LLM-Assisted Ontology Restriction Verification With Clustering-Based Description Generation

An ontology is a scheme for structuring relationships between concepts in a domain, promoting data interoperability and system integration. However, poorly designed ontologies can lead to errors and performance issues. While systems engineering has standardized evaluation guidelines (e.g., ISO/IEC), ontology engineering lacks such standards, leading to various independent evaluation methods. One frequent issue among novice developers is the misuse of ontology restrictions, particularly ‘allValuesFrom’ and ‘someValuesFrom’, which can significantly impact the correctness and reliability of ontologies. However, existing studies have not adequately addressed effective methods for detecting such errors. To address this gap, we propose a context-aware verification framework utilizing large language models to detect and correct misuse in ontology restrictions. Unlike conventional methods, our framework integrates contextual descriptions derived from ontological axioms, enabling more accurate verification. Additionally, we introduce a clustering-based description generation method that systematically organizes contextual information, further enhancing verification accuracy. Experimental evaluation conducted on diverse ontology datasets suggests that contextual integration improves verification performance. Moreover, the clustering-based description generation improves restriction misuse detection and correction compared to traditional approaches. By automating ontology restriction verification, this study contributes significantly to enhancing the reliability of ontology evaluation and provides a foundation for developing more scalable and standardized verification techniques.