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）与分面分析知识组织（KO）分别是人工智能领域和信息科学领域中两种最突出的数据与知识建模方法论。知识表示拥有强大且可扩展的技术生态体系以支持知识建模，但其模型（及基于模型的数据）质量往往未得到足够重视。相比之下，知识组织虽较少受技术驱动，却已建立起一套确保建模（及基于模型的数据）质量的稳健指导原则（规范体系）。本文详细阐释了知识表示与分面分析知识组织两种方法论，并在两者间建立起功能映射关系。基于该映射关系，本文提出了一种融合知识组织特性的知识表示方法论，该方法论既包含知识表示方法论的全部标准组件，又融入了知识组织关于建模质量的指导规范。通过一项基于知识表示的图像标注典型案例研究，本文验证了该方法论整合的实践效益。