Högskolan i Skövde

In recent years graph databases (GDBs) have become popular for managing large amounts of data. Especially the capability to integrate and link heterogeneous data is a driving factor for its success. This makes it a promising tool to support knowledge driven optimization (KDO), which involves various types of data, information, and artifacts. In particular, the link between simulation model, optimization algorithms with their parameters, results, and preferences of the stakeholders is interesting for investigation and traceability. In this paper, we propose a simple ontology as a schema for a KDO graph database and demonstrate how it can be used with an industrial use case. Furthermore, we highlight the ability to add lower level model information from an industrial use case to the GDB.