Problem
Research questions and friction points this paper is trying to address.
institutional repositories
research outputs
scholarly discovery
Westlake Scholar: AI-Enhanced Scholarly Discovery over an Institutional Repository
🤖 AI Summary
为解决机构库中论文关联和发现的问题，Westlake Scholar通过添加四个AI服务来增强机构库的功能，包括上下文阅读、研究方向引导的论文发现等。
📝 Abstract
Institutional repositories (IRs) provide mature infrastructure for preserving and disseminating research outputs, but conventional record- and document-centric interfaces provide limited support for connecting deposited papers to related research and people. We present Westlake Scholar, an open-source, institution-grounded platform that adds four complementary artificial intelligence (AI) services to repository infrastructure: contextual paper reading, research-direction-guided paper discovery, publication-grounded expert discovery, and AI-generated research chronologies for scholars. The services draw on a shared institutional knowledge layer connecting approved publication records, paper content, and scholar--publication relationships. This allows the same paper to support contextual reading, cross-paper discovery, expert matching, and longitudinal views of scholarly work. Westlake Scholar provides an open and governable implementation of an institution-controlled AI layer that connects repository content, scholarly discovery, and researcher relationships while preserving provenance, human review, and institutional governance. A deployment at Westlake University, in operation since April 2026, demonstrates that the integrated system can operate in a live institutional setting.
Innovation
Methods, ideas, or system contributions that make the work stand out.
AI services
institutional knowledge layer
contextual paper reading
research-direction-guided discovery
expert discovery
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