# Data Poisoning Attack against Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/1904.12052 · HTML: https://mlsanity.com/paper/1904.12052/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 30 of 36)

- [A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability](https://mlsanity.com/paper/2204.08570/) (2022)
- [Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation](https://mlsanity.com/paper/2201.07986/) (2022)
- [GraphRAG under Fire](https://mlsanity.com/paper/2501.14050/) (2025)
- [A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection](https://mlsanity.com/paper/2205.10014/) (2022)
- [Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods](https://mlsanity.com/paper/2111.03120/) (2021)
- [Universal Spectral Adversarial Attacks for Deformable Shapes](https://mlsanity.com/paper/2104.03356/) (2021)
- [Poisoning Knowledge Graph Embeddings via Relation Inference Patterns](https://mlsanity.com/paper/2111.06345/) (2021)
- [Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector](https://mlsanity.com/paper/2301.01044/) (2023)
- [It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness](https://mlsanity.com/paper/2303.09767/) (2023)
- [Task and Model Agnostic Adversarial Attack on Graph Neural Networks](https://mlsanity.com/paper/2112.13267/) (2021)
- [On the Security Risks of Knowledge Graph Reasoning](https://mlsanity.com/paper/2305.02383/) (2023)
- [Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection](https://mlsanity.com/paper/2310.12011/) (2023)
- [Random Walks for Adversarial Meshes](https://mlsanity.com/paper/2202.07453/) (2022)
- [Fake Resume Attacks: Data Poisoning on Online Job Platforms](https://mlsanity.com/paper/2402.14124/) (2024)
- [Untargeted Adversarial Attack on Knowledge Graph Embeddings](https://mlsanity.com/paper/2405.10970/) (2024)
- [Watermarking Graph Neural Networks via Explanations for Ownership Protection](https://mlsanity.com/paper/2501.05614/) (2025)
- [RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.08862/) (2025)
- [TWIG: Towards pre-hoc Hyperparameter Optimisation and Cross-Graph Generalisation via Simulated KGE Models](https://mlsanity.com/paper/2402.06097/) (2024)
- [A Cognac Shot To Forget Bad Memories: Corrective Unlearning for Graph Neural Networks](https://mlsanity.com/paper/2412.00789/) (2024)
- [eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules](https://mlsanity.com/paper/2412.04846/) (2024)
- [Making Theft Useless: Adulteration-Based Protection of Proprietary Knowledge Graphs in GraphRAG Systems](https://mlsanity.com/paper/2601.00274/) (2026)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)
- [Resilience in Knowledge Graph Embeddings](https://mlsanity.com/paper/2410.21163/) (2024)
- [KGMark: A Diffusion Watermark for Knowledge Graphs](https://mlsanity.com/paper/2505.23873/) (2025)
- [Unifying Post-hoc Explanations of Knowledge Graph Completions](https://mlsanity.com/paper/2507.22951/) (2025)
- [ShadowMerge: A Novel Poisoning Attack on Graph-Based Agent Memory via Relation-Channel Conflicts](https://mlsanity.com/paper/2605.09033/) (2026)
- [Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning](https://mlsanity.com/paper/2605.09822/) (2026)
- [Taxonomy of AISecOps Threat Modeling for Cloud Based Medical Chatbots](https://mlsanity.com/paper/2305.11189/) (2023)
- [Poisoning × Evasion: Symbiotic Adversarial Robustness for Graph Neural Networks](https://mlsanity.com/paper/2312.05502/) (2023)
- [From Latent to Lucid: Transforming Knowledge Graph Embeddings into Interpretable Structures with KGEPrisma](https://mlsanity.com/paper/2406.01759/) (2024)

## Abstract (arXiv)

Knowledge graph embedding (KGE) is a technique for learning continuous embeddings for entities and relations in the knowledge graph.Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE' robustness to adversarial attacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.