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.
Automatic categorization of fine art paintings across multiple semantic facets, such as artist, style, and genre, is fundamental for large-scale digital archiving, semantic indexing, and knowledge organization of cultural heritage collections. In this paper, we propose convolutional neural network (CNN)-Transformer Hybrid Attention model for art paintings categorization (CTHArt), a CNN-Transformer Hybrid Attention network for multitask art painting categorization. The model employs a dual-branch hybrid backbone that combines a CNN stream for fine-grained local texture modeling and a Transformer stream for global compositional and stylistic context learning. To further exploit inter-facet semantic dependencies, we introduce a Cross-Task Attention Head, which enables task-specific classifiers to exchange information through learnable cross-attention interactions. This design supports coordinated facet prediction consistent with knowledge organization principles. We evaluate the proposed framework on three benchmark datasets. Experimental results demonstrate that CTHArt consistently achieves state-of-the-art performance. The proposed approach provides an effective and scalable solution for artificial intelligence (AI)-assisted knowledge organization of art collections.
This paper investigates the application of machine learning (ML) to the automatic classification of records and archives, framing it as a critical challenge in Knowledge Organization (KO). As digitization creates massive volumes of uncategorized data, the following research question arises: how can fundamental archival principles-such as provenance, original order, and hierarchical description-be translated into this new computational paradigm? This study first synthesizes, based on a multidisciplinary review of archival science, classification theory, KO, computer science, and information science, a proposal of six fundamental guidelines for the responsible application of artificial intelligence (AI) in records and archives. These guidelines connect traditional archival theory with the modern imperatives of trustworthy and explainable AI. Second, we conduct a comparative analysis of 24 published ML experiments, assessing their adherence to these guidelines. Our analysis reveals a significant and troubling disconnect. While most experiments acknowledge the principle of provenance (75.0%), they demonstrate profound neglect of guidelines related to diverse perspectives (25.0%), explainability (16.7%), and, most critically, algorithmic accountability (0.0%). The results indicate that current practices often succeed in basic content categorization but fail in the more sophisticated archival task of preserving archives' evidentiary and relational integrity by treating records as decontextualized data. The study calls for urgently developing an Archival AI Lifecycle-a framework that weaves archival principles, classification theory, and knowledge organization into AI development, safeguarding archival practice's intellectual and ethical integrity in the digital age.
Knowledge Organization Ecosystems (KOE) constitute an analytical metaphor for understanding the growing complexity of contemporary information environments, characterized by interdependence, automation, continuous updating, and algorithmic mediation. Traditionally, Knowledge Organization has relied on relatively stable structures, such as documentary languages and ontologies. However, the incorporation of artificial intelligence and digital infrastructures has expanded this scope, requiring conceptual frameworks capable of integrating epistemological, sociotechnical, and ethical dimensions. This article critically examines KOE as ecosystems composed of people, technologies, and organizations, proposing an interpretative perspective that articulates epistemic plurality, distributed governance, and sociotechnical responsibility. Methodologically, the study is based on an analytical and conceptual review of 26 texts selected for thematic relevance, of which 10 form the theoretical core used to characterize KOE and compare them with Knowledge Organization Systems (KOS). The results indicate that KOS provide semantic stability and terminological control, whereas KOE incorporate collaborative dynamics, algorithmic mediation, and ethical governance, enabling a more comprehensive understanding of knowledge organization in artificial intelligence contexts. The article concludes that the ecosystem paradigm enhances the field's explanatory capacity by recognizing the distributed, ethical, and adaptive nature of contemporary knowledge mediation systems, thereby establishing KOE as a necessary methodological evolution within Information Science.
The rapid growth of digital social information service platforms has fundamentally transformed information diffusion in financial markets, creating an urgent need for knowledge organization systems that can integrate structured financial knowledge with dynamic social interactions. Traditional approaches, which rely on either ontological precision or social network analysis alone, face significant limitations in capturing the complex, multimodal associations that drive modern market behavior. This study proposes a novel Social-Knowledge Big Graph (SKBG) framework to address this gap. The SKBG is designed from a knowledge association perspective and systematically models five core patterns through a three-layer fusion model. The framework semantically grounds social entities in ontologies, enables consistent reasoning and knowledge discovery, and captures temporal dynamics and influence propagation. A case study of China's A-share market and associated investor communities demonstrates the SKBG's practical value. It identifies latent "cross-modal influencer" archetypes, traces semantically enriched misinformation propagation paths that reveal coordination mechanisms, and uncovers socially mediated stock price comovement that traditional models miss. These findings validate the SKBG as a knowledge organization system that bridges the divide between discrete social interactions and complex financial dynamics. This study contributes a robust methodological framework for constructing large-scale socio-financial knowledge organization systems, with important implications for intelligent financial services, risk surveillance, and market regulation in the digital age.
