# An Experiment in Retrofitting Competency Questions for Existing Ontologies

arXiv: https://arxiv.org/abs/2311.05662 · HTML: https://mlsanity.com/paper/2311.05662/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 29 references that are in the map.
- First posted on arXiv 9 Nov 2023.

## Builds on (most cited first, 4 of 4)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Automatically Drafting Ontologies from Competency Questions with FrODO](https://mlsanity.com/paper/2206.02485/) (2022)

## Built on by (most cited first, 12 of 12)

- [OntoChat: a Framework for Conversational Ontology Engineering using Language Models](https://mlsanity.com/paper/2403.05921/) (2024)
- [Generative AI for Requirements Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2409.06741/) (2024)
- [A RAG Approach for Generating Competency Questions in Ontology Engineering](https://mlsanity.com/paper/2409.08820/) (2024)
- [Discerning and Characterising Types of Competency Questions for Ontologies](https://mlsanity.com/paper/2412.13688/) (2024)
- [Retrieval-Augmented Generation of Ontologies from Relational Databases](https://mlsanity.com/paper/2506.01232/) (2025)
- [Bench4KE: Benchmarking Automated Competency Question Generation](https://mlsanity.com/paper/2505.24554/) (2025)
- [A Comparative Study of Competency Question Elicitation Methods from Ontology Requirements](https://mlsanity.com/paper/2507.02989/) (2025)
- [Large Language Models Assisting Ontology Evaluation](https://mlsanity.com/paper/2507.14552/) (2025)
- [Enhancing Rhetorical Figure Annotation: An Ontology-Based Web Application with RAG Integration](https://mlsanity.com/paper/2412.13799/) (2024)
- [IDEA2: Expert-in-the-loop competency question elicitation for collaborative ontology engineering](https://mlsanity.com/paper/2604.01344/) (2026)
- [Characterising LLM-Generated Competency Questions: a Cross-Domain Empirical Study using Open and Closed Models](https://mlsanity.com/paper/2604.16258/) (2026)
- [OntoKG-EQ: A provenance-grounded, competency-question-governed knowledge graph for auditable analyst querying](https://mlsanity.com/paper/2609.08869/) (2026)

## Abstract (arXiv)

Competency Questions (CQs) are a form of ontology functional requirements expressed as natural language questions. Inspecting CQs together with the axioms in an ontology provides critical insights into the intended scope and applicability of the ontology. CQs also underpin a number of tasks in the development of ontologies e.g. ontology reuse, ontology testing, requirement specification, and the definition of patterns that implement such requirements. Although CQs are integral to the majority of ontology engineering methodologies, the practice of publishing CQs alongside the ontological artefacts is not widely observed by the community. In this context, we present an experiment in retrofitting CQs from existing ontologies. We propose RETROFIT-CQs, a method to extract candidate CQs directly from ontologies using Generative AI. In the paper we present the pipeline that facilitates the extraction of CQs by leveraging Large Language Models (LLMs) and we discuss its application to a number of existing ontologies.