Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2006.13774v1
• Date: Wed, 24 Jun 2020 14:47:33 GMT
• Title: Benchmark and Best Practices for Biomedical Knowledge Graph Embeddings
• Authors: David Chang, Ivana Balazevic, Carl Allen, Daniel Chawla, Cynthia Brandt, Richard Andrew Taylor
• Abstract summary: We train several state-of-the-art knowledge graph embedding models on the SNOMED-CT knowledge graph. We make a case for the importance of leveraging the multi-relational nature of knowledge graphs for learning biomedical knowledge representation.
• Score: 8.835844347471626
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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