Ontologies are essential for structuring domain knowledge, improving accessibility, sharing, and reuse. However, traditional ontology construction relies on manual annotation and conventional natural language processing (NLP) techniques, making the process labour-intensive and costly, especially in specialised fields like casting manufacturing. The rise of Large Language Models (LLMs) offers new possibilities for automating knowledge extraction. This study investigates three LLM-based approaches, including pre-trained LLM-driven method, in-context learning (ICL) method and fine-tuning method to extract terms and relations from domain-specific texts using limited data. We compare their performances and use the best-performing method to build a casting ontology that validated by domian expert.

翻译：本体对于结构化领域知识、提升可访问性、共享与重用至关重要。然而，传统的本体构建依赖于人工标注和传统的自然语言处理技术，使得该过程劳动密集且成本高昂，尤其是在铸造制造等专业领域。大语言模型的兴起为自动化知识抽取提供了新的可能性。本研究探讨了三种基于大语言模型的方法，包括预训练大语言模型驱动方法、上下文学习方法以及微调方法，以利用有限数据从领域特定文本中抽取术语和关系。我们比较了它们的性能，并使用最佳表现的方法构建了一个经领域专家验证的铸造本体。