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BiomixQA

Tasks:
Question Answering
Multiple Choice
Modalities:
Text
Formats:
csv
Languages:
English
Size:
< 1K
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Tags:
biology
medical
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metadata
license: apache-2.0
task_categories:
  - question-answering
  - multiple-choice
language:
  - en
tags:
  - biology
  - medical
formats:
  - csv
pretty_name: biomixQA
size_categories:
  - n<1K

BiomixQA Dataset

Overview

BiomixQA is a curated biomedical question-answering dataset comprising two distinct components:

  1. Multiple Choice Questions (MCQ)
  2. True/False Questions

This dataset has been utilized to validate the Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) framework across different Large Language Models (LLMs). The diverse nature of questions in this dataset, spanning multiple choice and true/false formats, along with its coverage of various biomedical concepts, makes it particularly suitable for assessing the performance of KG-RAG framework.

Hence, this dataset is designed to support research and development in biomedical natural language processing, knowledge graph reasoning, and question-answering systems.

Dataset Description

Dataset Components

1. Multiple Choice Questions (MCQ)

  • File: mcq_biomix.csv
  • Size: 306 questions
  • Format: Each question has five choices with a single correct answer

2. True/False Questions

  • File: true_false_biomix.csv
  • Size: 311 questions
  • Format: Binary (True/False) questions

Potential Uses

  1. Evaluating biomedical question-answering systems
  2. Testing natural language processing models in the biomedical domain
  3. Assessing retrieval capabilities of various RAG (Retrieval-Augmented Generation) frameworks
  4. Supporting research in biomedical ontologies and knowledge graphs

Source Data

  1. SPOKE: A large scale biomedical knowledge graph that consists of ~40 million biomedical concepts and ~140 million biologically meaningful relationships (Morris et al. 2023).
  2. DisGeNET: Consolidates data about genes and genetic variants linked to human diseases from curated repositories, the GWAS catalog, animal models, and scientific literature (Piñero et al. 2016).
  3. MONDO: Provides information about the ontological classification of Disease entities in the Open Biomedical Ontologies (OBO) format (Vasilevsky et al. 2022).
  4. SemMedDB: Contains semantic predications extracted from PubMed citations (Kilicoglu et al. 2012).
  5. Monarch Initiative: A platform for disease-gene association data (Mungall et al. 2017).
  6. ROBOKOP: A knowledge graph-based system for biomedical data integration and analysis (Bizon et al. 2019).

Citation

If you use this dataset in your research, please cite the following paper:

@article{soman2023biomedical,
  title={Biomedical knowledge graph-enhanced prompt generation for large language models},
  author={Soman, Karthik and Rose, Peter W and Morris, John H and Akbas, Rabia E and Smith, Brett and Peetoom, Braian and Villouta-Reyes, Catalina and Cerono, Gabriel and Shi, Yongmei and Rizk-Jackson, Angela and others},
  journal={arXiv preprint arXiv:2311.17330},
  year={2023}
}