Bridging Classification Systems: The Potentialities of Artificial Intelligence in Developing Concordance Tables for Science, Technology, and Policy

This paper explores the challenges and methodologies involved in aligning diverse subject classification systems through the development of concordance tables. It investigates prior efforts, identifies successful implementations, and evaluates employed methods. Using a multi-meth o d approach, the research combines a literature review with Artificial Intelligence (AI)-enhanced content analysis in Scopus to identify trends and gaps in existing studies. The findings highlight the potential of AI-driven methodologies to improve automation and reliability in creating concordance tables while identifying areas for future research. The study emphasizes the importance and the limits of using AI for integrating classification systems, supporting knowledge organization, and facilitatin g science and innovation policy decision-making.