# Poisoning Knowledge Graph Embeddings via Relation Inference Patterns

arXiv: https://arxiv.org/abs/2111.06345 · HTML: https://mlsanity.com/paper/2111.06345/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 11 Nov 2021.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 22 of 22)

- [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)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Adversarial Attacks on Neural Networks for Graph Data](https://mlsanity.com/paper/1805.07984/) (2018)
- [Adversarial Attack on Graph Structured Data](https://mlsanity.com/paper/1806.02371/) (2018)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [Adversarial Attacks on Node Embeddings via Graph Poisoning](https://mlsanity.com/paper/1809.01093/) (2018)
- [Complex Query Answering with Neural Link Predictors](https://mlsanity.com/paper/2011.03459/) (2020)
- [KBGAN: Adversarial Learning for Knowledge Graph Embeddings](https://mlsanity.com/paper/1711.04071/) (2017)
- [Traversing Knowledge Graphs in Vector Space](https://mlsanity.com/paper/1506.01094/) (2015)
- [Knowledge Graph Embedding with Iterative Guidance from Soft Rules](https://mlsanity.com/paper/1711.11231/) (2017)
- [Interaction Embeddings for Prediction and Explanation in Knowledge Graphs](https://mlsanity.com/paper/1903.04750/) (2019)
- [Data Poisoning Attack against Knowledge Graph Embedding](https://mlsanity.com/paper/1904.12052/) (2019)
- [Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications](https://mlsanity.com/paper/1905.00563/) (2019)
- [From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules](https://mlsanity.com/paper/1805.10461/) (2018)
- [On Multi-Relational Link Prediction with Bilinear Models](https://mlsanity.com/paper/1709.04808/) (2017)
- [Adversarial Sets for Regularising Neural Link Predictors](https://mlsanity.com/paper/1707.07596/) (2017)
- [Explaining Neural Matrix Factorization with Gradient Rollback](https://mlsanity.com/paper/2010.05516/) (2020)
- [On Inductive Abilities of Latent Factor Models for Relational Learning](https://mlsanity.com/paper/1709.05666/) (2017)

## Built on by (most cited first, 12 of 12)

- [GraphRAG under Fire](https://mlsanity.com/paper/2501.14050/) (2025)
- [Safety in Graph Machine Learning: Threats and Safeguards](https://mlsanity.com/paper/2405.11034/) (2024)
- [Predictive Multiplicity of Knowledge Graph Embeddings in Link Prediction](https://mlsanity.com/paper/2408.08226/) (2024)
- [On the Security Risks of Knowledge Graph Reasoning](https://mlsanity.com/paper/2305.02383/) (2023)
- [Schema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE](https://mlsanity.com/paper/2306.03659/) (2023)
- [Untargeted Adversarial Attack on Knowledge Graph Embeddings](https://mlsanity.com/paper/2405.10970/) (2024)
- [RAG Safety: Exploring Knowledge Poisoning Attacks to Retrieval-Augmented Generation](https://mlsanity.com/paper/2507.08862/) (2025)
- [Explaining Link Predictions in Knowledge Graph Embedding Models with Influential Examples](https://mlsanity.com/paper/2212.02651/) (2022)
- [Resilience in Knowledge Graph Embeddings](https://mlsanity.com/paper/2410.21163/) (2024)
- [Multi-Instance Adversarial Attack on GNN-Based Malicious Domain Detection](https://mlsanity.com/paper/2308.11754/) (2023)
- [Performance Evaluation of Knowledge Graph Embedding Approaches under Non-adversarial Attacks](https://mlsanity.com/paper/2407.06855/) (2024)
- [LLMAtKGE: Large Language Models as Explainable Attackers against Knowledge Graph Embeddings](https://mlsanity.com/paper/2510.11584/) (2025)

## Abstract (arXiv)

We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and composition in the knowledge graph. Specifically, to degrade the model's prediction confidence on target facts, we propose to improve the model's prediction confidence on a set of decoy facts. Thus, we craft adversarial additions that can improve the model's prediction confidence on decoy facts through different inference patterns. Our experiments demonstrate that the proposed poisoning attacks outperform state-of-art baselines on four KGE models for two publicly available datasets. We also find that the symmetry pattern based attacks generalize across all model-dataset combinations which indicates the sensitivity of KGE models to this pattern.