Knowledge Organization (KO) and Knowledge Representation (KR) have been the two mainstream methodologies of knowledge modelling in the Information Science community and the Artificial Intelligence community, respectively. The facet-analytical tradition of KO has developed an exhaustive set of guiding canons for ensuring quality in organising and managing knowledge but has remained limited in terms of technology-driven activities to expand its scope and services beyond the bibliographic universe of knowledge. KR, on the other hand, boasts of a robust ecosystem of technologies and technology-driven service design which can be tailored to model any entity or scale to any service in the entire universe of knowledge. This paper elucidates both the facet-analytical KO and KR methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KR-enriched KO methodology with all the standard components of a KO methodology plus the advanced technologies provided by the KR approach. The practical benefits of the methodological integration has been exemplified through the flagship application of the Digital University at the University of Trento, Italy.

翻译：知识组织（KO）与知识表征（KR）分别是信息科学领域和人工智能领域实现知识建模的两大主流方法论。KO的面分析法传统已发展出一系列详尽的质量保障准则，用于规范知识组织与管理，但其技术驱动型活动始终局限于知识书目学范畴，未能拓展服务范围与边界。而KR则构建了强大的技术生态系统与技术驱动的服务设计体系，可针对知识全宇宙中的任意实体进行建模，或扩展至任意规模的服务。本文详细阐释了面分析型KO与KR两种方法论，并在两者之间建立了功能性映射关系。基于该映射，本文提出了一种融合KR的增强型KO方法论，该方法论既包含KO方法论的所有标准组件，又融入了KR方法所提供的先进技术。通过意大利特伦托大学旗舰项目"数字大学"的应用实例，论证了方法论整合的实际效益。