3. Knowledge Modelling and Active Learning in Manufacturing

By Jože M. Rožanec, Jožef Stefan Institute, Slovenia | Inna Novalija, Jožef Stefan Institute, Slovenia | Patrik Zajec, Jožef Stefan Institute, Slovenia | Klemen Kenda, Jožef Stefan Institute, Slovenia | Dunja Mladenić, Jožef Stefan Institute, Slovenia

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Published: 22 Nov 2021

© 2021 Jože M. Rožanec | Inna Novalija | Patrik Zajec | Klemen Kenda | Dunja Mladenić


The increasing digitalization of themanufacturing domainrequires adequate knowledgemodeling to capturerelevant information.Ontologies and Knowledge Graphs provide means to model and relate a wide range of concepts, problems, and congurations. Both can be used to generate new knowledge through deductive inference and identify missing knowledge.While digitalization increases the amount of data available, much data is not labeled and cannot be directly used to train supervised machine learning models. Active learning can be used to identify the most informative data instances for which to obtain users'feedback, reduce friction, and maximize knowledge acquisition. By combining semantic technologies and active learning, multiple use cases in manufacturing domain can be addressed taking advantage of the available knowledge and data.