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。

基于关系概念的模型

Relational Concept Based Models

Causal reasoning in relational domains is fundamental to studying real-world
social phenomena in which individual units can influence each other's traits
and behavior. Dynamics between interconnected units can be represented as an
instantiation of a relational causal model; however, causal reasoning over such
instantiation requires additional templating assumptions that capture feedback
loops of influence. Previous research has developed lifted representations to
address the relational nature of such dynamics but has strictly required that
the representation has no cycles. To facilitate cycles in relational
representation and learning, we introduce relational $\sigma$-separation, a new
criterion for understanding relational systems with feedback loops. We also
introduce a new lifted representation, $\sigma$-abstract ground graph which
helps with abstracting statistical independence relations in all possible
instantiations of the cyclic relational model. We show the necessary and
sufficient conditions for the completeness of $\sigma$-AGG and that relational
$\sigma$-separation is sound and complete in the presence of one or more cycles
with arbitrary length. To the best of our knowledge, this is the first work on
representation of and reasoning with cyclic relational causal models.

本文介绍了一种新的关于环形关系因果模型的表示方法和推理方法。通过引入关系 σ 分离的新标准，以及一种新的提升表示，σ- 抽象地面图，帮助在所有可能的实例化中抽象统计独立关系。我们还表明，基于 σ-AGG，关系 σ 分离对于任意长度的一个或多个循环的存在是完整的和有必要的。本文是有关现有研究所建立的第一个运行时表示和推理循环关系因果模型。

具有循环的关系因果模型：表示和推理

Relational Causal Models with Cycles:Representation and Reasoning

An approach to clustering is presented that adapts the basic top-down
induction of decision trees method towards clustering. To this aim, it employs
the principles of instance based learning. The resulting methodology is
implemented in the TIC (Top down Induction of Clustering trees) system for
first order clustering. The TIC system employs the first order logical decision
tree representation of the inductive logic programming system Tilde. Various
experiments with TIC are presented, in both propositional and relational
domains.

本文提出了一种基于实例学习的改进决策树自顶向下聚类方法，实现了基于一阶逻辑决策树表示的 TIC 系统进行一阶聚类，并在命题和关系域中进行了多种实验。