---
pretty_name: PrimeKG-CL Benchmark v1.0 (Anon)
license: cc-by-4.0
task_categories:
- other
language:
- en
tags:
- biomedical
- knowledge-graph
- continual-learning
- link-prediction
size_categories:
- 10M<n<100M
---

# PrimeKG-CL Benchmark v1.0 (Anon)

PrimeKG-CL is a continual graph learning benchmark built from an evolving biomedical knowledge graph with real temporal change.

## Dataset Summary

- Two temporal snapshots (`t0`, `t1`) of a biomedical KG.
- Temporal diff data for added/removed/persistent facts.
- 10 continual-learning tasks with train/valid/test splits.
- Stratified evaluation metadata for retention and forgetting analysis.
- Multimodal node features (text + molecular + graph tensors).

### Snapshots

- `snapshots/kg_t0.csv` — June 2021 PrimeKG release: 8,100,498 triples, 129,375 nodes, 30 relations.
- `snapshots/kg_t1.csv` — July 2023 reconstruction from nine upstream databases (Bgee, CTD, GO, Gene2GO, HPO, HPOA, MONDO, Uberon, HGNC): 13,001,666 triples, 134,211 nodes, 25 relations.
- `snapshots/t1_build_info.json` — provenance and database versions for the `t1` reconstruction.

### Temporal diff (`t0 -> t1`)

- Added: 5,760,234 triples
- Removed: 888,848 triples
- Persistent: 7,208,624 triples

Detailed breakdown is in `diffs/diff_t0_t1.json`.

## Data Structure

### Continual tasks

Tasks are in `tasks/task_*`. Each task directory contains:

- `train.txt`
- `valid.txt`
- `test.txt`

Each line is a tab-separated triple:

- `head_id<TAB>relation<TAB>tail_id`

Split ratio: 70/10/20.

Task sizes:

- `task_0_base`: 8,100,498 triples (full `t0`)
- `task_1_disease_related`: 17,009
- `task_1_drug_related`: 125,343
- `task_2_disease_related`: 115,382
- `task_2_gene_protein`: 2,850,593
- `task_3_gene_protein`: 99,761
- `task_3_phenotype_related`: 47,997
- `task_4_biological_process`: 116,118
- `task_4_phenotype_related`: 57,390
- `task_5_anatomy_pathway`: 2,752,675

### Stratified evaluation

`test_stratification.json` partitions each task's test triples into:

- **persistent** (in both snapshots)
- **added** (new in `t1`)
- **removed** (only in `t0`)

This supports stratified-MRR evaluation of retention vs forgetting.

### Multimodal features

- `features/text_embeddings.pt` — BiomedBERT [CLS] embeddings projected to 256-d (36% entity coverage).
- `features/mol_features.pt` — Morgan fingerprints (radius 2, 1024 bits) for drugs with SMILES (4% entity coverage).
- `features/edge_index.pt`, `features/edge_type.pt` — R-GCN tensors for the full `t0` graph.
- `features/node_has_text.pt`, `features/node_has_mol.pt` — feature availability masks.
- `features/node_index_map.csv` — global `entity_id <-> row_index` mapping.
- `features/vocab_sizes.json` — entity/relation vocabulary sizes.

## Files

- `LICENSE` — MIT (code) + CC BY 4.0 (data)
- `croissant.json` — MLCommons Croissant metadata
- `README.md` — dataset card
- `statistics.json` — benchmark summary stats
- `test_stratification.json` — per-task persistent/added/removed counts
- `diffs/diff_t0_t1.json` — full temporal diff
- `snapshots/` — `kg_t0.csv`, `kg_t1.csv`, provenance metadata
- `tasks/` — continual-learning task splits
- `features/` — multimodal node features

## Intended Uses

- Continual knowledge graph completion
- Continual link prediction
- Retention/forgetting analysis under temporal KG evolution
- Multimodal CGL ablations using structure/text/molecular signals

## Limitations

- Dataset quality depends on upstream biomedical resource quality and curation biases.
- Restrictively licensed sources (DrugBank, UMLS, DrugCentral, SIDER, DisGeNET) were not re-queried for `t1`; corresponding `t0` edges were carried forward unchanged.
- This benchmark is for machine learning research and is not intended for clinical decision-making.

## Licensing

- Data files: CC BY 4.0 (inheriting PrimeKG terms)
- Code/configuration artifacts: MIT

Users are responsible for compliance with applicable upstream database licenses.

## Citation

```bibtex
@inproceedings{primekgcl2026,
  title={PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs},
  author={Anonymous},
  booktitle={NeurIPS Datasets and Benchmarks Track},
  year={2026}
}
```