  The paper proposes a novel architecture for explainable AI based on semantic
technologies and AI. We tailor the architecture for the domain of demand
forecasting and validate it on a real-world case study. The provided
explanations combine concepts describing features relevant to a particular
forecast, related media events, and metadata regarding external datasets of
interest. The knowledge graph provides concepts that convey feature information
at a higher abstraction level. By using them, explanations do not expose
sensitive details regarding the demand forecasting models. The explanations
also emphasize actionable dimensions where suitable. We link domain knowledge,
forecasted values, and forecast explanations in a Knowledge Graph. The ontology
and dataset we developed for this use case are publicly available for further
research.
