Enriching Artificial Intelligence Explanations with Knowledge Fragments

Jože Rožanec; Elena Trajkova; Inna Novalija; Patrik Zajec; Klemen Kenda; Blaž Fortuna; Dunja Mladenić

doi:10.20944/preprints202204.0073.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 7 April 2022 / Approved: 8 April 2022 / Online: 8 April 2022 (08:11:56 CEST)

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.

Explainable Artificial Intelligence; Human-Centric Artificial Intelligence; Smart Manufacturing; Demand Forecasting; Industry 4.0; Industry 5.0

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Enriching Artificial Intelligence Explanations with Knowledge Fragments

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