From Knowledge Representation to Knowledge Organization and Back
Fausto Giunchiglia, Mayukh Bagchi
TL;DR
The paper addresses persistent quality gaps in Knowledge Representation (KR) models and the underutilized guidance of Knowledge Organization (KO) canons. It proposes a KO-enriched KR methodology that integrates facet-analytic KO canons into KR workflows, introducing an Ontology Engineer to design high-quality Entity Type Graphs (ETGs) and an ETG repository to support robust knowledge graphs. A formal mapping between KR and KO components demonstrates how each methodology's roles and artifacts align, enabling a loop from KR to KO and back with enhanced data quality. The ImageNet case study (ECAI23) demonstrates practical gains in alignment between visual and linguistic classifications, improved annotation quality, and reduced annotation costs, highlighting the approach's potential to improve data-driven model performance across domains.
Abstract
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.