Applying Integrative Levels Classification to Korean KOS Registry
Bridging Phenomena and Knowledge Systems
Parole chiave:
Korean Knowledge Organization System (K-KOS), Knowledge Organization System (KOS) Registry, Integrative Levels Classification (ILC), Phenomenon-based Classification, Generative AI-assisted ClassificationAbstract
This study investigates the applicability of the phenomena-based classification system ILC2 to the classification of the Korean KOS Registry. It also examines the potential of generative AI to supplement human expert validation in the classification process. To this end, approximately 450 KOS entries were classified through a three-stage cross-validation by human experts, along with classification code suggestions generated by ChatGPT. The results indicate that the phenomena-centered ILC effectively reflects KOS topics organized around applied domains, and it can further support the identification of needs for new categories in emerging applied fields, as well as Korea- and Asia-specific subjects. Additionally, generative AI contributed to both suggesting classification notations and facilitating expert cross-checking; however, limitations were observed, such as hallucinations where the AI suggested notations not present in existing classification systems. This study provides both practical and theoretical implications for the management of KOSs and the development of classification systems.
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Copyright (c) 2025 SoYoung Yoon, Claudio Gnoli, Ziyoung Park
Questo lavoro è fornito con la licenza Creative Commons Attribuzione - Non commerciale - Condividi allo stesso modo 4.0 Internazionale.
