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Found 2 papers, 2 papers with code
Composition of Relational Features with an Application to Explaining Black-Box Predictors
Using a notion of explanations based on the compositional structure of features in a CRM, we provide empirical evidence on synthetic data of the ability to identify appropriate explanations; and demonstrate the use of CRMs as 'explanation machines' for black-box models that do not provide explanations for their predictions.
Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
We also provide experimental evidence comparing BotGNNs favourably to multi-layer perceptrons (MLPs) that use features representing a "propositionalised" form of the background knowledge; and BotGNNs to a standard ILP based on the use of most-specific clauses.
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