Abstract
The proceedings contain 6 papers. The topics discussed include: can ontologies help making machine learning systems accountable?; using ontologies to enhance human understandability of global post-hoc explanations of black-box models; learning and reasoning with logic tensor networks: the framework and an application; automated and explainable ontology extension based on deep learning: a case study in the chemical domain; semantic queries explaining opaque machine learning classifiers; and complementing language embeddings with knowledge bases for specific domains.