# Link Prediction on N-ary Relational Data Based on Relatedness Evaluation arXiv: https://arxiv.org/abs/2104.10424 ยท HTML: https://mlsanity.com/paper/2104.10424/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 62 references that are in the map. - First posted on arXiv 21 Apr 2021. ## Builds on (most cited first, 27 of 27) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015) - [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017) - [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017) - [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014) - [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016) - [Convolutional 2D Knowledge Graph Embeddings](https://mlsanity.com/paper/1707.01476/) (2017) - [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019) - [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017) - [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015) - [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017) - [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015) - [A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Network](https://mlsanity.com/paper/1712.02121/) (2017) - [End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://mlsanity.com/paper/1811.04441/) (2018) - [Modeling Relation Paths for Representation Learning of Knowledge Bases](https://mlsanity.com/paper/1506.00379/) (2015) - [On the representation and embedding of knowledge bases beyond binary relations](https://mlsanity.com/paper/1604.08642/) (2016) - [Traversing Knowledge Graphs in Vector Space](https://mlsanity.com/paper/1506.01094/) (2015) - [Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases](https://mlsanity.com/paper/2007.03988/) (2020) - [TorusE: Knowledge Graph Embedding on a Lie Group](https://mlsanity.com/paper/1711.05435/) (2017) - [Improving Knowledge Graph Embedding Using Simple Constraints](https://mlsanity.com/paper/1805.02408/) (2018) - [Type-Constrained Representation Learning in Knowledge Graphs](https://mlsanity.com/paper/1508.02593/) (2015) - [TransG : A Generative Mixture Model for Knowledge Graph Embedding](https://mlsanity.com/paper/1509.05488/) (2015) - [ProjE: Embedding Projection for Knowledge Graph Completion](https://mlsanity.com/paper/1611.05425/) (2016) - [Permutation-equivariant neural networks applied to dynamics prediction](https://mlsanity.com/paper/1612.04530/) (2016) - [Locally Adaptive Translation for Knowledge Graph Embedding](https://mlsanity.com/paper/1512.01370/) (2015) - [Data-dependent Learning of Symmetric/Antisymmetric Relations for Knowledge Base Completion](https://mlsanity.com/paper/1808.08361/) (2018) - [Knowledge Hypergraphs: Prediction Beyond Binary Relations](https://mlsanity.com/paper/1906.00137/) (2019) ## Built on by (most cited first, 15 of 15) - [What is Event Knowledge Graph: A Survey](https://mlsanity.com/paper/2112.15280/) (2021) - [Representation Learning on Hyper-Relational and Numeric Knowledge Graphs with Transformers](https://mlsanity.com/paper/2305.18256/) (2023) - [Shrinking Embeddings for Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2306.02199/) (2023) - [Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2307.10219/) (2023) - [Link Prediction with Relational Hypergraphs](https://mlsanity.com/paper/2402.04062/) (2024) - [DHGE: Dual-View Hyper-Relational Knowledge Graph Embedding for Link Prediction and Entity Typing](https://mlsanity.com/paper/2207.08562/) (2022) - [HYPER: A Foundation Model for Inductive Link Prediction with Knowledge Hypergraphs](https://mlsanity.com/paper/2506.12362/) (2025) - [HyCubE: Efficient Knowledge Hypergraph 3D Circular Convolutional Embedding](https://mlsanity.com/paper/2402.08961/) (2024) - [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) - [Few-shot Link Prediction on N-ary Facts](https://mlsanity.com/paper/2305.06104/) (2023) - [HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation](https://mlsanity.com/paper/2404.09848/) (2024) - [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025) - [Relation-dependent Contrastive Learning with Cluster Sampling for Inductive Relation Prediction](https://mlsanity.com/paper/2211.12266/) (2022) - [Generative Representation Learning on Hyper-relational Knowledge Graphs via Masked Discrete Diffusion](https://mlsanity.com/paper/2605.24064/) (2026) ## Abstract (arXiv) With the overwhelming popularity of Knowledge Graphs (KGs), researchers have poured attention to link prediction to fill in missing facts for a long time. However, they mainly focus on link prediction on binary relational data, where facts are usually represented as triples in the form of (head entity, relation, tail entity). In practice, n-ary relational facts are also ubiquitous. When encountering such facts, existing studies usually decompose them into triples by introducing a multitude of auxiliary virtual entities and additional triples. These conversions result in the complexity of carrying out link prediction on n-ary relational data. It has even proven that they may cause loss of structure information. To overcome these problems, in this paper, we represent each n-ary relational fact as a set of its role and role-value pairs. We then propose a method called NaLP to conduct link prediction on n-ary relational data, which explicitly models the relatedness of all the role and role-value pairs in an n-ary relational fact. We further extend NaLP by introducing type constraints of roles and role-values without any external type-specific supervision, and proposing a more reasonable negative sampling mechanism. Experimental results validate the effectiveness and merits of the proposed methods.