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