Charting the Future of Scholarly Knowledge with AI: A Community Perspective

2025-08-27

AI Summary

The explosive growth of academic knowledge and increasing demand for interdisciplinary integration are hindered by existing tools’ poor domain adaptability, fragmented scholarly communities, and low methodological reusability—forcing researchers to rely on inefficient manual curation. This paper proposes an interdisciplinary collaborative framework that systematically integrates AI-driven knowledge extraction, NLP-based modeling, and knowledge graph construction to enable automated, structured organization and dynamic updating of scholarly literature. Its core contributions are: (1) identifying cross-domain common challenges and establishing a unified evaluation framework; (2) designing modular, plug-and-play technical interfaces to facilitate model sharing and practical reuse; and (3) articulating a community-driven consensus pathway and strategic guidelines for sustainable collaboration. Deployed across multiple disciplines, the framework has enabled co-development of queryable, scalable, and domain-adaptive intelligent academic knowledge bases—providing a systematic solution for next-generation knowledge infrastructure.

Technology Category

• Knowledge Representation and Reasoning: Knowledge Acquisition
• Data Mining & Knowledge Management: Knowledge Acquisition from the Web
• Application Domains: Humanities & Computational Social Science

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining

Abstract

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.

Problem

• Researchers rely on manual methods due to unfamiliarity with AI tools
• Rapid publication growth necessitates scalable AI knowledge synthesis
• Limited cross-community interaction hinders integrated scholarly solutions

Innovation

• Develops AI tools for scholarly knowledge extraction
• Creates domain-adapted scalable knowledge bases
• Fosters cross-disciplinary collaboration methods