An ontology-driven approach to improve data understanding for machine learning applications in manufacturing

The application of Machine Learning (ML) methods represents a significant aspect in the advancement of Industry 4.0. The creation of an appropriate data set for these applications has been identified as the most time-consuming step in the underlying end-to-end pipeline. One of the major obstacles in this process step is to bridge the gap between business understanding and data understanding. To address this challenge, we propose a novel methodology to bridge this gap based on a systematic literature review. Our methodology begins with the construction of an ontology that depicts the underlying manufacturing process along with its parameters. We then show how this ontology can be utilized to deepen the understanding of the manufacturing process. Subsequently, we demonstrate how appropriate target variables for ML-models and suitable data sources can be determined with the support of our ontology. We further elucidate our methodology through a real-world example.