Domain knowledge is important for all organizations to succeed in the competitive market. This can be found in knowledge-intensive processes (KiP) and derived from knowledge workers. The conventional method of representing a process, i.e., the control flow of well-structured activities, is unsuitable for knowledge-intensive processes (KiP). Process mining aims to derive knowledge from process data stored in event logs. However, the quality of event logs has a limiting factor in obtaining reliable knowledge and has no meaningfulness. Therefore, the goal was to enhance the event log, which has no meaningfulness, with domain knowledge from KiP. This leads to an increase in the efficiency and quality of process mining. This master thesis provides a solid literature review and takes the strategy of Design Science Research (DSR) into account. Based on the awareness phase, the case of the student admission process for the Master of Science in Business Information Systems (MSc BIS) at the Applied Sciences Northwestern Switzerland FHNW was chosen. Data was collected from unstructured interviews and from a workshop with several process experts. To obtain more than subjective views of the process, documents, and data sources relevant to the process were analyzed. A solution for enriching event logs with domain knowledge was identified. For the student admission process at FHNW, a knowledge base using an ontology was created. The ontology was built using the software Protégé 5. A prototype was developed to connect the ontology to the event log. This was done with the help of SWRL Rules.