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摘要: 【目的】目前的大语言模型往往将智能写作视为无结构的纯文本数据,忽视了文本内部的逻辑结构和联系,制约了内容质量效果。【方法】精准快速地定位相关信息是提升内容质量的关键,通过融合 RAG 与知识图谱(KG)的智能写作优化技术,构建知识图谱来整合信息,保留文本的内部逻辑和关联性,来提升内容写作效果。【结果】该方法能够更精确地解析写作需求,并通过引入知识图谱,提高信息检索精确度及优化了内容生成质量。【结论】该技术已在中国新闻技术工作者联合会 AIGC 应用研究中心(广西实验室)试运行近半年,显示该方法可显著提升内容生成的精准度和效率。
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[1] Hu,Y.,Lei,Z.,Zhang,Z.,Pan,B.,Ling,C.,& Zhao,L.(2024).GRAG:Graph Retrieval-Augmented Generation.arXiv preprint arXiv:2405.16506. [2] Edge D,Trinh H,Cheng N,et al.From Local to Global:A Graph RAG Approach to Query-Focused Summarization[J].arXiv preprint arXiv:2404.16130,2024. [3] 梁莹 . 人工智能写作与新闻采编的融合与创新 [J]. 新闻潮,2023(7):37-39,52. [4] Yunfan Gao,Yun Xiong,Xinyu Gao,Kangxiang Jia,Jinliu Pan,Yuxi Bi,Yi Dai,Jiawei Sun,Meng Wang,Haofen Wang.Retrieval-Augmented Generation for Large Language Models:A Survey[J].arXiv preprint arXiv:2312.10997v5 [cs.CL],2024. [5] D.Edge,H.Trinh,N.Cheng,J.Bradley,A.Chao,A.Mody,S.Truitt,J.Larson.From Local to Global:A Graph RAG Approach to Query-Focused Summarization [cs.CL].arXiv preprint arXiv:2404.16130v1,24 Apr 2024. [6] Dong,Y.,Wang,S.,Zheng,H.,Chen,J.,Zhang,Z.,& Wang,C.(2024).Graph-Based Retrieval-Augmented Generation for Complex Knowledge Reasoning.arXiv preprint arXiv:2411.03572. [7] 黄薇,周卫,李宏杰,张涛 . 基于 BERT+BILSTM+CRF的中国非物质文化遗产知识图谱构建 [J]. 现代计算机,2023,29(24):74-78. [8] Derong Xu,Wei Chen,Wenjun Peng,Chao Zhang,Tong Xu,Xiangyu Zhao,Xian Wu,Yefeng Zheng,Yang Wang,Enhong Chen.Large Language Models for Generative Information Extraction:A Survey[J].arXiv preprint arXiv:2312.17617v2 [cs.CL],2024. [9] 沈思,严大钰,卞嘉欣,何宏旭 . 基于学术知识图谱的增强语义表示与检索 [J]. 湖南大学学报(自然科学版),2024,51(6):108-118. [10] Peng,B.,et al.(2024).Graph Retrieval-Augmented Generation:A Survey.arXiv preprint arXiv:2408.08921v2. [11] Dong,Y.,et al.(2024).Graph-Based RetrievalAugmented Generation for Complex Knowledge Reasoning.arXiv preprint arXiv:2411.03572v1. [12] Min,C.,et al.(2025).Scalable GraphRAG Deployment Framework for Enterprise Environments.arXiv preprint arXiv:2507.03226v1. [13] 马恒志,钱育蓉,冷洪勇,等 . 知识图谱嵌入研究进展综述 [J]. 计算机工程,2025,51(2):18-34. [14] D.Edge,H.Trinh,N.Cheng,J.Bradley,A.Chao,A.Mody,S.Truitt,J.Larson.From Local to Global:A Graph RAG Approach to Query-Focused Summarization [cs.CL].arXiv preprint arXiv:2404.16130v1,24 Apr 2024. [15] Z.Xu,M.J.Cruz,M.Guevara,T.Wang,M.Deshpande,X.Wang,Z.Li.Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering [D].arXiv preprint arXiv:2404.17723v2,2024. [16] D.Li,S.Yang,Z.Tan,J.Y.Baik,S.Yun,J.Lee,A.Chacko,B.Hou,D.Duong-Tran,Y.Ding,H.Liu,L.Shen,T.Chen.DALK:Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer’s Disease Questions with Scientific Literature [cs.CL].arXiv preprint arXiv:2405.04819v2,12 May 2024. [17] Luo,H.,E,H.,Chen,G.,Lin,Q.,Guo,Y.,Xu,F.,Kuang,Z.,Song,M.,Wu,X.,Zhu,Y.,& Tuan,L.A.(2025).Graph-R1:An Agentic GraphRAG Framework via End-to-End Reinforcement Learning.arXiv preprint arXiv:2507.21892v1. [18] Xiang,Z.,et al.(2025).When to use Graphs in RAG:A Comprehensive Analysis for Graph Retrieval-Augmented Generation.arXiv preprint arXiv:2506.05690v1. -

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