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--- |
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license: apache-2.0 |
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task_categories: |
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- question-answering |
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- multiple-choice |
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language: |
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- en |
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tags: |
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- biology |
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- medical |
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formats: |
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- csv |
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pretty_name: biomixQA |
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size_categories: |
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- n<1K |
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--- |
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# BiomixQA Dataset |
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## Overview |
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BiomixQA is a curated biomedical question-answering dataset comprising two distinct components: |
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1. Multiple Choice Questions (MCQ) |
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2. True/False Questions |
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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. |
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Hence, this dataset is designed to support research and development in biomedical natural language processing, knowledge graph reasoning, and question-answering systems. |
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## Dataset Description |
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- **Repository:** https://github.com/BaranziniLab/KG_RAG |
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- **Paper:** [Biomedical knowledge graph-optimized prompt generation for large language models](https://arxiv.org/abs/2311.17330) |
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- **Point of Contact:** [Karthik Soman](mailto:[email protected]) |
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## Dataset Components |
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### 1. Multiple Choice Questions (MCQ) |
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- **File**: `mcq_biomix.csv` |
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- **Size**: 306 questions |
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- **Format**: Each question has five choices with a single correct answer |
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### 2. True/False Questions |
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- **File**: `true_false_biomix.csv` |
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- **Size**: 311 questions |
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- **Format**: Binary (True/False) questions |
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## Potential Uses |
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1. Evaluating biomedical question-answering systems |
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2. Testing natural language processing models in the biomedical domain |
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3. Assessing retrieval capabilities of various RAG (Retrieval-Augmented Generation) frameworks |
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4. Supporting research in biomedical ontologies and knowledge graphs |
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## Source Data |
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1. SPOKE: A large scale biomedical knowledge graph that consists of ~40 million biomedical concepts and ~140 million biologically meaningful relationships (Morris et al. |
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2023). |
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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 |
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al. 2016). |
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3. MONDO: Provides information about the ontological classification of Disease entities in the Open Biomedical Ontologies (OBO) format (Vasilevsky et al. 2022). |
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4. SemMedDB: Contains semantic predications extracted from PubMed citations (Kilicoglu et al. 2012). |
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5. Monarch Initiative: A platform for disease-gene association data (Mungall et al. 2017). |
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6. ROBOKOP: A knowledge graph-based system for biomedical data integration and analysis (Bizon et al. 2019). |
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## Citation |
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If you use this dataset in your research, please cite the following paper: |
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``` |
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@article{soman2023biomedical, |
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title={Biomedical knowledge graph-enhanced prompt generation for large language models}, |
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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}, |
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journal={arXiv preprint arXiv:2311.17330}, |
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year={2023} |
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} |
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``` |
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