One of the challenges with data analysis revolves around selecting the best analysis method for a data set that will provide appropriate and meaningful results. This thesis presents an ontology-based framework to address challenges around selecting an analysis method that can best represent a data set and the information you want to get out of it. Two ontologies were developed, one to capture semantic and syntactic descriptions on a data source, and likewise one to capture the description of analysis methods. Ontologies were selected for their flexibility in providing a description between a set of concepts and relationships along with their ability to reason between these descriptions. In order to demonstrate the advantages of using ontologies within the framework, this thesis focuses on the analysis methods and challenges in the domain of syndromic surveillance concentrating on ontology design to leverage knowledge between domain and analysis experts.