Open Graph Benchmark - BioKG (Biomedical Knowledge Graph)

Heterogeneous biomedical knowledge graph with 5 node types: drugs, diseases, proteins, side effects, and genes. Contains 51 different relation types representing various biomedical relationships (drug-target, gene-disease, protein-gene, disease-symptom, etc.). Useful for evaluating link prediction in highly heterogeneous, multi-relational knowledge graphs.

npx keepset fetch ogbl-biokg

Nodes: 93,773
Edges: 5,088,454
Size: 918.7 MiB

Fetch and preview this dataset on this page, or download the result as JSON. The command line fetches it without a browser.

Overview

Id	`ogbl-biokg`
Category	`graph/v1`
Kind	graph
Domain	Biomedical knowledge graph
Licence	CC0-1.0
Catalogue	`keepset` 1.33.0

Sources and provenance

#	URL	Status	Retrieved	Read by recipe	Pinned snapshot	SHA-256
0	https://data.pyg.org/datasets/ogbl_biokg.zip	live	2026-03-26
1	https://snap.stanford.edu/ogb/data/linkproppred/biokg.zip	live	2026-10-21	yes

Citations

Hu et al. (2020). Open Graph Benchmark: Datasets for Machine Learning on Graphs. NeurIPS.

Recipe

Source	#1
Archive	zip
Files	matching `[^/]+/raw/(?:num-node-list\|num-node-dict\|edge\|num-edge-list\|triplet-type-list\|relations/[^/]+/(?:edge\|num-edge-list))\.csv\.gz`
Inner archive	gzip
Parser format	`"ogb"`
Parser encoding	`"utf-8"`

Crosswalk

keepset/topology to graph/v1, as a crossmap crosswalk.

Rule	From	To	Direction
move	`meta`	`meta`	both
move	`nodes[].id`	`nodes[].id`	both
move	`edges[].source`	`edges[].source`	both
move	`edges[].target`	`edges[].target`	both

Expected counts

Edges	5,088,454
Nodes	93,773

Properties

Components	6
Connected	no
Cyclic	yes
Density	0.0005064
Directed	yes
Download bytes	918.7 MiB
Edge types	1
Heterogeneous	no
Multigraph	yes
Node types	2
Raw bytes	0 B
Self loops	no
Weighted	no