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Manufacturing business competition is driven by efficiency in order to offer the best price for products. Of paramount importance to achieve this efficiency is to get the right information at the right time. Manufacturing is a very complex process. To manage such a complex process a lot of data are required. These data are diversely spread out in different IT systems or silos, eg, Enterprise Resource Planning (ERP), Manufacturing Execution Systems (MES), and Master Data (MD). These silos comprise no explicit semantics. They also contain differences in the way real-world concepts are modeled, ie, Semantic Interoperability Conflicts (SIC)[1], thus hindering data re-usability. To tackle these problems Knowledge Graph (KG)-based applications have emerged. For instance, the Line Information System (LIS)[2] for manufacturing enables semantic harmonization, ie, the resolution of SICs of data on production lines. However, despite this and other previous efforts at Bosch using KGs [3, 4, 5, 6, 7] for handling semantic harmonization many more data is being generated and consumed (cf. Figure 1). In addition, there are still no mechanisms to fulfill the FAIR principles [8] in manufacturing scenarios at Bosch. Of key relevance here is to have the FAIR principles in action, ie, the data consumers should be capable of finding, accessing, and reusing data whenever required. Moreover, these data should be interoperable which remains as a huge challenge. To accelerate the data exchange and to meet the expectations of data consumers, it is required to move from an application mindset to a data centric one, where KG-based data products present concrete …