2024/02/22 by Mahsa Shamsabadi, Shamsabadi, Mahsa, Jennifer D’Souza +1 · 1 citation
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Digital Libraries (cs.DL) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Library Science and Information Systems #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2402.14622
openalex publication_date 2024/02/22 · openalex created_date 2024/02/24 · openalex updated_date 2026/07/28
This 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 information access system as an IR method accessible at https://orkg.org/usecases/r0-estimates.