| --- |
| 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} |
| } |
| ``` |
|
|
