| # PrimeKG-CL Benchmark v1.0 |
|
|
| A continual graph learning benchmark on a real biomedical knowledge graph |
| with genuine temporal evolution. |
|
|
| ## 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 / database versions used for the t_1 reconstruction. |
| |
| ## Temporal diff (t_0 -> t_1) |
| - Added: 5,760,234 new triples (driven by GO annotations, disease-phenotype refinement, drug-target updates) |
| - Removed: 888,848 deprecated triples (retracted associations, ontology corrections) |
| - Persistent: 7,208,624 triples unchanged across both snapshots |
| |
| Full breakdown in `diffs/diff_t0_t1.json`. |
| |
| ## Continual learning tasks |
| 10 entity-type-grouped tasks (`tasks/task_*`). Each directory contains |
| `train.txt`, `valid.txt`, `test.txt`, one tab-separated triple per line |
| (`head_id<TAB>relation<TAB>tail_id`). 70/10/20 train/valid/test split. |
| |
| ``` |
| task_0_base 8,100,498 triples (full t_0) |
| 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` — per-task partition of test triples into |
| **persistent** (in both snapshots), **added** (new in t_1), and |
| **removed** (only in t_0) strata, supporting stratified-MRR analysis of |
| correct retention vs. correct forgetting. |
| |
| Task 0's test split: 1,443,243 persistent + 176,856 removed = 1,620,099 total. |
| |
| ## Multimodal features (`features/`) |
| - `text_embeddings.pt` — BiomedBERT [CLS] embeddings projected to 256-d. |
| Coverage: 36% of entities (those with textual descriptions). |
| - `mol_features.pt` — Morgan fingerprints (radius 2, 1024 bits) for drugs |
| with available SMILES. Coverage: 4% of entities. |
| - `edge_index.pt`, `edge_type.pt` — R-GCN message-passing tensors for the |
| full t_0 graph (used by the structural encoder). |
| - `node_has_text.pt`, `node_has_mol.pt` — boolean coverage masks. |
| - `node_index_map.csv` — global entity_id <-> row_index map. |
| - `vocab_sizes.json` — entity / relation vocab sizes. |
| |
| ## Files |
| ``` |
| LICENSE MIT (code) + CC BY 4.0 (data) |
| croissant.json MLCommons Croissant metadata |
| README.md this file |
| statistics.json benchmark-level summary |
| test_stratification.json per-task persistent/added/removed counts |
| diffs/diff_t0_t1.json full t_0->t_1 diff with per-relation breakdown |
| snapshots/ kg_t0.csv, kg_t1.csv, build provenance |
| tasks/ 10 task directories with train/valid/test splits |
| features/ multimodal node features |
| ``` |
| |
| ## Citation |
| ``` |
| @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} |
| } |
| ``` |
| |
| ## License |
| - Code components: MIT. |
| - Data files: CC BY 4.0, inheriting PrimeKG's license; users must respect upstream-database licenses where applicable. |
| - See `LICENSE` for details. |
| |
