| --- |
| language: |
| - en |
| task_categories: |
| - question-answering |
| tags: |
| - biomedical |
| - knowledge-graph |
| - reasoning |
| - benchmark |
| pretty_name: ImmunoGraphBench2027 |
| configs: |
| - config_name: medium |
| default: true |
| data_files: |
| - split: test |
| path: viewer/medium_final20k_corrected.parquet |
| - config_name: easy |
| data_files: |
| - split: test |
| path: viewer/easy_final_50000.parquet |
| - config_name: hard_context_swap |
| data_files: |
| - split: test |
| path: viewer/hard_context_swap_19985.parquet |
| - config_name: hard_context_quantity |
| data_files: |
| - split: test |
| path: viewer/hard_gold_context_no_advantage_3000.parquet |
| - config_name: hard_structural_loss |
| data_files: |
| - split: test |
| path: viewer/hard_structural_loss_10000.parquet |
| --- |
| |
| # ImmunoGraphBench2027 |
|
|
| An anonymized review release for studying the contribution of relation-level |
| context to biomedical knowledge-graph reasoning. |
|
|
| ## Included benchmark data |
|
|
| | Configuration | Instances | Description | |
| |---|---:|---| |
| | `easy` | 50,000 | Direct factual QA | |
| | `medium` | 20,000 | Final corrected compositional QA | |
| | `hard_context_swap` | 19,985 | Relation-context swap intervention | |
| | `hard_context_quantity` | 3,000 | Gold context has no quantity advantage | |
| | `hard_structural_loss` | 10,000 | Partial graph-structure removal | |
|
|
| Hard instances are interventions on Medium instances, not an independent pool |
| of unrelated questions. Do not sum these rows to claim independent examples. |
| `test` denotes an evaluation collection, not an established train/test split. |
|
|
| Use the configuration selector above to inspect every instance in the Dataset |
| Viewer. `data/` contains full nested JSONL records (gzip compressed). `viewer/` |
| contains the same sanitized records in Parquet with stable, readable columns. |
|
|
| ## Quick loading |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| |
| ds = load_dataset("ImmunoGraph-Bench/ImmunoGraphBench2027", "medium", split="test") |
| print(ds[0]["question"]) |
| qa = json.loads(ds[0]["record_json"]) |
| ``` |
|
|
| The viewer columns are `qa_id`, `question`, `answer`, `level`, `subtype`, |
| `source_medium_qa_id`, and `record_json`. The last column is the complete QA |
| object, including its available graph, context and evaluation annotations. |
| For a non-string answer, the viewer's `answer` is JSON-encoded; the nested |
| record remains authoritative. |
|
|
| ## Record contents and leakage precautions |
|
|
| Medium/Hard records include `question`, `short_answer`, `gold`, |
| `candidate_subgraph`, and intervention-specific fields where applicable. |
| Easy records instead use `answer`, `gold_triples`, `gold_path`, and `context`. |
| Detailed fields can vary by subtype and intervention. |
|
|
| **Do not send the entire QA object to a model.** Gold answers, selected-candidate |
| annotations and validation metadata are evaluation-only information. Inputs |
| must be rendered from the fields appropriate to the experimental condition. |
| Stored structural-loss `model_inputs` are preserved verbatim; other experiments |
| must use their appropriate condition-specific renderer. |
|
|
| ## Minimal evaluation code |
|
|
| The `code/` directory contains dependency-free Python demos: |
|
|
| ```sh |
| python code/evaluate_predictions.py --qa data/Medium/medium_final20k_corrected.jsonl.gz --predictions predictions.jsonl |
| python -m unittest discover -s code -p 'test_*.py' -v |
| ``` |
|
|
| Predictions use one condition per file, with `qa_id` and `answer` (a string or |
| single-element list). Optional `--aliases aliases.json` accepts an explicitly |
| provided QA-ID-to-answer-variants mapping. This demo reports simple normalized |
| exact matching; **it is not the paper's full biomedical alias adjudication**. |
| `score_frozen.py` separately aggregates existing boolean judgments with an |
| explicit error policy. See [code documentation](code/README.md). |
|
|
| ## Release integrity and anonymization |
|
|
| The published questions, answers, scientific entity IDs, graph relations, |
| context text and numeric metadata are retained from the frozen source records. |
| Historical pre-repair questions and internal repair/provenance bookkeeping are |
| omitted. Absolute `source_file` paths are reduced to basenames. No original Git |
| history, personal repository links, credentials or internal audit logs are |
| part of this release. Scientific source citations are retained. |
|
|
| Per-file row counts, sizes and SHA-256 digests are supplied in |
| `data_manifest.json` and `viewer_manifest.json`. |
|
|
| ## Scope and limitations |
|
|
| This initial review deposit contains the five benchmark collections above and |
| minimal offline evaluation code. It does not yet include the full KG, the full |
| historical construction pipeline, all model replies, embedding analyses, or |
| the separate retrieval/random-context/Q2 experiment inputs. It is not a claim |
| of one-command reproduction of every paper table. |
|
|
| ## Source attribution and rights |
|
|
| Records retain available source identifiers and evidence citations. Public |
| access to this review deposit does not assign a new blanket licence to |
| third-party database material or literature excerpts. Applicable source terms |
| remain in effect. A project-wide code/data licence has not yet been assigned; |
| no unrestricted redistribution rights are asserted here. |
|
|
| This resource is for research evaluation, not clinical decision-making. |
|
|
