Fine-tuned large language models with structured prompts enable efficient construction of lung cancer knowledge graphs.

Conventional methods for constructing lung cancer knowledge graphs require extensive annotated data, resulting in high construction costs. To address this challenge, this study developed the Knowledge Graph Large Model (KGLM) through a fine-tuning strategy to efficiently extract lung cancer knowledge triples. Carefully designed prompts were used during knowledge extraction, efficiently process complex, unstructured lung cancer information. Simultaneously, semi-structured clinical data was integrated with structured public graph data, and an entity alignment approach based on Jaccard similarity and Sentence-BERT (SBERT) successfully constructed the Lung Cancer Knowledge Graph (LCKG). The experimental outcomes highlighted the significance of our unified framework, which integrates prompt engineering and fine-tuning. Notably, the KGLM model with its structured prompts demonstrated superior performance in relation extraction tasks on large datasets, achieving an F1 score of 82%, a 25% improvement over baseline models. Furthermore, comparisons with traditional deep learning methods validated the effectiveness and suitability of employing large language models for knowledge graph construction.