16章+摘要与附录
24幅解释图
33项参考资料
104页双语报告
知识连接,证据先行
按章阅读
00
研究摘要
1 小节 ↗01研究问题与证据方法
7 小节 ↗02知识图谱的语义与证据基础
6 小节 ↗03检索增强生成的机制与限制
6 小节 ↗04微软GraphRAG的工作机制
6 小节 ↗05图增强检索方法的比较与选择
6 小节 ↗06多模态知识图谱的表示与对齐
6 小节 ↗07美团菜品图谱与CMIngre案例分析
6 小节 ↗08统一证据架构与工程实现
6 小节 ↗09分层评测实验设计与不确定性
6 小节 ↗10成本性能与持续运行
6 小节 ↗11权限治理安全与责任
6 小节 ↗12企业与科研应用蓝图
6 小节 ↗13教育文化与视觉思维实践
6 小节 ↗14校园菜品多模态GraphRAG应用设计
6 小节 ↗15可复算的微型图谱实验
6 小节 ↗16研究议程采用路线与综合判断
6 小节 ↗17附录A 研究与实施操作表
6 小节 ↗18附录B 双语术语与易混概念
6 小节 ↗完整报告下载
PDF和Word均为104页中英逐节对照完整版;网页按语言分别呈现。
参考文献与官方资料
检索截至2026年10月19日。保留原题名与阅读范围;预印本和技术文章的结论须结合原始条件理解。
- [R01]Jonathan Larson and Steven Truitt (2024-02-13). GraphRAG: Unlocking LLM discovery on narrative private dataMicrosoft Research blog · article or documentation content
- [R02]Darren Edge et al. (2024; v2 2025-02-19). From Local to Global: A GraphRAG Approach to Query-Focused SummarizationResearch preprint · abstract and workflow sections
- [R03]美团技术团队 (2024-05-17). 基于多模态信息抽取的菜品知识图谱构建Industry technical article · article or documentation content
- [R04]Lanjun Wang et al. (2024 online; IEEE TMM volume 27 (2025), pp. 2863–2874). Toward Chinese Food Understanding: A Cross-Modal Ingredient-Level BenchmarkIEEE Transactions on Multimedia · abstract or primary publication record
- [R05]CMIngre research team (accessed 2026-10-18). CMIngre Dataset and Supplementary MaterialAuthor dataset · article or documentation content
- [R06]Patrick Lewis et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP TasksResearch paper · abstract or primary publication record
- [R07]Boci Peng et al. (2024 preprint; later journal version). Graph Retrieval-Augmented Generation: A SurveySurvey · abstract or primary publication record
- [R08]Haoyu Han et al. (2025 arXiv identifier). Retrieval-Augmented Generation with Graphs (GraphRAG)Survey · abstract or primary publication record
- [R09]Haoyu Han et al. (2025 preprint). RAG vs. GraphRAG: A Systematic Evaluation and Key InsightsComparative study · abstract or primary publication record
- [R10]Zirui Guo et al. (2024 preprint; 2025 EMNLP Findings). LightRAG: Simple and Fast Retrieval-Augmented GenerationResearch paper · abstract or primary publication record
- [R11]Bernal Jiménez Gutiérrez et al. (2024). HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language ModelsResearch paper · abstract or primary publication record
- [R12]Bernal Jiménez Gutiérrez et al. (2025). From RAG to Memory: Non-Parametric Continual Learning for Large Language ModelsHippoRAG 2 research paper · abstract or primary publication record
- [R13]Xiangrong Zhu et al. (2025). Knowledge Graph-Guided Retrieval Augmented GenerationKG2RAG research paper · abstract or primary publication record
- [R14]Xiaoxin He et al. (2024). G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question AnsweringResearch paper · abstract or primary publication record
- [R15]Xiangru Zhu et al. (2022 preprint). Multi-Modal Knowledge Graph Construction and Application: A SurveySurvey · abstract or primary publication record
- [R16]Zhuo Chen et al. (2024). Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive SurveySurvey · abstract or primary publication record
- [R17]Alec Radford et al. (2021). Learning Transferable Visual Models From Natural Language SupervisionICML research paper · abstract or primary publication record
- [R18]Wenhu Chen et al. (2022). MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and TextEMNLP research paper · abstract or primary publication record
- [R19]Xueyao Wan and Hang Yu (2025 first version; v3 2026-07-18). MMGraphRAG: Bridging Vision and Language with Interpretable Multimodal Knowledge GraphsResearch preprint · abstract or primary publication record
- [R20]Xu Yuan, Liangbo Ning, Qingqing Ye, Wenqi Fan and Qing Li (2025 preprint; v2 2026-04-27; SIGIR 2026). mKG-RAG: Leveraging Multimodal Knowledge Graphs in Retrieval-Augmented Generation for Knowledge-intensive VQASIGIR 2026 paper; inspected arXiv version · abstract or primary publication record
- [R21]Yin Wu, Quanyu Long, Jing Li, Jianfei Yu and Wenya Wang (2025 preprint v2; related SIGIR 2026 Resource Track version). Visual-RAG: Benchmarking Text-to-Image Retrieval Augmented Generation for Visual Knowledge Intensive QueriesBenchmark paper; inspected arXiv v2 and author repository · abstract or primary publication record
- [R22]Juyeon Kim, Geon Lee, Taeuk Kim and Kijung Shin (2025). KGMEL: Knowledge Graph-Enhanced Multimodal Entity LinkingSIGIR 2025 short paper; inspected arXiv record · abstract or primary publication record
- [R23]Manuel Faysse et al. (2024). ColPali: Efficient Document Retrieval with Vision Language ModelsResearch paper · abstract or primary publication record
- [R24]Zirui Guo et al. (2025). RAG-Anything: All-in-One RAG FrameworkResearch preprint · abstract or primary publication record
- [R25]Shahul Es et al. (2024). RAGAs: Automated Evaluation of Retrieval Augmented GenerationEACL System Demonstrations · abstract or primary publication record
- [R26]Hao Yu et al. (2024). Evaluation of Retrieval-Augmented Generation: A SurveySurvey preprint · abstract or primary publication record
- [R27]V. A. Traag, L. Waltman and N. J. van Eck (2019). From Louvain to Leiden: guaranteeing well-connected communitiesScientific Reports · abstract or primary publication record
- [R28]W3C (2013). PROV OverviewW3C Working Group Note · abstract or primary publication record
- [R29]Nelson F. Liu et al. (2023 preprint). Lost in the Middle: How Language Models Use Long ContextsResearch paper · abstract or primary publication record
- [R30]Alon Talmor et al. (2021). MultiModalQA: Complex Question Answering over Text, Tables and ImagesBenchmark paper · abstract or primary publication record
- [R31]Yingshan Chang et al. (2021 preprint; CVPR 2022). WebQA: Multihop and Multimodal QABenchmark paper · abstract or primary publication record
- [R32]Microsoft (accessed 2026-10-18). GraphRAG Indexing DataflowOfficial documentation · article or documentation content
- [R33]Microsoft (accessed 2026-10-18). GraphRAG Global SearchOfficial documentation · article or documentation content
