---
pretty_name: PrimeKG-CL
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

PrimeKG-CL is a continual graph learning benchmark built on an evolving biomedical knowledge graph. It contains two temporal snapshots (`t0`, `t1`), temporal diffs, 10 continual-learning tasks, and multimodal node features for evaluating retention/forgetting under distribution shift.

This dataset repository stores the benchmark under the `benchmark/` directory.

## Dataset Summary

- **Domain:** Biomedical knowledge graphs
- **Primary use:** Continual knowledge graph completion / link prediction
- **Snapshots:**
  - `benchmark/snapshots/kg_t0.csv` (June 2021 PrimeKG release): 8,100,498 triples, 129,375 nodes, 30 relations
  - `benchmark/snapshots/kg_t1.csv` (July 2023 reconstruction): 13,001,666 triples, 134,211 nodes, 25 relations
- **Temporal change (`t0 -> t1`):**
  - Added: 5,760,234 triples
  - Removed: 888,848 triples
  - Persistent: 7,208,624 triples

## Repository Structure

- `benchmark/README.md`: benchmark documentation
- `benchmark/LICENSE`: benchmark licensing terms
- `benchmark/snapshots/`: temporal KG snapshots and build provenance
- `benchmark/diffs/diff_t0_t1.json`: full temporal diff and relation-level breakdown
- `benchmark/tasks/task_*/`: 10 continual tasks with `train.txt`, `valid.txt`, `test.txt`
- `benchmark/test_stratification.json`: persistent/added/removed test strata per task
- `benchmark/features/`: multimodal features and graph tensors
- `benchmark/statistics.json`: benchmark-level summary statistics
- `benchmark/croissant.json`: MLCommons Croissant metadata

## Data Format

- Triple files are tab-separated text with one triple per line:
  - `head_id<TAB>relation<TAB>tail_id`
- Task splits use 70/10/20 train/valid/test.
- Feature tensors are stored as `.pt` files.

## Suggested Usage

1. Use `task_0_base` as the base pretraining stage on `t0`.
2. Train/evaluate sequentially on tasks `task_1_*` through `task_5_*`.
3. Report both aggregate and stratified metrics (persistent vs added vs removed), using `benchmark/test_stratification.json`.

## Limitations and Considerations

- This benchmark reflects biomedical database curation quality and biases from source resources.
- `t1` reconstruction intentionally excludes re-querying some restrictively licensed sources; corresponding `t0` edges are carried forward unchanged.
- Performance on this benchmark does not imply clinical validity.

## Licensing

- **Data files:** CC BY 4.0 (inherits PrimeKG license)
- **Code/config components:** MIT

Users must comply with applicable upstream database licenses where required.

## Citation

If you use this dataset, please cite:

```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}
}
```