The field of semi-supervised learning has been expanding rapidly in the past few years, with a sheer increase in the number of related publications. In this paper we present the SSL problem in contrast with supervised and unsupervised learning. In addition, we propose a taxonomy with which we categorize many existing approaches described in the literature based on their underlying framework, data representation, and algorithmic class.