Relational Concept Based Models
Pietro Barbiero Francesco Giannini Gabriele Ciravegna Michelangelo Diligenti Giuseppe Marra
The design of interpretable deep learning models working in relational
domains poses an open challenge: interpretable deep learning methods, such as
Concept-Based Models (CBMs), are not designed to solve relational problems,
while relational models are not as interpretable as CBMs. To address this
problem, we propose Relational Concept-Based Models, a family of relational
deep learning methods providing interpretable task predictions. Our
experiments, ranging from image classification to link prediction in knowledge
graphs, show that relational CBMs (i) match generalization performance of
existing relational black-boxes (as opposed to non-relational CBMs), (ii)
support the generation of quantified concept-based explanations, (iii)
effectively respond to test-time interventions, and (iv) withstand demanding
settings including out-of-distribution scenarios, limited training data
regimes, and scarce concept supervisions.