SS14 - Knowledge Graphs for Smart Manufacturing: Modeling and Applications

Special Session Organized by

Irlan Grangel-Gonzale, Bosch Corporate Research, Germany and Tamás Ruppert, MTA-PE Lendület Complex Systems Monitoring Research Group, University of Pannonia, Hungary and Franz Listl, Siemens AG, Germany and Nada Sahlab, Institute of Industrial Automation and Software Engineering, University of Stuttgart, Germany

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Knowledge Graphs depict an effective way for structuring heterogeneous data, which is characterized by a high semantic expressiveness. They are used to continuously gather data describing dynamic real-world entities and their relations in a unified model for gaining new insights. Knowledge Graphs emerged in the past decade with research focused on knowledge representation, learning and application.

Enabling concepts of Industry 4.0, such as the Internet of Things, Cyber-Physical Systems as well as Digital Twin require mechanisms for handling highly heterogeneous, dynamic and complex data from the physical asset and its environment to realize various applications. Knowledge Graphs, although a promising approach to address some of the data modeling, exchange and management challenges remain scarce within the industrial domain.

The objective of this special session is to highlight on the potential and applicability of Knowledge Graphs for smart manufacturing.

Topics under this session include (but not limited to)

  • Knowledge Organization and Representation
  • Ontology-based Knowledge Graphs
  • Knowledge Graph evolution with streaming data
  • Knowledge Graph and its relevance to the Digital Twin
  • Knowledge Graphs and AI, e.g., graph embeddings
  • Applications of Knowledge Graphs