Learning basic concepts before complex ones is a natural form of learning. This paper addresses the specific problem of identifying concept prerequisites to inform about the basic knowledge required to understand a particular concept. Briefly, given a target concept c, the goal is to (a) find candidate concepts in a Knowledge Graph (KG) that serve as possible prerequisite for c; and, (b) evaluate the prerequisite relation between the target and candidates concepts via a supervised learning model. Our approach explores the DBpedia Knowledge Graph and its semantic relations to find candidate concepts as well as a pruning step to reduce the candidate concept set. Finally, we employ supervised learning algorithms to evaluate and generate a list of prerequisites for the target concept. A ground truth created based on expert knowledge is used to validate our approach, exhibiting promising results with a precision varying between 83% and 92.9%.