LLM-S2KG: LLM-Based Semantic–Structural Dual Knowledge Graph

In knowledge graph construction tasks, recent research often leverages Large Language Models (LLMs) to enhance the efficiency and accuracy of unstructured data processing. However, current LLMs rely on lexical co-occurrence statistical patterns, making it difficult to capture deep semantic relationships. Furthermore, existing research largely focuses on entity-relation extraction or semantic-level optimization, overlooking the inherent hierarchical logical structures within text paragraphs (e.g., chapter organization, paragraph coherence). This leads to insufficient semantic completeness and damaged structural consistency in the constructed knowledge graphs. To address this dual limitation, we propose LLM-S2KG, a semantic–structural information extraction method that integrates LLMs with semantic correlation analysis. This method achieves synergistic modeling of semantic depth and logical structure by simultaneously performing dual parsing of keywords and structure, discovering and completing semantic associations, and finally integrating these dual graphs for construction. Experiments show that in query tasks, LLM-S2KG improved the F1 score by 0.1183, 0.1412, and 0.0231 compared with KeyBERT, TF-IDF, and LLM-KG, respectively. In fill-in-the-blank QA tasks, it achieved an accuracy of 94.81%; and in open-ended QA tasks, an accuracy of 85.885%, moderately outperforming LLM Triple Extraction (73.308%), LLM Triple Extraction with Source Sentence Augmentation (80.085%), and Chroma Database Import (76.150%). In summary, LLM-S2KG provides a unified modeling paradigm for structured knowledge extraction using LLMs, featuring mutual empowerment and co-evolution of semantics and structure.