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Graph Neural Networks (GNN) show good performance in relational data classification. However, their contribution to concept learning and the validation of their output from an application domain’s and user’s perspective have not been thoroughly studied. We argue that combining symbolic learning methods, such as Inductive Logic Programming (ILP), with statistical machine learning methods, especially GNNs, is an essential forward-looking step to perform powerful and validatable relational concept learning. In this contribution, we introduce a benchmark for the conceptual validation of GNN classification outputs. It consists of the symbolic representations of symmetric and non-symmetric figures that are taken from a well-known Kandinsky Pattern data set. We further provide a novel validation framework that can be used to generate comprehensible explanations with ILP on top of the relevance output of GNN explainers and human-expected relevance for concepts learned by GNNs. Our experiments conducted on our benchmark data set demonstrate that it is possible to extract symbolic concepts from the most relevant explanations that are representative of what a GNN has learned. Our findings open up a variety of avenues for future research on validatable explanations for GNNs.

The original version of this chapter was inadvertently published with a misspelling in the explanatory dialogue of Fig. 5. It has been updated as follows:bobby is a herbivore and dandelion is a plant. Fig. 5. An explanatory tree for tracks_down(bobby,dandelion), that can be queried by the user to get a local explanation why Bobby tracks down dandelion (steps A and B). A dialogue is realized by different drill-down questions, either to get more detailed verbal explanations or visual explanations (steps C.a and C.b)). Furthermore, the user can return to the last explanation (step D).

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