![]() ![]() It consists of the symbolic representations of symmetric and non-symmetric figures that are taken from a well-known Kandinsky Pattern data set. In this contribution, we introduce a benchmark for the conceptual validation of GNN classification outputs. 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. 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. Graph Neural Networks (GNN) show good performance in relational data classification. ![]()
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