  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.
