  Ontology-based approaches for predicting gene-disease associations include
the more classical semantic similarity methods and more recently knowledge
graph embeddings. While semantic similarity is typically restricted to
hierarchical relations within the ontology, knowledge graph embeddings consider
their full breadth. However, embeddings are produced over a single graph and
complex tasks such as gene-disease association may require additional
ontologies. We investigate the impact of employing richer semantic
representations that are based on more than one ontology, able to represent
both genes and diseases and consider multiple kinds of relations within the
ontologies. Our experiments demonstrate the value of employing knowledge graph
embeddings based on random-walks and highlight the need for a closer
integration of different ontologies.
