  Artificial Intelligence models are increasingly used in manufacturing to
inform decision-making. Responsible decision-making requires accurate forecasts
and an understanding of the models' behavior. Furthermore, the insights into
models' rationale can be enriched with domain knowledge. This research builds
explanations considering feature rankings for a particular forecast, enriching
them with media news entries, datasets' metadata, and entries from the Google
Knowledge Graph. We compare two approaches (embeddings-based and
semantic-based) on a real-world use case regarding demand forecasting.
