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. 2023 Jul 1;39(7):btad418.
doi: 10.1093/bioinformatics/btad418.

KG-Hub-building and exchanging biological knowledge graphs

J Harry Caufield  1 , Tim Putman  2 , Kevin Schaper  2 , Deepak R Unni  3 , Harshad Hegde  1 , Tiffany J Callahan  4 , Luca Cappelletti  5 , Sierra A T Moxon  1 , Vida Ravanmehr  6 , Seth Carbon  1 , Lauren E Chan  7 , Katherina Cortes  2 , Kent A Shefchek  2 , Glass Elsarboukh  2 , Jim Balhoff  8 , Tommaso Fontana  9 , Nicolas Matentzoglu  10 , Richard M Bruskiewich  11 , Anne E Thessen  2 , Nomi L Harris  1 , Monica C Munoz-Torres  2 , Melissa A Haendel  2 , Peter N Robinson  12 , Marcin P Joachimiak  1 , Christopher J Mungall  1 , Justin T Reese  1
Affiliations

KG-Hub-building and exchanging biological knowledge graphs

J Harry Caufield et al. Bioinformatics. .

Abstract

Motivation: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of KGs is lacking.

Results: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of KGs. Features include a simple, modular extract-transform-load pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate KGs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

Availability and implementation: https://kghub.org.

PubMed Disclaimer

Conflict of interest statement

None declared.

Figures

Figure 1.
Figure 1.
Integration of instance data and ontologies into knowledge graphs using KG-Hub ETL (extract, transform, and load) tooling to create new, emergent knowledge that is not present in any one data source. KG-Hub tooling comprises download (kghub-downloader), transform (Koza), and merge (KGX) components. When combined, data can provide new knowledge such as indirect relationships between patient phenotypes and drugs.
Figure 2.
Figure 2.
KG projects currently included in KG-Hub. KG-Hub currently hosts seven KG projects, which integrate disease, drug/chemical, gene/protein, phenotype, and other data. Graph projects may contain both ontology and instance data. Many KG-Hub projects are constructed around a core set of ontologies related to biomedicine: GO (gene ontology/gene function), Mondo (human diseases), HPO (human disease phenotypes), and ChEBI (drugs/chemicals).
Figure 3.
Figure 3.
Schematic of tooling integrated into KG-Hub. Software developers store ETL code on GitHub. Automated builds are orchestrated on a KG-Hub server using Jenkins. Optionally, graph ML tasks can be specified for each build using NEAT yaml, and are executed using GRAPE. KGs can be directly loaded into graph databases (Neo4j, Blazegraph) using KGX, a Python library for working with graphs. Graph builds, graph ML output, provenance, and other artifacts are stored on the cloud (S3). Project summary data can be browsed on KGHub.org, and graphs and other artifacts can be browsed and downloaded on KGHub.io. A dashboard (https://kghub.org/kg-hub-dashboard/) displays detailed graph statistics for KG projects.
See this image and copyright information in PMC

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