Knowledge Modelling and Active Learning in Manufacturing
This work addresses knowledge acquisition and data utilization challenges for manufacturing professionals, but it appears incremental as it integrates existing methods without claiming major breakthroughs.
The paper tackles the problem of knowledge modeling and data labeling in manufacturing by combining ontologies and knowledge graphs with active learning to generate new knowledge and efficiently acquire labels, addressing multiple use cases.
The increasing digitalization of the manufacturing domain requires adequate knowledge modeling to capture relevant information. Ontologies and Knowledge Graphs provide means to model and relate a wide range of concepts, problems, and configurations. 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 the manufacturing domain can be addressed taking advantage of the available knowledge and data.