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

2026-07-20

AI Summary

Traditional ontology extension methods are resource-intensive and error-prone, while existing large language model–based approaches lack explicit alignment with user requirements and reusable core ontologies, and suffer from insufficient systematic evaluation. This work proposes the first ontology extension framework that integrates competency question–driven design with retrieval-augmented generation (RAG), enabling context-aware, requirement-guided generation of ontology fragments by explicitly linking user needs—formulated as competency questions—with existing ontological knowledge. Evaluated on two real-world use cases, the generated fragments exhibit sound structural integrity and pass all functional tests; expert engineers assessed them as requiring only minor to moderate revisions for integration. These results demonstrate the feasibility, scalability, and evalability of the proposed approach.

Abstract

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.

Problem

• ontology extension
• requirement-driven
• Large Language Models
• competency questions
• systematic evaluation

Innovation

• ontology extension
• large language models
• retrieval-augmented generation
• competency questions
• requirements-driven