# Ontology Development is Consensus Creation, Not (Merely) Representation

arXiv: https://arxiv.org/abs/2210.12026 · HTML: https://mlsanity.com/paper/2210.12026/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 61 references that are in the map.
- First posted on arXiv 21 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [Automated and Explainable Ontology Extension Based on Deep Learning: A Case Study in the Chemical Domain](https://mlsanity.com/paper/2109.09202/) (2021)

## Built on by (most cited first, 5 of 5)

- [Knowledge Graphs for the Life Sciences: Recent Developments, Challenges and Opportunities](https://mlsanity.com/paper/2309.17255/) (2023)
- [LLM Agents Are the Antidote to Walled Gardens](https://mlsanity.com/paper/2506.23978/) (2025)
- [Ontology Pre-training for Poison Prediction](https://mlsanity.com/paper/2301.08577/) (2023)
- [Ontology Enabled Hybrid Modeling and Simulation](https://mlsanity.com/paper/2506.12290/) (2025)
- [SciSchema.org: A Multidisciplinary Collection of Schemas for Structured Scientific Process Descriptions](https://mlsanity.com/paper/2607.27955/) (2026)

## Abstract (arXiv)

Ontology development methodologies emphasise knowledge gathering from domain experts and documentary resources, and knowledge representation using an ontology language such as OWL or FOL. However, working ontologists are often surprised by how challenging and slow it can be to develop ontologies. Here, with a particular emphasis on the sorts of ontologies that are content-heavy and intended to be shared across a community of users (reference ontologies), we propose that a significant and heretofore under-emphasised contributor of challenges during ontology development is the need to create, or bring about, consensus in the face of disagreement. For this reason reference ontology development cannot be automated, at least within the limitations of existing AI approaches. Further, for the same reason ontologists are required to have specific social-negotiating skills which are currently lacking in most technical curricula.