An Exploration of Explainable Machine Learning Using Semantic Web Technology

The behavior of a Machine Learning (ML) algorithm is generally accepted to be a black box, i.e., it cannot be opened and understood. This paper reports on an effort to provide explanation to ML algorithms by using semantic background knowledge. A preliminary paper was found as a project seed, its ex...

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Bibliographic Details

Published in:	2022 IEEE 16th International Conference on Semantic Computing (ICSC) pp. 143 - 146
Main Authors:	Procko, Tyler, Elvira, Timothy, Ochoa, Omar, Del Rio, Nicholas
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.01.2022
Subjects:	Artificial neural networks black box DL-Learner explanation Knowledge engineering Machine learning Machine learning algorithms Ontologies semantic web Semantics Training
Online Access:	Get full text
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