Datasets:

ImmunoGraph-Bench
/

ImmunoGraphBench2027

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

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:

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.

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.