  Knowledge Graphs have been one of the fundamental methods for integrating
heterogeneous data sources. Integrating heterogeneous data sources is crucial,
especially in the biomedical domain, where central data-driven tasks such as
drug discovery rely on incorporating information from different biomedical
databases. These databases contain various biological entities and relations
such as proteins (PDB), genes (Gene Ontology), drugs (DrugBank), diseases
(DDB), and protein-protein interactions (BioGRID). The process of semantically
integrating heterogeneous biomedical databases is often riddled with
imperfections. The quality of data-driven drug discovery relies on the accuracy
of the mining methods used and the data's quality as well. Thus, having
complete and refined biomedical knowledge graphs is central to achieving more
accurate drug discovery outcomes. Here we propose using the latest graph
representation learning and embedding models to refine and complete biomedical
knowledge graphs. This preliminary work demonstrates learning discrete
representations of the integrated biomedical knowledge graph Chem2Bio2RD [3].
We perform a knowledge graph completion and refinement task using a simple
top-K cosine similarity measure between the learned embedding vectors to
predict missing links between drugs and targets present in the data. We show
that this simple procedure can be used alternatively to binary classifiers in
link prediction.
