The Application of Constructing Knowledge Graph of Oral Historical Archives Resources Based on LLM-RAG
Yi Sun, Wanru Yang, Yin Liu · 2024
Oral historical archive resources are an emerging archive resource with the rapid development of modern technology. Its "bottom-up" approach to historical research has received widespread attention in the fields of history, archives, and libraries. Under the common knowledge discovery mode, oral historical archives resources are showing a dispersed state. Information technology represented by knowledge graphs can break through the data solidification of oral historical archives, reshape the information stack of oral historical archives, and achieve knowledge association and aggregation of oral historical archive resources. The article attempts to construct a knowledge graph of the oral historical archives resources on the theme of "science and art" in the collection of T.D. Lee Library of Shanghai Jiao Tong University. It uses Large Language Model - Retrieval Augmented Generation (LLM-RAG) for knowledge extraction, and then uses a semantic model for knowledge organization and management. The article attempts to empower humanities with technology, exploring the possibility of combining "digital technology" and "humanities research", extending traditional humanities research methods, breaking down barriers between technology and humanities resources, and providing a new path reference for revealing resource content characteristics, semantic deep correlation, and multi-dimensional knowledge discovery.