# PairRE: Knowledge Graph Embeddings via Paired Relation Vectors

arXiv: https://arxiv.org/abs/2011.03798 · HTML: https://mlsanity.com/paper/2011.03798/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2020.

## Built on by (most cited first, 30 of 67)

- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs](https://mlsanity.com/paper/2206.04798/) (2022)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [NativE: Multi-modal Knowledge Graph Completion in the Wild](https://mlsanity.com/paper/2406.17605/) (2024)
- [How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View](https://mlsanity.com/paper/2109.11800/) (2021)
- [Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation](https://mlsanity.com/paper/2404.09468/) (2024)
- [Multiple Heads are Better than One: Mixture of Modality Knowledge Experts for Entity Representation Learning](https://mlsanity.com/paper/2405.16869/) (2024)
- [SMiLE: Schema-augmented Multi-level Contrastive Learning for Knowledge Graph Link Prediction](https://mlsanity.com/paper/2210.04870/) (2022)
- [Bilinear Scoring Function Search for Knowledge Graph Learning](https://mlsanity.com/paper/2107.00184/) (2021)
- [CompoundE: Knowledge Graph Embedding with Translation, Rotation and Scaling Compound Operations](https://mlsanity.com/paper/2207.05324/) (2022)
- [TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors](https://mlsanity.com/paper/2209.08271/) (2022)
- [Knowledge Graph Embedding: An Overview](https://mlsanity.com/paper/2309.12501/) (2023)
- [Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2401.06072/) (2024)
- [Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion](https://mlsanity.com/paper/2402.15444/) (2024)
- [GreenKGC: A Lightweight Knowledge Graph Completion Method](https://mlsanity.com/paper/2208.09137/) (2022)
- [TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representation](https://mlsanity.com/paper/2204.08401/) (2022)
- [TranSHER: Translating Knowledge Graph Embedding with Hyper-Ellipsoidal Restriction](https://mlsanity.com/paper/2204.13221/) (2022)
- [Generalizing Knowledge Graph Embedding with Universal Orthogonal Parameterization](https://mlsanity.com/paper/2405.08540/) (2024)
- [Start from Zero: Triple Set Prediction for Automatic Knowledge Graph Completion](https://mlsanity.com/paper/2406.18166/) (2024)
- [InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities](https://mlsanity.com/paper/2202.04897/) (2022)
- [STaR: Knowledge Graph Embedding by Scaling, Translation and Rotation](https://mlsanity.com/paper/2202.07130/) (2022)
- [Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities](https://mlsanity.com/paper/2208.10378/) (2022)
- [Mitigating Relational Bias on Knowledge Graphs](https://mlsanity.com/paper/2211.14489/) (2022)
- [Deep Outdated Fact Detection in Knowledge Graphs](https://mlsanity.com/paper/2402.03732/) (2024)
- [Triple Classification for Scholarly Knowledge Graph Completion](https://mlsanity.com/paper/2111.11845/) (2021)
- [PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning](https://mlsanity.com/paper/2204.13957/) (2022)
- [Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning](https://mlsanity.com/paper/2205.06456/) (2022)
- [Analogical Inference Enhanced Knowledge Graph Embedding](https://mlsanity.com/paper/2301.00982/) (2023)
- [Knowledge Graph Embedding with 3D Compound Geometric Transformations](https://mlsanity.com/paper/2304.00378/) (2023)

## Abstract (arXiv)

Distance based knowledge graph embedding methods show promising results on link prediction task, on which two topics have been widely studied: one is the ability to handle complex relations, such as N-to-1, 1-to-N and N-to-N, the other is to encode various relation patterns, such as symmetry/antisymmetry. However, the existing methods fail to solve these two problems at the same time, which leads to unsatisfactory results. To mitigate this problem, we propose PairRE, a model with paired vectors for each relation representation. The paired vectors enable an adaptive adjustment of the margin in loss function to fit for complex relations. Besides, PairRE is capable of encoding three important relation patterns, symmetry/antisymmetry, inverse and composition. Given simple constraints on relation representations, PairRE can encode subrelation further. Experiments on link prediction benchmarks demonstrate the proposed key capabilities of PairRE. Moreover, We set a new state-of-the-art on two knowledge graph datasets of the challenging Open Graph Benchmark.