  Medical diagnosis is the process of making a prediction of the disease a
patient is likely to have, given a set of symptoms and observations. This
requires extensive expert knowledge, in particular when covering a large
variety of diseases. Such knowledge can be coded in a knowledge graph --
encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge
itself and its encoding can be incomplete, refining the knowledge graph with
additional information helps physicians making better predictions. At the same
time, for deployment in a hospital, the diagnosis must be explainable and
transparent. In this paper, we present an approach using diagnosis paths in a
medical knowledge graph. We show that those graphs can be refined using latent
representations with RDF2vec, while the final diagnosis is still made in an
explainable way. Using both an intrinsic as well as an expert-based evaluation,
we show that the embedding-based prediction approach is beneficial for refining
the graph with additional valid conditions.
