Stealthy Targeted Data Poisoning Attack on Knowledge Graphs

A host of different KG embedding techniques have emerged recently and have been empirically shown to be very effective in accurately predicting missing facts in a KG, thus improving its coverage and quality. Unfortunately, embedding techniques can fall prey to adversarial data poisoning attack. In this form of attack, facts may be added to or deleted from a KG, called performing perturbations, that results in the manipulation of the …