# KG-Hub -- Building and Exchanging Biological Knowledge Graphs

arXiv: https://arxiv.org/abs/2302.10800 · HTML: https://mlsanity.com/paper/2302.10800/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 58 references that are in the map.
- First posted on arXiv 31 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [A Review of Relational Machine Learning for Knowledge Graphs](https://mlsanity.com/paper/1503.00759/) (2015)
- [GRAM: Graph-based Attention Model for Healthcare Representation Learning](https://mlsanity.com/paper/1611.07012/) (2016)
- [Graph Representation Learning in Biomedicine](https://mlsanity.com/paper/2104.04883/) (2021)
- [Drug Repurposing for COVID-19 via Knowledge Graph Completion](https://mlsanity.com/paper/2010.09600/) (2020)
- [Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science](https://mlsanity.com/paper/2203.13906/) (2022)
- [Repurpose Open Data to Discover Therapeutics for COVID-19 using Deep Learning](https://mlsanity.com/paper/2005.10831/) (2020)
- [GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding](https://mlsanity.com/paper/2110.06196/) (2021)

## Built on by (most cited first, 6 of 6)

- [An Open-Source Knowledge Graph Ecosystem for the Life Sciences](https://mlsanity.com/paper/2307.05727/) (2023)
- [MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction](https://mlsanity.com/paper/2406.08649/) (2024)
- [Traceable LLM-based validation of statements in knowledge graphs](https://mlsanity.com/paper/2409.07507/) (2024)
- [From GNNs to Trees: Multi-Granular Interpretability for Graph Neural Networks](https://mlsanity.com/paper/2505.00364/) (2025)
- [Automating SPARQL Query Translations between DBpedia and Wikidata](https://mlsanity.com/paper/2507.10045/) (2025)
- [Generating Drug Repurposing Hypotheses through the Combination of Disease-Specific Hypergraphs](https://mlsanity.com/paper/2311.09596/) (2023)

## Abstract (arXiv)

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.