two novel revisions: (1) Replacing the computation-heavy graph neural network module with light-weight entity/relation embedding processing techniques for efficiency improvement without sacrificing effectiveness; (2) Adding a qualifier-oriented auxiliary training task for boosting the prediction power of our approach on HKG completion. The proposed approach consistently outperforms STARE in our experiments on three benchmark datasets, with significantly improved computational efficiency.