  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
knowledge graphs is lacking. Here we present KG-Hub, a platform that enables
standardized construction, exchange, and reuse of knowledge graphs. Features
include a simple, modular extract-transform-load (ETL) 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 knowledge graphs. KG-Hub is also tightly integrated with graph
machine learning (ML) tools which allow automated graph machine learning,
including node embeddings and training of models for link prediction and node
classification.
