Challenges of Modeling and Evaluating the Semantics of Technical Content Deployed in Recommendation Systems for Industry 4.0

Jos Lehmann, Michael Shamiyeh, Sven Ziemer

Abstract

In the context of Industry 4.0 the Smart Factory is enabled by the automation of physical production activities. The automation of intellectual pre-production activities enables what is here dubbed the “Smart Studio”. A key-element of the Smart Studio is Semantic Technology. While prototyping an ontology-based recommendation system for technical content about the case-study of the aviation industry, the problem of the readiness level of Semantic Technology became apparent. This led to the formulation of a Semantic Modeling and Tagging Methodology. The evaluation of both prototype and methodology yielded valuable insight about (i) the quantity and quality of semantics needed in the Smart Studio, (ii) the different interaction profiles identified when testing recommendations, (iii) the efficiency and effectiveness of the methods required to achieve semantics of right quantity and quality, (iv) the extent to which an ontology-based recommendation system is feasible and reduces double work for knowledge workers. Based on these results in this paper a position is formulated about the challenges for the viable application of Semantic Technology to technical content in Industry 4.0.

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