Faceted Classification Theory as a Conceptual Foundation for the Evolution of Knowledge Organization Systems in AI-Mediated Environments
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Abstract
Artificial Intelligence (AI) has expanded the role of Knowledge Organization Systems (KOS), transforming them from traditional documentary tools into semantic infrastructures that support knowledge representation, interoperability, explainability, governance, and contextualization in AI-mediated environments. Although recent studies have explored the application of AI to KOS, limited attention has been given to how these transformations affect the conceptual foundations of KOS themselves. This study investigates how recent literature characterizes the challenges faced by KOS in AI-mediated environments and interprets them through the principles of Faceted Classification Theory (FCT). An integrative literature review, combined with a conceptual synthesis, identified recurring challenges, including semantic interoperability, integration of heterogeneous knowledge representations, explainability, knowledge governance, scalability, and continuous evolution. Based on this interpretation, a conceptual framework was developed to relate these challenges to core FCT principles, including conceptual decomposition, faceted synthesis, hospitality, terminological control, multidimensionality, explicit semantic relationships, and structural flexibility. Rather than proposing new theoretical principles, the study demonstrates that many challenges currently attributed to AI can be understood as conceptual organization problems for which FCT already provides relevant structural foundations, reaffirming its contemporary relevance for the evolution of KOS in AI-mediated environments.
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