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Knowledge Graphs have been one of the fundamental methods for integratingheterogeneous data sources. Integrating heterogeneous data sources is crucial,especially in the biomedical domain, where central data-driven tasks such asdrug discovery rely on incorporating information from different biomedicaldatabases. These databases contain various biological entities and relationssuch as proteins (PDB), genes (Gene Ontology), drugs (DrugBank), diseases(DDB), and protein-protein interactions (BioGRID). The process of semanticallyintegrating heterogeneous biomedical databases is often riddled withimperfections. The quality of data-driven drug discovery relies on the accuracyof the mining methods used and the data's quality as well. Thus, havingcomplete and refined biomedical knowledge graphs is central to achieving moreaccurate drug discovery outcomes. Here we propose using the latest graphrepresentation learning and embedding models to refine and complete biomedicalknowledge graphs. This preliminary work demonstrates learning discreterepresentations of the integrated biomedical knowledge graph Chem2Bio2RD [3].We perform a knowledge graph completion and refinement task using a simpletop-K cosine similarity measure between the learned embedding vectors topredict missing links between drugs and targets present in the data. We showthat this simple procedure can be used alternatively to binary classifiers inlink prediction.

Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure