Efficient automation systems engineering process support based on semantic integration of engineering knowledge

Modern industrial automation systems engineering (ASE) environments have to accommodate for heterogeneity coming from the engineering disciplines involved, the software tools and their data models, and run-time data collection. In many ASE environments domain experts have to invest considerable effort to bridge the semantic gaps between common project-level engineering concepts and the diverse local data representations. In this paper we discuss the needs for semantic integration and applications of machine-understandable knowledge engineering in three real-world ASE use cases from our industry partners. We provide an evaluation concept with empirical studies to measure the benefits and limitations of the proposed approach compared to the traditional expert-intensive approach. Major result of the initial evaluation is that semantic integration has good potential to make engineering processes more efficient and robust if supported well with user interfaces that end users find usable and useful.