基于大模型的全球航展知识图谱构建与问答系统框架研究OA

Against the backdrop of rapid advancement of global aerospace technology,airshows serve as a pivotal plat-form for international industry exchanges and technological demonstrations,accumulating massive heterogeneous infor-mation including airshow history,exhibit technical parameters,and social media public opinion on airshow topics.Effi-cient information acquisition and utilization through existing technologies hold significant practical value.A global air-show domain knowledge graph construction method based on large-model research is proposed.By integrating retrieval augmentation generation(RAG)technology,an airshow domain Q&A system framework is constructed on this graph through fine-tuning low-rank adaptation(LoRA)and implementing chain-of-thought design for pre-trained models,con-structing the domain knowledge graph,and introducing document parsing,semantic chunking,hybrid retrieval,and cue enhancement techniques.Dataset-based tests demonstrate that the proposed method improves the benchmark model in accuracy,precision,and F1-score metrics,and subjective and objective evaluations of the Q&A system show that the RAG framework effectively reduces model"hallucination"frequency and enhances professional Q&A capabilities in spe-cialized domains.