  The first step towards digitalization within organizations lies in
digitization - the conversion of analog data into digitally stored data. This
basic step is the prerequisite for all following activities like the
digitalization of processes or the servitization of products or offerings.
However, digitization itself often leads to 'data-rich' but 'knowledge-poor'
material. Knowledge discovery and knowledge extraction as approaches try to
increase the usefulness of digitized data. In this paper, we point out the key
challenges in the context of knowledge discovery and present an approach to
addressing these using a microservices architecture. Our solution led to a
conceptual design focusing on keyword extraction, similarity calculation of
documents, database queries in natural language, and programming language
independent provision of the extracted information. In addition, the conceptual
design provides referential design guidelines for integrating processes and
applications for semi-automatic learning, editing, and visualization of
ontologies. The concept also uses a microservices architecture to address
non-functional requirements, such as scalability and resilience. The evaluation
of the specified requirements is performed using a demonstrator that implements
the concept. Furthermore, this modern approach is used in the German patent
office in an extended version.
