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 various initiatives, including the Biomedical Data
Translator Consortium and the Monarch Initiative, and show how it has supported
easier integration and interoperability of biomedical KGs, bringing together
knowledge from multiple sources and helping to realize the goals of
translational science.