The medical domain provides a fertile ground for the application of ontological knowledge. Ontologies are an essential part of many approaches to medical text processing, understanding and reasoning. However, the creation of large, high quality medical ontologies is not trivial, requiring the analysis of domain sources, background knowledge, as well as obtaining consensus among experts. Current methods are labor intensive, prone to generate inconsistencies, and often require expert knowledge. Fortunately, semi structured information repositories, like Wikipedia, provide a valuable resource from which to mine structured information. In this paper we propose a novel framework for automatically creating medical ontologies from semi-structured data. As part of this framework, we present a Directional Feedback Edge Labeling (DFEL) algorithm. We successfully demonstrate the effectiveness of the DFEL algorithm on the task of labeling the relations of Okinet, a Wikipedia based medical ontology. Current results demonstrate the high performance, utility, and flexibility of our approach. We conclude by describing ROSE, an application that combines Okinet with other medical ontologies.