# Measuring Expert Performance at Manually Classifying Domain Entities under Upper Ontology Classes

arXiv: https://arxiv.org/abs/1810.05093 · HTML: https://mlsanity.com/paper/1810.05093/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Conversational Ontology Alignment with ChatGPT](https://mlsanity.com/paper/2308.09217/) (2023)

## Abstract (arXiv)

Classifying entities in domain ontologies under upper ontology classes is a recommended task in ontology engineering to facilitate semantic interoperability and modelling consistency. Integrating upper ontologies this way is difficult and, despite emerging automated methods, remains a largely manual task. Little is known about how well experts perform at upper ontology integration. To develop methodological and tool support, we first need to understand how well experts do this task. We designed a study to measure the performance of human experts at manually classifying classes in a general knowledge domain ontology with entities in the Basic Formal Ontology (BFO), an upper ontology used widely in the biomedical domain. We conclude that manually classifying domain entities under upper ontology classes is indeed very difficult to do correctly. Given the importance of the task and the high degree of inconsistent classifications we encountered, we further conclude that it is necessary to improve the methodological framework surrounding the manual integration of domain and upper ontologies.