An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Abstract
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.
Keywords
Cite
@article{arxiv.2307.05727,
title = {An Open-Source Knowledge Graph Ecosystem for the Life Sciences},
author = {Tiffany J. Callahan and Ignacio J. Tripodi and Adrianne L. Stefanski and Luca Cappelletti and Sanya B. Taneja and Jordan M. Wyrwa and Elena Casiraghi and Nicolas A. Matentzoglu and Justin Reese and Jonathan C. Silverstein and Charles Tapley Hoyt and Richard D. Boyce and Scott A. Malec and Deepak R. Unni and Marcin P. Joachimiak and Peter N. Robinson and Christopher J. Mungall and Emanuele Cavalleri and Tommaso Fontana and Giorgio Valentini and Marco Mesiti and Lucas A. Gillenwater and Brook Santangelo and Nicole A. Vasilevsky and Robert Hoehndorf and Tellen D. Bennett and Patrick B. Ryan and George Hripcsak and Michael G. Kahn and Michael Bada and William A. Baumgartner and Lawrence E. Hunter},
journal= {arXiv preprint arXiv:2307.05727},
year = {2024}
}