README.md

metadata

license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
tags:
  - knowledge-graph
  - biomedical
  - sparql
  - compositional-qa
  - multi-hop
pretty_name: KORA Benchmark
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: full
        path: benchmark/benchmark_clean.csv
  - config_name: splits
    data_files:
      - split: train
        path: benchmark/train.csv
      - split: validation
        path: benchmark/val.csv
      - split: test
        path: benchmark/test.csv

KORA Benchmark

Resources for reproducing KORA: Adaptive Multi-Agent Orchestrated Retrieval over Knowledge Graphs — including the BioCQ benchmark dataset, entity resolution indexes, and the combined biomedical knowledge graph.

Repository Contents

Path	Description
benchmark/	BioCQ question splits (train / val / test / full)
indexes/scispacy*/	Pre-built SciSpaCy entity resolution indexes (~1 GB)
indexes/ark_bm25/	Pre-built ARK BM25 retrieval indexes (~406 MB)
kg/kg.nt	Combined RDF knowledge graph (PrimeKG + OptimusKG + AfrOMedKG) in N-Triples format
kg/virtuoso_clean.ini	Virtuoso SPARQL server config template

BioCQ Dataset

8,578 compositional biomedical questions across five reasoning types:

Question Type	Questions
Multi-hop Traversal	2,094
Aggregation & Counting	1,991
Constrained Retrieval	1,784
Differential Diagnosis	1,437
Knowledge Gap	1,272
Total	8,578

Questions are grounded in three biomedical knowledge graphs: PrimeKG, OptimusKG, and AfrOMedKG.

Usage

Load the benchmark

import pandas as pd

train = pd.read_csv("benchmark/train.csv")
val   = pd.read_csv("benchmark/val.csv")
test  = pd.read_csv("benchmark/test.csv")

Or via the HuggingFace datasets library:

from datasets import load_dataset

# Full dataset (8,578 questions)
ds = load_dataset("anonymous-kgqa/KORA-Benchmark")["full"]

# Pre-defined train / val / test splits
splits = load_dataset("anonymous-kgqa/KORA-Benchmark", "splits")
train, val, test = splits["train"], splits["validation"], splits["test"]

Download SciSpaCy entity resolution indexes

hf download anonymous-kgqa/KORA-Benchmark --repo-type=dataset \
  --include "indexes/scispacy*" --local-dir kora/kg/index_data

Download ARK BM25 indexes

hf download anonymous-kgqa/KORA-Benchmark --repo-type=dataset \
  --include "indexes/ark_bm25/*" --local-dir baselines/ark/temp && \
mv baselines/ark/temp/indexes/ark_bm25/*.pkl baselines/ark/temp/ && \
rm -rf baselines/ark/temp/indexes

Download the knowledge graph

hf download anonymous-kgqa/KORA-Benchmark --repo-type=dataset \
  --include "kg/*" --local-dir data/kg

Load kg/kg.nt into Virtuoso using the provided kg/virtuoso_clean.ini template. See the KORA repository for full setup instructions.

License

CC BY 4.0