Abstract Purpose This paper compares the paradigmatic differences between knowledge organization (KO) in library and information science and knowledge representation (KR) in AI to show the convergence in KO and KR methods and applications. Methodology The literature review and comparative analysis of KO and KR paradigms is the primary method used in this paper. Findings A key difference between KO and KR lays in the purpose of KO is to organize knowledge into certain structure for standardizing and/or normalizing the vocabulary of concepts and relations, while KR is problem-solving oriented. Differences between KO and KR are discussed based on the goal, methods, and functions. Research limitations This is only a preliminary research with a case study as proof of concept. Practical implications The paper articulates on the opportunities in applying KR and other AI methods and techniques to enhance the functions of KO. Originality/value: Ontologies and linked data as the evidence of the convergence of KO and KR paradigms provide theoretical and methodological support to innovate KO in the AI era.

中文翻译：

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人工智能视角下的知识组织与表征

摘要目的比较图书馆知识组织（KO）与情报科学和人工智能领域知识表示（KR）之间的范式差异，以证明KO和KR方法与应用的融合。方法论文献综述和对KO和KR范式的比较分析是本文使用的主要方法。研究结果KO和KR之间的主要区别在于KO的目的是将知识组织为某种结构，以标准化和/或规范化概念和关系的词汇，而KR则以解决问题为导向。根据目标，方法和功能，讨论了KO和KR之间的差异。研究局限性这只是一个初步研究，以案例研究为概念验证。实际意义本文阐述了应用KR和其他AI方法和技术来增强KO功能的机会。独创性/价值：本体论和关联数据是KO和KR范式融合的证据，为AI时代的KO创新提供了理论和方法论上的支持。