From Knowledge Representation to Knowledge Organization and Back
This work addresses the problem of improving knowledge modelling quality in AI and information science by bridging two established methodologies, though it appears incremental as it combines existing frameworks.
The paper tackles the integration of Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO) methodologies, proposing an enriched KR approach that incorporates KO's quality canons, demonstrated through a case study on image annotation.
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.