  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.
