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README.md

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+---
+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}
+}
+```