Enriching Artificial Intelligence Explanations with Knowledge Fragments
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 28 of its 101 references that are in the map. First posted on arXiv 12 Apr 2022.
Builds on
28 of 101 references.
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16 more
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