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Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science
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Abstract
Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among 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, anatomic 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.
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Cite it as
@article{unni2022biolink,
title = {Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science},
author = {Deepak Raveendran Unni and Sierra A. Taylor Moxon and Michael A. Bada and Matthew Brush and Richard Bruskiewich and John Harry Caufield and Paul A. Clemons and Vlado Dančík and Michel Dumontier and Karamarie Fecho and Gustavo Glusman and Jennifer Hadlock and Nomi L. Harris and Arpita Joshi and Tim Putman and Guangrong Qin and Stephen A. Ramsey and Kent Shefchek and Harold R. Solbrig and Karthik Soman and Anne Thessen and Melissa Anne Haendel and Chris Bizon and Chris Mungall},
year = {2022},
journal = {Clinical and Translational Science},
publisher = {Wiley},
volume = {15},
number = {8},
pages = {1848--1855},
doi = {10.1111/cts.13302},
}