Complex event processing received an increasing interest during the last years with the adoption of event-driven architectures in various application domains. Despite a number of solutions that can process events in near real-time, their effectiveness for decision support relies heavily upon human domain knowledge. This poses a problem in areas that require vast amounts of specialized knowledge and background information, such as medical environments. We propose four techniques to enrich complex event processing with domain knowledge from ontologies to overcome this limitation. These techniques focus on preserving the strengths of state-of-the-art systems and enhancing them with existing ontologies to increase accuracy and effectiveness. The viability of our approach is demonstrated in a multifaceted experiment.