# Robust Knowledge Graph Embedding via Denoising

arXiv: https://arxiv.org/abs/2505.18171 · HTML: https://mlsanity.com/paper/2505.18171/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2025.

## Built on by (most cited first, 1 of 1)

- [KoALA: KL-L0 Adversarial Detector via Label Agreement](https://mlsanity.com/paper/2510.12752/) (2025)

## Abstract (arXiv)

We focus on obtaining robust knowledge graph embedding under perturbation in the embedding space. To address these challenges, we introduce a novel framework, Robust Knowledge Graph Embedding via Denoising, which enhances the robustness of KGE models on noisy triples. By treating KGE methods as energy-based models, we leverage the established connection between denoising and score matching, enabling the training of a robust denoising KGE model. Furthermore, we propose certified robustness evaluation metrics for KGE methods based on the concept of randomized smoothing. Through comprehensive experiments on benchmark datasets, our framework consistently shows superior performance compared to existing state-of-the-art KGE methods when faced with perturbed entity embedding.