# OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

arXiv: https://arxiv.org/abs/2607.17963 · HTML: https://mlsanity.com/paper/2607.17963/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 20 Jul 2026.

## Builds on (most cited first, 10 of 10)

- [Ontology Generation using Large Language Models](https://mlsanity.com/paper/2503.05388/) (2025)
- [Ontology engineering with Large Language Models](https://mlsanity.com/paper/2307.16699/) (2023)
- [Ontology Enrichment from Texts: A Biomedical Dataset for Concept Discovery and Placement](https://mlsanity.com/paper/2306.14704/) (2023)
- [Assessing the Capability of Large Language Models for Domain-Specific Ontology Generation](https://mlsanity.com/paper/2504.17402/) (2025)
- [The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies](https://mlsanity.com/paper/2404.03044/) (2024)
- [Measuring agreement among several raters classifying subjects into one or more (hierarchical) categories: A generalization of Fleiss' kappa](https://mlsanity.com/paper/2303.12502/) (2023)
- [Large Language Models Assisting Ontology Evaluation](https://mlsanity.com/paper/2507.14552/) (2025)
- [Enhancing Manufacturing Knowledge Access with LLMs and Context-aware Prompting](https://mlsanity.com/paper/2507.22619/) (2025)
- [Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study](https://mlsanity.com/paper/2508.20693/) (2025)
- [Enriching Taxonomies Using Large Language Models](https://mlsanity.com/paper/2602.22213/) (2025)

## Built on by (most cited first, 2 of 2)

- [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026)
- [When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning](https://mlsanity.com/paper/2608.31118/) (2026)

## Abstract (arXiv)

Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete. This task is a resource-intensive and error-prone process. Large Language Models (LLMs) have shown promising performance on generating ontologies from scratch, but current approaches rarely tie ontology extension explicitly to requirements or reusable core models, and offer limited, systematic evaluation of LLM outputs. This paper introduces OntoExtend, a requirements-driven framework for ontology extension with LLMs. It uses retrieval-augmented generation (RAG) over relevant input ontologies and requirements in the form of competency questions to propose grounded extensions. We evaluate OntoExtend on 39 CQs from two use cases: a public EU-project ontology, Onto-DESIDE, and an industrial ontology from Bosch. The generated fragments show few structural issues, satisfy all functional evaluation tests, and are rated by ontology engineers as requiring minor to moderate revision before integration. These results suggest that OntoExtend is useful as a drafting assistant for requirement-driven ontology extension in real world scenarios, while remaining sensitive to CQ specificity and modelling profile.