The traditional process of conducting literature reviews requires a significant amount of manual work and is often constrained by the limitations of small sample sizes. Although novel computational approaches to language analysis offer great opportunities, they are rarely applied to literature reviews. We aim to demonstrate the potential of ontology-based computational literature reviews. Therefore, we conducted a Design Science Research (DSR) project to apply and extend a method for this type of review. Since DSR shows a considerable diversity in theoretical foundations and methodological approaches and can serve as an insightful example, we first developed a machine learning classifier to identify DSR articles. Second, we applied and extended a method for ontological annotation and sentence classification. Finally, we conducted a computational literature review of 6235 DSR articles, focusing on the distribution of theories, methods, and topics. We also developed an interactive dashboard prototype with selected results from our study.