Statistical relational learning allows algorithms to simultaneously reason about complex structure and uncertainty with a given domain. One common challenge when analyzing these domains is the presence of latent structure within the data. We present a novel algorithm that automatically groups together different objects in a domain in order to uncover latent structure, including a hierarchy or even heterarchy. We empirically evaluate our algorithm on two large real-world tasks where the goal is to predict whether a patient will have an adverse reaction to a medication. We found that the proposed approach produced a more accurate model than the baseline approach. Furthermore, we found interesting latent structure that was deemed to be relevant and interesting by a medical collaborator.