Within clinical, biomedical, and translational science, an increasing numberof projects are adopting graphs for knowledge representation. Graph-based datamodels 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 acrossthese "knowledge graphs" (KGs) has remained difficult. Data set heterogeneityand complexity; the proliferation of ad hoc data formats; poor compliance withguidelines on findability, accessibility, interoperability, and reusability;and, in particular, the lack of a universally-accepted, open-access model forstandardization across biomedical KGs has left the task of reconciling datasources to downstream consumers. Biolink Model is an open source data modelthat can be used to formalize the relationships between data structures intranslational science. It incorporates object-oriented classification andgraph-oriented features. The core of the model is a set of hierarchical,interconnected classes (or categories) and relationships between them (orpredicates), representing biomedical entities such as gene, disease, chemical,anatomical structure, and phenotype. The model provides class and edgeattributes and associations that guide how entities should relate to oneanother. Here, we highlight the need for a standardized data model for KGs,describe Biolink Model, and compare it with other models. We demonstrate theutility of Biolink Model in various initiatives, including the Biomedical DataTranslator Consortium and the Monarch Initiative, and show how it has supportedeasier integration and interoperability of biomedical KGs, bringing togetherknowledge from multiple sources and helping to realize the goals oftranslational science.