  Similarly to other connectionist models, Graph Neural Networks (GNNs) lack
transparency in their decision-making. A number of sub-symbolic approaches have
been developed to provide insights into the GNN decision making process. These
are first important steps on the way to explainability, but the generated
explanations are often hard to understand for users that are not AI experts. To
overcome this problem, we introduce a conceptual approach combining
sub-symbolic and symbolic methods for human-centric explanations, that
incorporate domain knowledge and causality. We furthermore introduce the notion
of fidelity as a metric for evaluating how close the explanation is to the
GNN's internal decision making process. The evaluation with a chemical dataset
and ontology shows the explanatory value and reliability of our method.
