This short paper highlights the growing importance of information retrieval (IR) engines in the scientific community, addressing the inefficiency of traditional keyword-based search engines due to the rising volume of publications. The proposed solution involves structured records, underpinning advanced information technology (IT) tools, including visualization dashboards, to revolutionize how researchers access and filter articles, replacing the traditional text-heavy approach. This vision is exemplified through a proof of concept centered on the ``reproductive number estimate of infectious diseases'' research theme, using a fine-tuned large language model (LLM) to automate the creation of structured records to populate a backend database that now goes beyond keywords. The result is a next-generation IR method accessible at https://orkg.org/usecases/r0-estimates.

翻译：这篇短文强调了信息检索引擎在科学界日益增长的重要性，并指出由于出版物数量激增，传统基于关键词的搜索引擎效率低下的问题。所提出的解决方案涉及结构化记录，这些记录支撑着包括可视化仪表盘在内的先进信息技术工具，旨在彻底改变研究人员获取和筛选文章的方式，取代传统的以文本为主的方法。这一设想通过一个以“传染病再生数估计”研究主题为中心的概念验证得以体现，该验证利用微调后的大语言模型自动创建结构化记录，以填充超越关键词范畴的后端数据库。其成果是一种可访问于 https://orkg.org/usecases/r0-estimates 的下一代信息检索方法。