  Much of biomedical and healthcare data is encoded in discrete, symbolic form
such as text and medical codes. There is a wealth of expert-curated biomedical
domain knowledge stored in knowledge bases and ontologies, but the lack of
reliable methods for learning knowledge representation has limited their
usefulness in machine learning applications. While text-based representation
learning has significantly improved in recent years through advances in natural
language processing, attempts to learn biomedical concept embeddings so far
have been lacking. A recent family of models called knowledge graph embeddings
have shown promising results on general domain knowledge graphs, and we explore
their capabilities in the biomedical domain. We train several state-of-the-art
knowledge graph embedding models on the SNOMED-CT knowledge graph, provide a
benchmark with comparison to existing methods and in-depth discussion on best
practices, and make a case for the importance of leveraging the
multi-relational nature of knowledge graphs for learning biomedical knowledge
representation. The embeddings, code, and materials will be made available to
the communitY.
