Abstract: This paper aims to address the high manual costs and insufficient accuracy associated with the current construction of intangible cultural heritage(ICH) knowledge graphs by proposing a new approach using the large-scale pre-trained model ChatGPT. Specifically, we propose a domain knowledge graph construction framework, ChatKG(Chat Knowledge Graph), based on large language model and prompt engineering. By reusing the CIDOC CRM ontology model and combining manual curation with ChatGPT-assisted knowledge extraction, the framework enables the construction of ontologies for intangible cultural heritage. Additionally, we propose a Chain of Thought(CoT) prompt optimization method for accurate knowledge extraction. This provides a rapid, efficient, and low-cost solution for building domain ontology models and extracting knowledge in the ICH field, which originally lacks reusable ontology concept models and high-quality annotated data. Taking the Chinese intangible cultural heritage ceramic craft as an example, the model successfully identified 419 craft entities and 763 relationships between entities, ultimately constructing a knowledge graph and exploring application scenarios, thereby validating the effectiveness of our method.

Key words: Large language model, Knowledge graph, Prompt engineering, , Ontology construction, Knowledge extraction ,