BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding

[2601.04524] BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2601.04524 (cs) [Submitted on 8 Jan 2026 ( v1 ), last revised 28 Aug 2026 (this version, v3)] Title: BioPIE: A Biomedical Protocol Information Extraction Dataset for Experiment Understanding Authors: Haofei Hou , Shunyi Zhao , Fanxu Meng , Kairui Yang , Lecheng Ruan , Qining Wang View PDF HTML (experimental) Abstract: Understanding biomedical experiments provides a foundation for downstream tasks, e.g., laboratory automation, and facilitates effective cross-disciplinary communication. Two challenges, High Information Density (HID) and Multi-Step Reasoning (MSR), pose unique difficulties for precise automatic experimental understanding. Extracting structured knowledge, e.g., Knowledge Graphs (KGs), is an effective approach to address the HID and MSR. However, existing biomedical datasets for structured knowledge Information Extraction (IE) are limited to a general or coarse-grained level, hindering fine-grained experimental understanding. To address this gap, we introduce Biomedical Protocol Information Extraction Dataset (BioPIE), a dataset providing procedure-centric KGs that captures entities, actions, and relations at a scale sufficient for reasoning across biomedical protocols. We evaluate both supervised and LLM-based IE methods on BioPIE to verify its effectiveness, and implement a biomedical question answering system to provide a quantitative illustration of BioPIE's effectiveness for downstream understanding tasks. The experimental results demonstrate improved understanding performance on both the HID and MSR question sets. Comments: Accepted to Findings of EMNLP 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2601.04524 [cs.AI] (or arXiv:2601.04524v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2601.04524 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Haofei Hou [ view email ] [v1] Thu, 8 Jan 2026 02:44:37 UTC (4,441 KB) [v2] Tue, 23 Jun 2026 09:19:52 UTC (4,621 KB) [v3] Fri, 28 Aug 2026 08:07:45 UTC (4,636 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.AI < prev | next > new | recent | 2026-01 Change to browse by: cs References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

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