BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets

2026-04-28 · Richard A. A. Jonker, Bárbara Maria Ribeiro de Abreu Martins, Sérgio Matos arxiv

This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. In the core of this framework is a graphlet-anchored generation process, where small subgraphs from a Knowledge Graph (KG) are used in a structured prompt to control the complexity and ensure the factual grounding of questions generated by Large Language Models. The first instantiation of this framework is BioGraphletQA, a new biomedical KGQA dataset of 119,856 QA pairs. Each entry is grounded in a graphlet of up to five nodes from the OREGANO KG, with most of the pairs being enriched with relevant document snippets from PubMed. We start by demonstrating the framework's value and the dataset's quality through evaluation by a domain expert on 106 QA pairs, confirming the high scientific validity and complexity of the generated data. Secondly, we establish its practical utility by showing that augmenting downstream benchmarks with our data improves accuracy on PubMedQA from 49.2% to 68.5% in a low-resource setting, and on MedQA from a 41.4% baseline to 44.8% in a full-resource setting. Our framework provides a robust and generalizable solution for creating critical resources to advance complex QA tasks, including MCQA and KGQA. All resources supporting this work, including the dataset (https://zenodo.org/records/17381119) and framework code (https://github.com/ieeta-pt/BioGraphletQA), are publicly available to facilitate use, reproducibility and extension.

📄 PDF Abstract BibTeX arXiv:2604.26048

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Tasks

Question Answering

Results from the Paper

Rank	Task	Dataset	Model	Metrics
#24	Question Answering	MedQA	BioGraphletQA	Accuracy: 41.4
#34	Question Answering	PubMedQA	BioGraphletQA	Accuracy: 49.2

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