AI RESEARCH
Construction of Historical Knowledge Graphs Based on BERT and Graph Neural Networks
arXiv CS.AI
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ArXi:2606.01747v1 Announce Type: cross Through digital humanities research and scale-up historical data analysis, a significant amount of traditional historical text is converted into structured knowledge graphs. This paper provides a high-level architecture that combines bidirectional encoder representations of transformers (BERT) and graph neural networks (GNN) to extract the entities and relationships from various types of historical texts.