In this paper we address the problem of graph-based semi-supervised learning in tasks where a graph describing the relationships between data points is not available. We propose a method to jointly learn the graph and the parameters of a semi-supervised model using a probabilistic framework. We empirically show our proposal achieves competitive results in a variety of datasets.