CIDOC CRM-Based Knowledge Graph Construction for Cultural Heritage Using Large Language Models

Abstract

The cultural heritage of the Liao dynasty in Chifeng encompasses significant historical and cultural information that requires systematic digital preservation and management. However, heterogeneous data sources across museums, archives, and research institutions lack semantic interoperability, creating barriers for cross-system integration and knowledge discovery. This study proposes a standardized knowledge graph construction method by integrating the CIDOC Conceptual Reference Model version 7.2 with large language models. A unified ontology framework enables semantic consistency across diverse heritage data, while Generative Pre-trained Transformer-based models automatically extract structured triples from unstructured texts through prompt engineering and entity disambiguation, with the resulting knowledge graph implemented in Neo4j graph database. The constructed knowledge graph integrates 106 immovable cultural heritage records from Chifeng City with approximately 20 types of semantic relationships, forming a comprehensive semantic network covering people, places, events, time, and materials. K-means clustering reveals five cultural value themes, including “Nomadic Imperial Power System” and “Multi-Capital Governance Network”, while geospatial mapping identifies a “dual-core and ring-belt” distribution pattern for heritage protection zoning. This research demonstrates how international semantic standards can be integrated with artificial intelligence technologies to enable interoperable cultural heritage knowledge systems, providing practical implications for cross-institutional heritage management and archaeological survey planning.