In today’s age of (Industrial) Internet of Things, large amounts of data are generated in public and industrial settings every second. For enabling data analytics and aggregation, many companies currently focus on the approach of data lakes. While this approach allows the centralized storage of all available kinds of data, it leads to challenges as the stored data has to be found, understood and processed. One solution for describing semantics of data sources is the use of semantic models based on an available vocabulary. However, creating detailed semantic models can be a challenging task for users who are not familiar with semantic modeling and today’s available tools.
To overcome these challenges, we developed an intuitive and user-friendly interface, allowing data owners to define detailed semantic models for their data sources. The design of the user interface is based on an intensive requirement analysis gathered among several peers. It provides an intuitive mapping of semantic concepts to data attributes and the definition of relations between those concepts using drag and drop interaction. The user is given full modeling freedom as the insertion of semantic concepts and relations that are missing in the underlying vocabulary can be done on-demand and does not delay or impair the modeling process. Additionally, the refinement of the original detected data schema is supported with several operations. We built the interface into the semantic data platform ESKAPE, which already uses a flexible knowledge graph as underlying vocabulary and provides a detailed analysis of the data schema.