Charting the Future of Scholarly Knowledge with AI: A Community Perspective
This work aims to improve scholarly knowledge organization for researchers, but it is incremental as it focuses on community collaboration rather than new technical methods.
The paper addresses the challenge of managing the rapid growth of scholarly publications by highlighting the need for scalable AI approaches to structure and synthesize knowledge, and it proposes fostering cross-disciplinary dialogue to overcome fragmented community efforts and integrate solutions.
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.