Explaining Trained Neural Networks with Semantic Web Technologies: First Steps
This work addresses the interpretability of neural networks for researchers and practitioners, but it is incremental as it applies existing technologies to a new application.
The paper tackles the problem of explaining trained neural networks by leveraging publicly available structured data from the Semantic Web, resulting in a conceptual approach and experimental proof of concept.
The ever increasing prevalence of publicly available structured data on the World Wide Web enables new applications in a variety of domains. In this paper, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept.