The ever increasing prevalence of publicly available struc-tured data on the World Wide Web enables new applications in a varietyof domains. In this paper, we provide a conceptual approach that lever-ages such data in order to explain the input-output behavior of trainedartificial neural networks. We apply existing Semantic Web technologiesin order to provide an experimental proof of concept.
Raymer, M. L.,
& Hitzler, P.
(2017). Explaining Trained Neural Networks with Semantic Web Technologies: First Steps. Proceedings of the Twelfth International Workshop on Neural-Symbolic Learning and Reasoning.