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.

我们提出了一种解决关系领域中可解释深度学习模型设计问题的方法：关系概念模型（Relational Concept-Based Models）。实验结果表明，关系概念模型与现有的关系黑匣子模型在泛化性能上相匹配，并支持生成定量概念解释，有效地响应测试时干预，并适应多种严苛环境，包括分布不均、训练数据有限和概念监督不足。