# From Knowledge Representation to Knowledge Organization and Back

arXiv: https://arxiv.org/abs/2312.07302 · HTML: https://mlsanity.com/paper/2312.07302/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 58 references that are in the map.
- First posted on arXiv 12 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [Data and its (dis)contents: A survey of dataset development and use in machine learning research](https://mlsanity.com/paper/2012.05345/) (2020)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research](https://mlsanity.com/paper/2112.01716/) (2021)
- [From ImageNet to Image Classification: Contextualizing Progress on Benchmarks](https://mlsanity.com/paper/2005.11295/) (2020)
- [Continual egocentric object recognition](https://mlsanity.com/paper/1912.05029/) (2019)
- [Stratified Data Integration](https://mlsanity.com/paper/2105.09432/) (2021)
- [Aligning Visual and Lexical Semantics](https://mlsanity.com/paper/2212.06629/) (2022)
- [Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal](https://mlsanity.com/paper/2110.13504/) (2021)
- [A semantics-driven methodology for high-quality image annotation](https://mlsanity.com/paper/2307.14119/) (2023)
- [Object Recognition as Classification via Visual Properties](https://mlsanity.com/paper/2112.10531/) (2021)
- [Popularity Driven Data Integration](https://mlsanity.com/paper/2209.14049/) (2022)
- [Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and Embedding](https://mlsanity.com/paper/2311.12465/) (2023)

## Built on by (most cited first, 3 of 3)

- [From Knowledge Organization to Knowledge Representation and Back](https://mlsanity.com/paper/2401.11753/) (2024)
- [What can Computer Vision learn from Ranganathan?](https://mlsanity.com/paper/2601.22634/) (2026)
- [Plurification in/of language technology -- The integration of culture in next-generation AI](https://mlsanity.com/paper/2606.22097/) (2026)

## Abstract (arXiv)

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.