Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Enriching Artificial Intelligence Explanations with Knowledge Fragments

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

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.

Journal reference: 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

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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