I seek to discover relationships within data via synthesizing BLOG models to describe them. BLOG (Bayesian LOGic) is a first-order probabilistic programming language that details probability distributions over worlds containing sets of objects. This sort of learning has previously been done with Probabilistic Relational Models, which are a highly restricted special case of BLOG models. I synthesize programs using a local search algorithm that maximizes over the likelihood of a model with the given data while penalizing complexity. I apply the algorithm to learning the generative model describing how parts of citations are written and show that it is capable of learning accurate and useful relationships.