Digital transformation of companies brings to the agenda a number of problems to be solved when new or changed (intelligent or connected) products, improved end-to-end operational processes or changes in business models are required. Solutions for these problems often require a formal representation of the relevant knowledge and contextual constraints in the application domain. Knowledge modelling using ontologies, semantic technologies or fuzzy logic contributes to this task.

Among the problems to be addressed in digital transformation are knowledge modelling of business units & processes based on customer requirements and preferences (trends leading to Industry 4.0, Logistics 4.0, and Mobility 4.0), managing different physical and information resources and providing for their efficient interaction (the Internet of Things and the Internet of Everything concepts, Industrial IoT), handling cultural differences of making business between employees and companies from different countries (organizational behaviour issues related to international dimensions and cross cultural aspects of collaboration and decision making) are among them. This causes a heavy dependency of the companies on how they maintain their knowledge.

Ontological modelling is defined as a complex set of relations between people, processes and technology bound together with cultural norms. Ontologies provide for a shared understanding of a domain that can be communicated across the multiple business resources. They facilitate knowledge sharing and reuse in open and dynamic distributed systems, and allow entities not designed to work together to interoperate.