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arXiv · 2202.03971

Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs

Edmund Dervakos·Orfeas Menis-Mastromichalakis·Alexandros Chortaras·Giorgos Stamou
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Abstract

The use of symbolic knowledge representation and reasoning as a way to resolve the lack of transparency of machine learning classifiers is a research area that lately attracts many researchers. In this work, we use knowledge graphs as the underlying framework providing the terminology for representing explanations for the operation of a machine learning classifier. In particular, given a description of the application domain of the classifier in the form of a knowledge graph, we introduce a novel method for extracting and representing black-box explanations of its operation, in the form of first-order logic rules expressed in the terminology of the knowledge graph.

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Edmund Dervakos, Orfeas Menis-Mastromichalakis, Alexandros Chortaras, Giorgos Stamou. 2022-02-08. Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphs. https://arxiv.org/abs/2202.03971

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