Simple and Interpretable Probabilistic Classifiers for Knowledge Graphs

TL;DR

This paper introduces simple, interpretable probabilistic classifiers based on belief networks for Knowledge Graphs, combining Naive Bayes and mixture models, with conversion to axioms for enhanced interpretability and empirical validation.

Contribution

It presents a novel approach using belief networks for probabilistic classification in Knowledge Graphs, enabling rule conversion and expert initialization.

Findings

Models effectively classify with different ontologies

Conversion to axioms improves interpretability

Empirical results demonstrate model effectiveness

Abstract

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.