Papers by Declan O’Sullivan
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods (2021.emnlp-main)
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| Challenge: | Knowledge Graph Embeddings (KGE) are widely used for relational learning on large scale Knowledge . however, little is known about the security vulnerabilities that might disrupt their intended behaviour. |
| Approach: | They propose to use model-agnostic instance attribution methods to select adversarial deletions and a heuristic method to replace one of the two entities in each influential triple to generate adversarials. |
| Outcome: | The proposed methods outperform the state-of-the-art data poisoning attacks on KGE models and improve the MRR degradation by up to 62% over the baselines. |
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns (2021.acl-long)
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| Challenge: | Knowledge graph embeddings (KGE) models are increasingly deployed in domains with high stake decision making where it is critical to identify the potential security vulnerabilities that might cause failure. |
| Approach: | They propose to exploit the inductive abilities of knowledge graph embedding models by crafting adversarial additions that can improve model’s confidence on decoy facts. |
| Outcome: | The proposed attacks outperform state-of-the-art baselines on four KGE models for two publicly available datasets and generalize across all model-dataset combinations. |