Knowledge Organization in AI-Era Biological Sciences: A Thematic Review

Knowledge Organization (KO) has historically been used to structure biological knowledge, from taxonomy to ontologies. This becomes increasingly challenging as life sciences evolve into a data-intensive domain. The advent of artificial intelligence (AI) has enabled knowledge organization systems (KOSs) to assume active roles in computational workflows rather than serve as passive repositories. This thematic review examines the evolution of KOSs in AI-augmented biological research by situating them within scientific paradigmatic and epistemological shifts. By synthesizing foundational theories from library and information science, philosophy of science, and biological systematics, we propose the Knowledge Organization Analysis Framework (KOAF) to capture bio-KOSs’ developments across functional sophistication, automation degree in system construction, and reasoning and inference capability. Representative empirical studies show that bio-KOSs enable semantic interoperability and data integration, while also contributing to hypothesis generation and reasoning. We argue that advanced bio-KOSs increasingly function as epistemic agents in scientific discovery. This transformation marks KOSs as theoretical frameworks shaping scientific inquiry through AI-KO convergence and highlights the need for future research on accountability, epistemic integrity, and scientific trustworthiness in AI-driven knowledge discovery.