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    <abstract>Comprehensive pathway datasets are essential resources for advancing biological research, yet constructing these datasets is labor intensive. Recognizing the labor-intensive nature of constructing these critical resources, we present BioGraphia, a web-based annotation platform designed to facilitate collaborative pathway graph annotation. BioGraphia supports multi-user collaboration with real-time monitoring, curation, and interactive pathway graph visualization. It enables users to directly annotate the nodes and relations on the candidate graph, guided by detailed instructions. The platform is further enhanced with a large language model that automatically generates explainable and span-aligned pre-annotation to accelerate the annotation process. Its modular design allows flexible integration of external knowledge bases, and customization of the definition of annotation schema and, to support adaptation to other graph-based annotation tasks. Code is available at https://github.com/LeiLiLab/BioGraphia</abstract>
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%0 Conference Proceedings
%T BioGraphia: A LLM-Assisted Biological Pathway Graph Annotation Platform
%A Xu, Xi
%A Jo, Sumin
%A Officer, Adam
%A Chen, Angela
%A Huang, Yufei
%A Li, Lei
%Y Habernal, Ivan
%Y Schulam, Peter
%Y Tiedemann, Jörg
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-334-0
%F xu-etal-2025-biographia
%X Comprehensive pathway datasets are essential resources for advancing biological research, yet constructing these datasets is labor intensive. Recognizing the labor-intensive nature of constructing these critical resources, we present BioGraphia, a web-based annotation platform designed to facilitate collaborative pathway graph annotation. BioGraphia supports multi-user collaboration with real-time monitoring, curation, and interactive pathway graph visualization. It enables users to directly annotate the nodes and relations on the candidate graph, guided by detailed instructions. The platform is further enhanced with a large language model that automatically generates explainable and span-aligned pre-annotation to accelerate the annotation process. Its modular design allows flexible integration of external knowledge bases, and customization of the definition of annotation schema and, to support adaptation to other graph-based annotation tasks. Code is available at https://github.com/LeiLiLab/BioGraphia
%U https://aclanthology.org/2025.emnlp-demos.34/
%P 480-486

Markdown (Informal)

[BioGraphia: A LLM-Assisted Biological Pathway Graph Annotation Platform](https://aclanthology.org/2025.emnlp-demos.34/) (Xu et al., EMNLP 2025)

• BioGraphia: A LLM-Assisted Biological Pathway Graph Annotation Platform (Xu et al., EMNLP 2025)

ACL

• Xi Xu, Sumin Jo, Adam Officer, Angela Chen, Yufei Huang, and Lei Li. 2025. BioGraphia: A LLM-Assisted Biological Pathway Graph Annotation Platform. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 480–486, Suzhou, China. Association for Computational Linguistics.