Ontology-Enhanced Machine Learning: A Bosch Use Case of Welding Quality Monitoring
| Autor: | Stefan Schmidt, Ralf Mikut, Tim Pychynski, Yulia Svetashova, York Sure-Vetter, Baifan Zhou, Evgeny Kharlamov |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry Process (engineering) 010401 analytical chemistry Automotive industry 020207 software engineering 02 engineering and technology Ontology (information science) Machine learning computer.software_genre Electric resistance welding 01 natural sciences Pipeline (software) 0104 chemical sciences Variety (cybernetics) Task (project management) 0202 electrical engineering electronic engineering information engineering Semantic technology Artificial intelligence business computer |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030624651 ISWC (2) |
| Popis: | In the automotive industry, welding is a critical process of automated manufacturing and its quality monitoring is important. IoT technologies behind automated factories enable adoption of Machine Learning (ML) approaches for quality monitoring. Development of such ML models requires collaborative work of experts from different areas, including data scientists, engineers, process experts, and managers. The asymmetry of their backgrounds, the high variety and diversity of data relevant for quality monitoring pose significant challenges for ML modeling. In this work, we address these challenges by empowering ML-based quality monitoring methods with semantic technologies. We propose a system, called SemML, for ontology-enhanced ML pipeline development. It has several novel components and relies on ontologies and ontology templates for task negotiation and for data and ML feature annotation. We evaluated SemML on the Bosch use-case of electric resistance welding with very promising results. |
| Databáze: | OpenAIRE |
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