Due to increasing global competition, companies are challenged to make their production flexible and adaptable. This leads to a steadily increasing complexity of production systems and thus their automation and control processes. At the same time, control processes must be quickly configurable in order to be able to react to short product life cycles. Robot-based adhesive application in automotive body assembly represents one such control and automation process. In car body assembly, industrial robots are increasingly being used for gluing side panels, enabling flow operation in assembly. In the event of a functional change in the production process, such as the replacement of the adhesive to be used, all the given process interrelationships must be analysed again and reconfigured if necessary in order to ensure the quality of the bonded joint. Comprehensive data management systems that provide an overview of all the system parameters and control levers are often not available in companies, so that reconfiguration is based on experience. Correct adjustment of the process parameters thus requires the user to have precise knowledge of the complex interrelationships between the process and bonding parameters, which makes the search for solutions in the event of a process change more difficult and time-consuming. In order to master the complexity of process planning and configuration, a large number of user-supporting solutions exist in the area of product lifecycle management (PLM). However, these neither have the functionality to generate solution and optimization proposals, nor do they map the existing expert knowledge with so-called empirical values about the system behaviour. The advantages of semantic technologies including ontologies, such as their graph structure and suitability for the use of optimization algorithms, illustrate their potential as the basis of a knowledge-based assistance solution. Against this background, the aim of this paper is to develop an ontology-based knowledge management system that can consolidate existing product and process information and add expert knowledge to it. The resulting knowledge graph of the process is then examined using selected optimization algorithms (PMS, Parallel Machine Scheduling). From the analysis, configuration suggestions can be derived, which can be presented to the user with a visualisation interface. Finally, the potential of ontologies as the basis of a knowledge-based assistance system is evaluated based on given results.