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A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

By Tiffany J Callahan, Ignacio J. Tripodi, Lawrence E Hunter, William A. Baumgartner

Posted 02 May 2020
bioRxiv DOI: 10.1101/2020.04.30.071407

Although knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms. PheKnowLator is available at: https://github.com/callahantiff/PheKnowLator. ### Competing Interest Statement The authors have declared no competing interest.

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