Large Language Models for Ontology Engineering: A Systematic Literature Review

Ontology engineering (OE) is a complex task in knowledge representation that relies heavily on domain experts to accurately define concepts and precise relationships in a domain of interest, as well as to maintain logical consistency throughout the resultant ontology. Recent advances in large language models (LLMs) have created new opportunities to automate and enhance various stages of ontology development. This article presents a systematic literature review on the use of LLMs in OE, focusing on their roles in core development activities, input–output characteristics, evaluation methods, and application domains. We analyze 36 papers covering 49 task-level studies to identify common tasks where LLMs have been applied, spanning ontology requirements specification, implementation, publication, and maintenance. Our findings indicate that LLMs primarily act as ontology engineers, domain experts, and evaluators, using models such as Generative Pretrained Transformer, Large Language Model Meta AI, and Text-to-Text Transfer Transformer. Different approaches rely on zero-shot and few-shot prompting to process heterogeneous inputs (e.g., Web Ontology Language ontologies, natural language text, and competency questions) and generate task-specific outputs (e.g., axioms, mappings, and documentation). Our review also reveals a lack of homogenization in task definitions, dataset selection, evaluation metrics, and experimental workflows. In addition, several studies do not release their complete evaluation protocols or code, making their results difficult to reproduce and their methods insufficiently transparent. Addressing these gaps through standardized benchmarks and hybrid workflows that integrate LLM automation with human expertise represents an important challenge for future research.