用AI辅助工具简化生物数据模式的编写与维护。
SchemaLink: An Intelligent Web Editor for LinkML Schema Curation
- 基于图形界面和RAG技术，支持新手快速构建LinkML模式。
- 通过AI辅助生成和编辑，提升模式一致性与质量。
- 适合生物数据管理员、非专业开发人员使用。
LinkML是一种适用于表示各类生物医学数据结构与内容约束的语言。尽管提出时间不长，已在多个生物医学场景中应用。然而，开发和维护LinkML模式对新手而言仍面临诸多挑战，非专业生物数据管理员可能因不熟悉语法和最佳实践而耗费大量时间和精力。本文提出SchemaLink，一个基于Web的图形化环境，用于构建和增强LinkML模式，满足以下需求：(i) 提供图形化语言以定义LinkML模式；(ii) 统一相似场景下模式的规范表达；(iii) 利用基于RAG的方法，帮助用户从零开始创建或编辑已有模式，简化设计与维护流程。多项实验分析表明，借助AI编辑功能可显著提升生成模式的质量。SchemaLink已上线：https://SchemaLink.biodata.di.unimi.it。代码与测试数据在GitHub开源：https://github.com/AnacletoLAB/{schemalink-webapp,schemalink-api}。
原文摘要 · Abstract (English)
Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data. Even if it is a quite recent proposal, it has been applied in several biomedical contexts. Developing and maintaining LinkML schemas presents several challenges, particularly for novice curators. Non-expert bio-curators may struggle with LinkML syntax and best practices, requiring significant time and effort to develop well-structured schemas. Results: In this paper we propose SchemaLink, a web-based environment for the graphical construction and enhancement of LinkML schemas that address the following requirements: $(i)$ introduce a graphical language for the specification of LinkML schemas, $(ii)$ make uniform the specification of schemas in similar contexts, $(iii)$ simplify the design and curation processes by exploiting a RAG-based approach to assist curators in creating new schemas from scratch and editing already developed ones. Several experimental analyses show the quality of the produced LinkML schemas through the AI-based editing facilities. Availability and Implementation: SchemaLink is available online at: https://SchemaLink.biodata.di.unimi.it. SchemaLink code and testing data are available as open-source on GitHub at: https://github.com/AnacletoLAB/{schemalink-webapp,schemalink-api}.
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