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Information modeling and knowledge extraction for machine learning applications in industrial production systems

: Windmann, Stefan; Kühnert, Christian

Volltext urn:nbn:de:0011-n-6088383 (1021 KByte PDF)
MD5 Fingerprint: 8ac5d7bc083c88824f5ccc686fad4fd9
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Erstellt am: 15.1.2021

Beyerer, J.:
Machine Learning for Cyber Physical Systems : Selected papers from the International Conference ML4CPS 2020, Berlin, March 12-13, 2020
Cham: Springer Nature, 2021 (Technologien für die intelligente Automation 13)
ISBN: 978-3-662-62745-7
ISBN: 978-3-662-62746-4
International Conference on Machine Learning for Cyber Physical Systems (ML4CPS) <5, 2020, Berlin>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
machine learning; information modeling; knowledge extraction

In this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360component descriptions. To model sensor data, components of the OGC Sensor Things model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.