  Identifying relationships between concepts is a key aspect of scientific
knowledge synthesis. Finding these links often requires a researcher to
laboriously search through scien- tific papers and databases, as the size of
these resources grows ever larger. In this paper we describe how distributional
semantics can be used to unify structured knowledge graphs with unstructured
text to predict new relationships between medical concepts, using a
probabilistic generative model. Our approach is also designed to ameliorate
data sparsity and scarcity issues in the medical domain, which make language
modelling more challenging. Specifically, we integrate the medical relational
database (SemMedDB) with text from electronic health records (EHRs) to perform
knowledge graph completion. We further demonstrate the ability of our model to
predict relationships between tokens not appearing in the relational database.
