Epistemic graphs are a proposal for modelling how agents may have beliefs in arguments and how beliefs in some arguments may influence the beliefs in others. The beliefs in arguments are represented by probability distributions and influences between arguments are represented by logical constraints on these probability distributions. This allows for various kinds of influence to be represented including supporting, attacking, and mixed, and it allows for aggregation of influence to be captured, in a context-sensitive way. In this paper, we investigate methods for learning constraints, and thereby the nature of influences, from data. We evaluate our approach by showing that we can obtain constraints with reasonable quality from two publicly available studies.