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

A peer-reviewed article of this Preprint also exists.

Rožanec, J.; Trajkova, E.; Novalija, I.; Zajec, P.; Kenda, K.; Fortuna, B.; Mladenić, D. Enriching Artificial Intelligence Explanations with Knowledge Fragments. Future Internet 2022, 14, 134. Rožanec, J.; Trajkova, E.; Novalija, I.; Zajec, P.; Kenda, K.; Fortuna, B.; Mladenić, D. Enriching Artificial Intelligence Explanations with Knowledge Fragments. Future Internet 2022, 14, 134.

DOI: 10.3390/fi14050134

Abstract

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.

Keywords

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

Subject

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Enriching Artificial Intelligence Explanations with Knowledge Fragments

Abstract

Keywords

Subject

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