Complex mechatronic products such as cars, airplanes, engineering machinery etc. usually have a large number of variants. Each variant is specified by configuration rules. In cars, every single component in average affects 90% of the other ~50,000 components. It takes tens of thousands of such configuration rules to map these relationships.
A knowledge-based representation of product configuration rules allows a high performant AI-based rule satisfiability analysis.
Knowledge Graphs (KGs) have been recognized by several industries as an efficient approach for data governance, semantic enrichment, and as a data integration technology that brings unstructured and structured data together. One of the paramount processes, which determines the usability of your KG over the years, is the process of the KG enrichment and curation.