# Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/1911.04910 · HTML: https://mlsanity.com/paper/1911.04910/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2019.

## Built on by (most cited first, 30 of 38)

- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2302.12529/) (2023)
- [Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphs](https://mlsanity.com/paper/2203.00255/) (2022)
- [NativE: Multi-modal Knowledge Graph Completion in the Wild](https://mlsanity.com/paper/2406.17605/) (2024)
- [Geometry Interaction Knowledge Graph Embeddings](https://mlsanity.com/paper/2206.12418/) (2022)
- [Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding](https://mlsanity.com/paper/2110.14450/) (2021)
- [Tokenization, Fusion, and Augmentation: Towards Fine-grained Multi-modal Entity Representation](https://mlsanity.com/paper/2404.09468/) (2024)
- [Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding](https://mlsanity.com/paper/2110.03789/) (2021)
- [Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis](https://mlsanity.com/paper/2104.04676/) (2021)
- [KGBoost: A Classification-based Knowledge Base Completion Method with Negative Sampling](https://mlsanity.com/paper/2112.09340/) (2021)
- [KRACL: Contrastive Learning with Graph Context Modeling for Sparse Knowledge Graph Completion](https://mlsanity.com/paper/2208.07622/) (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)
- [Unleashing the Power of Imbalanced Modality Information for Multi-modal Knowledge Graph Completion](https://mlsanity.com/paper/2402.15444/) (2024)
- [HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction](https://mlsanity.com/paper/2202.13352/) (2022)
- [InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities](https://mlsanity.com/paper/2202.04897/) (2022)
- [KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning](https://mlsanity.com/paper/2409.12865/) (2024)
- [PIE: a Parameter and Inference Efficient Solution for Large Scale Knowledge Graph Embedding Reasoning](https://mlsanity.com/paper/2204.13957/) (2022)
- [ER: Equivariance Regularizer for Knowledge Graph Completion](https://mlsanity.com/paper/2206.12142/) (2022)
- [MEIM: Multi-partition Embedding Interaction Beyond Block Term Format for Efficient and Expressive Link Prediction](https://mlsanity.com/paper/2209.15597/) (2022)
- [Hyperbolic Hierarchical Knowledge Graph Embeddings for Link Prediction in Low Dimensions](https://mlsanity.com/paper/2204.13704/) (2022)
- [Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning](https://mlsanity.com/paper/2205.06456/) (2022)
- [MQuinE: a cure for "Z-paradox" in knowledge graph embedding models](https://mlsanity.com/paper/2402.03583/) (2024)
- [What Has Been Enhanced in my Knowledge-Enhanced Language Model?](https://mlsanity.com/paper/2202.00964/) (2022)
- [StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph](https://mlsanity.com/paper/2205.14209/) (2022)
- [Document-level Relation Extraction with Relation Correlations](https://mlsanity.com/paper/2212.10171/) (2022)
- [TTM-RE: Memory-Augmented Document-Level Relation Extraction](https://mlsanity.com/paper/2406.05906/) (2024)
- [Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge Graph Completion](https://mlsanity.com/paper/2501.15393/) (2025)
- [RelWalk A Latent Variable Model Approach to Knowledge Graph Embedding](https://mlsanity.com/paper/2101.10070/) (2021)

## Abstract (arXiv)

Translational distance-based knowledge graph embedding has shown progressive improvements on the link prediction task, from TransE to the latest state-of-the-art RotatE. However, N-1, 1-N and N-N predictions still remain challenging. In this work, we propose a novel translational distance-based approach for knowledge graph link prediction. The proposed method includes two-folds, first we extend the RotatE from 2D complex domain to high dimension space with orthogonal transforms to model relations for better modeling capacity. Second, the graph context is explicitly modeled via two directed context representations. These context representations are used as part of the distance scoring function to measure the plausibility of the triples during training and inference. The proposed approach effectively improves prediction accuracy on the difficult N-1, 1-N and N-N cases for knowledge graph link prediction task. The experimental results show that it achieves better performance on two benchmark data sets compared to the baseline RotatE, especially on data set (FB15k-237) with many high in-degree connection nodes.