  Despite the widespread use of Knowledge Graph Embeddings (KGE), little is
known about the security vulnerabilities that might disrupt their intended
behaviour. We study data poisoning attacks against KGE models for link
prediction. These attacks craft adversarial additions or deletions at training
time to cause model failure at test time. To select adversarial deletions, we
propose to use the model-agnostic instance attribution methods from
Interpretable Machine Learning, which identify the training instances that are
most influential to a neural model's predictions on test instances. We use
these influential triples as adversarial deletions. We further propose a
heuristic method to replace one of the two entities in each influential triple
to generate adversarial additions. Our experiments show that the proposed
strategies outperform the state-of-art data poisoning attacks on KGE models and
improve the MRR degradation due to the attacks by up to 62% over the baselines.
