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Updated: Aug 5, 2025
05:47
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
404
BioASQ-QA: A manually curated corpus for Biomedical Question Answering
Anastasia Krithara1, Anastasios Nentidis2,3, Konstantinos Bougiatiotis2
1Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", Athens, Greece. akrithara@iit.demokritos.gr.
Scientific Data
|March 27, 2023
Summary
The BioASQ benchmark dataset offers realistic biomedical questions with golden answers and summaries. This dataset supports advancements in question answering, summarization, and information retrieval for experts.
Area of Science:
- Biomedical Informatics
- Natural Language Processing
Background:
- Existing question answering (QA) datasets often lack realism and complexity.
- Biomedical information needs require specialized QA datasets.
Purpose of the Study:
- Introduce the BioASQ question answering (QA) benchmark dataset.
- Provide a challenging and realistic resource for biomedical NLP research.
Main Methods:
- Dataset creation with English questions, golden standard answers, and related materials.
- Inclusion of ideal answers (summaries) for multi-document summarization research.
- Integration of structured and unstructured data, including documents, snippets, and concepts.
Main Results:
- The BioASQ dataset reflects real-world information needs of biomedical experts.
- It supports research in Information Retrieval, Passage Retrieval, and Natural Language Generation.
- Enables evaluation of paraphrasing and textual entailment methods for QA.
Conclusions:
- The BioASQ dataset is a valuable, continuously extended resource for biomedical QA.
- It facilitates progress in various NLP tasks within the biomedical domain.
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