Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO) have been the two most prominent methodologies of data and knowledge modelling in the Artificial Intelligence community and the Information Science community, respectively. KR boasts of a robust and scalable ecosystem of technologies to support knowledge modelling while, often, underemphasizing the quality of its models (and model-based data). KO, on the other hand, is less technology-driven but has developed a robust framework of guiding principles (canons) for ensuring modelling (and model-based data) quality. This paper elucidates both the KR and facet-analytical KO methodologies in detail and provides a functional mapping between them. Out of the mapping, the paper proposes an integrated KO-enriched KR methodology with all the standard components of a KR methodology plus the guiding canons of modelling quality provided by KO. The practical benefits of the methodological integration has been exemplified through a prominent case study of KR-based image annotation exercise.

翻译：知识表示（KR）与分面分析型知识组织（KO）分别是人工智能社群与信息科学社群中数据与知识建模的两大主流方法论。KR拥有稳健且可扩展的技术生态系统支撑知识建模，但常对其模型（及基于模型的数据）质量重视不足；而KO虽较少受技术驱动，却构建了确保建模（及基于模型的数据）质量的坚实指导原则框架（规范体系）。本文详细阐释了KR与分面分析型KO这两种方法论，并建立了二者间的功能映射关系。基于该映射，本文提出一种融合KO的增强型KR方法论，其包含KR方法论的全部标准组件，同时吸纳了KO提供的建模质量指导规范。通过基于KR的图像标注实践这一典型案例，本文论证了该方法论整合的实用价值。