Multi-dimensional intelligent reorganization and utilization of knowledge in ‘Biographies of Chinese Thinkers’

Biographical texts often fail to fully showcase their rich semantic knowledge due to traditional narrative modes and knowledge gaps between authors and readers. A multidimensional knowledge reorganization framework for biographical texts involves semantic description, fine-grained knowledge extraction, and knowledge reorganization applications. Based on ontology theory, a core conceptual model for biographical texts was established, employing GPT-4 and BERT for entity recognition. Knowledge reorganization strategies were proposed for key application scenarios and validated through case visualizations. A conceptual model for biographical texts was constructed. Significant enhancement of tag corpora was achieved through LLMs and the RoBERTa-BiLSTM-CRF model, achieving optimal fine-tuning in NER. Strategies based on temporal-spatial transformation, social network analysis, and thematic evolution were proposed, culminating in a knowledge graph centered on “Character-Works-Ideas”. Based on methods proposed by us, issues in semantic description and knowledge extraction of biographical texts have been effectively resolved, enhancing the application value of biographical resources.