[Purpose/Significance] Based on large language models, exploring the construction and application methods of an evidence-based knowledge base in the field of history can provide more intelligent tools for history research and promote the innovation of research methods in the digital humanities era. [Method/Process] First, an evidence-based history ontology was designed, establishing a framework for semantic organization of evidence. A multi-stage knowledge extraction strategy was employed to extract structured viewpoints and evidence from historical documents. Then, by combining vector retrieval with language models, semantic associations between viewpoint nodes were further constructed, forming an evidence-based knowledge base for history. Finally, a multi-level evidence retrieval strategy was adopted to progressively expand the scope of evidence retrieval, uncovering potential historical evidence associations to support the intelligent argumentation of historical viewpoints. [Result/Conclusion] The prototype system built on the evidence-based historical knowledge base has proven effective in uncovering deep historical evidence and enabling multidimensional argumentation. Expert evaluations suggest that the system demonstrates high relevance, reliability, and practicality, highlighting its potential to serve as an intelligent tool for historical research.
Fan Qi
,
Qi Hanyue
,
Lin Zefei
. Research on the Construction and Application Methods of Evidence-based Knowledge Base for History Research Based on Large Language Models[J]. Library and Information Service, 2025
, 69(18)
: 121
-130
.
DOI: 10.13266/j.issn.0252-3116.2025.18.010
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