融合大语言模型与动态反馈机制的鸟类知识图谱补全方法OA

Knowledge graphs have been widely used to transform fragmented information into structured and inferable relations,thereby supporting research on bird migration,conservation,and biodiversity.However,due to the high specialization and complexi-ty of avian knowledge,as well as the diversity of entity relations,existing bird-domain knowledge graphs have remained limited in coverage and exhibited shallow relation extraction.To address these limitations,a bird-domain knowledge graph completion frame-work was proposed by integrating large language models with fine-tuning and a dynamic feedback mechanism.The framework was designed to enhance the understanding and reasoning of fine-grained avian knowledge for more efficient graph completion.Specifi-cally,the Qwen model was fine-tuned using a domain-oriented instruction set to improve its specialized inference capability,and a dynamic feedback mechanism was incorporated to further strengthen reasoning and information extraction.Under relaxed evaluation settings,the proposed method achieved a Hits@1 of 63.0%,representing a 50.76%improvement over ChatGPT.With feedback en-hancement,the performance increased to 95.3%,surpassing DeepSeek by 3.1%.The correct model outputs were iteratively integrat-ed back into the graph,forming a closed-loop process of fine-tuning,completion,feedback,and expansion,which continuously strengthened self-learning and vertical-domain completion ability.