Tackling the problem of learning probabilistic classifiers from incomplete data in the context of Knowledge Graphs expressed in Description Logics, we describe an inductive approach based on learning simple belief networks. Specifically, we consider a basic probabilistic model, a Naive Bayes classifier, based on multivariate Bernoullis and its extension to a two-tier network in which this classification model is connected to a lower layer consisting of a mixture of Bernoullis. We show how such models can be converted into (probabilistic) axioms (or rules) thus ensuring more interpretability. Moreover they may be also initialized exploiting expert knowledge. We present and discuss the outcomes of an empirical evaluation which aimed at testing the effectiveness of the models on a number of random classification problems with different ontologies.

翻译：针对在描述逻辑表达的知识图谱背景下从不完整数据中学习概率分类器的问题，我们提出一种基于学习简单信念网络的归纳方法。具体而言，我们考虑一个基于多元伯努利分布的基本概率模型——朴素贝叶斯分类器，并将其扩展为双层网络结构，其中该分类模型与由伯努利混合组成的下层网络相连接。我们展示了如何将此类模型转化为（概率）公理（或规则），从而确保更高的可解释性。此外，这些模型还可利用专家知识进行初始化。我们通过实证评估测试了这些模型在多个具有不同本体的随机分类问题上的有效性，并对其结果进行了展示与讨论。