  Knowledge Graph Embedding (KGE) transforms a discrete Knowledge Graph (KG)
into a continuous vector space facilitating its use in various AI-driven
applications like Semantic Search, Question Answering, or Recommenders. While
KGE approaches are effective in these applications, most existing approaches
assume that all information in the given KG is correct. This enables attackers
to influence the output of these approaches, e.g., by perturbing the input.
Consequently, the robustness of such KGE approaches has to be addressed. Recent
work focused on adversarial attacks. However, non-adversarial attacks on all
attack surfaces of these approaches have not been thoroughly examined. We close
this gap by evaluating the impact of non-adversarial attacks on the performance
of 5 state-of-the-art KGE algorithms on 5 datasets with respect to attacks on 3
attack surfaces-graph, parameter, and label perturbation. Our evaluation
results suggest that label perturbation has a strong effect on the KGE
performance, followed by parameter perturbation with a moderate and graph with
a low effect.
