推动学术知识组织的AI化，促进跨领域协作与创新。
Charting the Future of Scholarly Knowledge with AI: A Community Perspective
- 提出跨学科对话框架，整合不同领域的知识组织工具
- 识别共性挑战并梳理未来研究方向，助力系统化发展
- 适合关注学术智能、知识图谱与科研效率的研究者
尽管支持学术知识提取与组织的工具日益增多，许多研究人员仍依赖手动方法，部分原因是对现有技术不熟悉或缺乏适用于特定领域的解决方案。与此同时，各学科论文数量迅速增长，使研究人员难以及时跟进，凸显了采用可扩展的AI方法来结构化和整合学术知识的迫切需求。不同研究社区已独立开展相关工作，开发出可靠、动态且可查询的学术知识库工具与框架，但彼此间交流有限，阻碍了方法、模型与最佳实践的共享，延缓了更一体化解决方案的进展。本文旨在促进跨学科对话，识别共同挑战，分类协作机会，并规划未来研究方向，推动学术知识组织的发展。
原文摘要 · Abstract (English)
Despite the growing availability of tools designed to support scholarly knowledge extraction and organization, many researchers still rely on manual methods, sometimes due to unfamiliarity with existing technologies or limited access to domain-adapted solutions. Meanwhile, the rapid increase in scholarly publications across disciplines has made it increasingly difficult to stay current, further underscoring the need for scalable, AI-enabled approaches to structuring and synthesizing scholarly knowledge. Various research communities have begun addressing this challenge independently, developing tools and frameworks aimed at building reliable, dynamic, and queryable scholarly knowledge bases. However, limited interaction across these communities has hindered the exchange of methods, models, and best practices, slowing progress toward more integrated solutions. This manuscript identifies ways to foster cross-disciplinary dialogue, identify shared challenges, categorize new collaboration and shape future research directions in scholarly knowledge and organization.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。