Semi-supervised learning is a family of classification methods conceived to reduce the amount of required labeled information in the training phase. Graph-based methods are among the most popular semi-supervised strategies: a nearest neighbor graph is built in such a way that the manifold of the data is captured and the labeled information is propagated to target samples along the structure of the manifold. Research in graph-based semi-supervised learning has mainly focused on two aspects: i) the construction of the k-nearest neighbors graph and/or ii) the propagation algorithm providing the classification. Differently from the previous literature, in this paper we focus on data representation with the aim of incorporating semi-supervision earlier in the process. To this end, we propose an algorithm that learns a new knowledge-aware data embedding via an ensemble of semi-supervised autoencoders to enhance a graph-based semi-supervised classification. The experiments carried out on different classification tasks demonstrate the benefit of our approach.