Sierra Moxon (Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA), Deepak Unni (Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany), Michael Bada (Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA), Matt Brush (Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA), Richard Bruskiewich (Star Informatics, Sooke, BC, Canada), Chris Mungall (Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA), Vlado Dancik (Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA), Michael Dumontier (Institute of Data Science, Maastricht University, Maastricht, The Netherlands), Karamarie Fecho (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA), Gwênlyn Glusman (Institute for Systems Biology, Seattle, WA, USA), Jennifer J. Hadlock (Institute for Systems Biology, Seattle, WA, USA), Nomi Harris (Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA), Arpita Joshi (Institute for Systems Biology, Seattle, WA, USA), Tim Putman (Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA), Guangrong Qin (Institute for Systems Biology, Seattle, WA, USA), Stephen A. Ramsey (Institute for Systems Biology, Seattle, WA, USA), Kent A. Shefchek (Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA), Harold Solbrig (Johns Hopkins University, Baltimore, MD, USA), Karthik Soman (University of California San Francisco), Anne T. Thessen (Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA), Melissa A. Haendel (Center for Health AI, University of Colorado Anschutz Medical Campus, Aurora, CO, USA), Chris Bizon (Renaissance Computing Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA), Biomedical Data Translator Consortium (LBNL), Paul Clemons (Chemical Biology and Therapeutics Science Program, Broad Institute, Cambridge, MA, USA)

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these ‘knowledge graphs’ (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally- accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates), representing biomedical entities such as gene, disease, chemical, anatomical structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in the Biomedical Data Translator Consortium.