Construction of Historical Knowledge Graphs Based on BERT and Graph Neural Networks

2026-06-01

AI Summary

This study addresses the challenges of entity and relation extraction from historical texts, where linguistic ambiguity, vague anaphora, and non-standard syntax impede accurate information retrieval. To tackle these issues, the authors propose a joint model that integrates BERT with graph neural networks (GNNs), leveraging context-sensitive semantic representations alongside relational graph learning. This approach effectively handles nested structures and implicit coreference, enabling the automatic construction of knowledge graphs from diverse unstructured historical sources such as municipal archives, parliamentary records, and historical correspondence. Experimental results demonstrate that the proposed system significantly outperforms both traditional rule-based methods and existing deep learning baselines in terms of Precision, Recall, and F1-score, thereby substantially improving the accuracy and completeness of historical knowledge graphs.

Abstract

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. The texts of traditional history resolve linguistic ambiguities, references limited by context, and a lack of established grammatical norms in a systematic way. This study develops a new image retrieval system based on FastRQNet and pre-trained vision-language model Vilt-qaformer+RoBInet in accordance with the aforementioned recommendations. The experiments make full use of a comprehensive collection of municipal records, parliamentary documents, and historical correspondence. When compared to conventional rule-based techniques and other popular deep-learning baselines, the joint BERT-GNN system obtains greater Precision, Recall, and F1-score (Table 2). Complex nested structures and implicit reference issues can be handled by this structure with sufficient accuracy and thoroughness when creating knowledge graphs. The aforementioned experiments show that combining relational graph learning algorithms with context-sensitive semantic representation techniques can automatically extract historical data to add accumulated wisdom to the knowledge repository.

Problem

• Historical Knowledge Graph
• Entity and Relation Extraction
• Linguistic Ambiguity
• Contextual Reference
• Structured Historical Data

Innovation

• BERT
• Graph Neural Networks
• Historical Knowledge Graph
• Entity and Relation Extraction
• Context-aware Semantic Representation