Intelligent search technology for Jiaodong gold deposits based on large models and GraphRAG

doi:10.13745/j.esf.sf.2025.4.77

Abstract

Abstract:

The Jiaodong gold deposit is a major concentration area of gold resources in eastern China, characterized by complex geological information and an extensive knowledge system. Traditional information retrieval methods struggle to meet the advanced demands of semantic understanding and knowledge reasoning in mineral exploration. To enhance geological knowledge service efficiency, this study develops an intelligent question-answering system for the Jiaodong gold deposit domain based on GraphRAG (Graph-enhanced Retrieval-Augmented Generation) technology. The research utilizes academic papers from CNKI as the corpus, employs OCR and large language models (LLMs) for text parsing and semantic standardization to establish an ontological knowledge system covering core concepts such as mineralization types, ore-controlling structures, and mineral assemblages. The system uses prompt engineering-driven LLMs to automatically extract entities and relationships, constructing a structured knowledge graph integrated into Neo4j. Furthermore, by combining semantic embedding with community clustering algorithms, a knowledge indexing network enables natural language question answering, semantic query expansion, and knowledge provenance. Evaluation results demonstrate the system’s superiority over traditional RAG and general models (e.g., ChatGPT-4o) in answer accuracy, contextual precision, and knowledge interpretability, exhibiting enhanced professional adaptability and reasoning capabilities. The findings provide a novel technical pathway for intelligent information services in gold deposits and theoretical support for knowledge of graph-enhanced language models in geoscience knowledge management.

Key words: GraphRAG, knowledge graph, large language model, Jiaodong gold deposit, knowledge question answering

CLC Number:

P628
P618.51

LI Bowen, WANG Yongzhi, DING Zhengjiang, WANG Bin, WEN Shibo, DONG Yuhao, JI Zheng. Intelligent search technology for Jiaodong gold deposits based on large models and GraphRAG[J]. Earth Science Frontiers, 2025, 32(4): 155-164.

Figures/Tables 9

Fig.1 Architecture of the intelligent search system for Jiaodong gold deposits

Fig.2 Flowchart for optimizing semantic retrieval in GraphRAG

Fig.3 Automatic construction effect subgraph of Jiaodong gold deposit knowledge graph

Fig.4 Knowledge graph auto-construction details visualization

Fig.5 Workflow of the intelligent question-answer system for Jiaodong gold deposits

Fig.6 Knowledge provenance mechanism in the Jiaodong gold deposit intelligent search system

Fig.7 Performance comparison on typical query types in Jiaodong gold deposit retrieval

Table 1 Comparative analysis table (Data current through March 10, 2025)

山东六队提供准确答案	GraphRAG+大模型	ChatGPT-4o通用模型	DeepSeek	豆包
破碎带蚀变岩型	破碎带蚀变岩型	脉状金矿	破碎带蚀变岩型	破碎带蚀变岩型金矿
石英脉型	金石英脉型	层状金矿	层间滑动角砾岩型
硫化物石英脉型	硫化物石英脉型	接触带金矿	火山岩型
网脉型	破碎带石英网脉带型	浸染型金矿	石英脉型
盆缘断裂角砾岩型	盆缘断裂角砾岩型
蚀变砾岩型	蚀变砾岩型
层间滑脱拆离带型	层间滑动构造带型
黄铁矿碳酸盐脉型

Table 2 RAGAS evaluation analysis

评判指标	RAG	GraphRAG
忠实度	0.94	0.96
语义相似度	0.87	0.89
上下文精度	0.86	0.96
上下文召回率	0.97	0.94

References 40

[1]	周永章, 肖凡. 管窥人工智能与大数据地球科学研究新进展[J]. 地学前缘, 2024, 31(4): 1-6. DOI
[2]	王成彬, 王明果, 王博, 等. 融合知识图谱的矿产资源定量预测[J]. 地学前缘, 2024, 31(4): 26-36. DOI
[3]	ZHANG Y, WEI C, WU S, et al. Geogpt: understanding and processing geospatial tasks through an autonomous gpt[J]. arXiv preprint, 2023. DOI: arXiv:2307.07930.
[4]	马凯. 地质大数据表示与关联关键技术研究[D]. 武汉: 中国地质大学(武汉), 2018.
[5]	WANG B, MA K, WU L, et al. Visual analytics and information extraction of geological content for text-based mineral exploration reports[J]. Ore Geology Reviews, 2022, 144: 104818.
[6]	朱小龙. 地质文本中油气藏特征提取及成藏知识图谱构建研究[D]. 武汉: 中国地质大学(武汉), 2021.
[7]	杨立强, 邓军, 王中亮, 等. 胶东中生代金成矿系统[J]. 岩石学报, 2014, 30(9): 2447-2467.
[8]	冯雅杰, 王永志, 丁正江, 等. 胶西北金矿成矿模式及找矿技术方法演变[J]. 地学前缘, 2025, 32(4): 165-181.
[9]	王永志, 金樑, 朱月琴, 等. 基于大数据技术的地学文档关键词提取算法研发[J]. 地球物理学进展, 2018, 33(3): 1274-1281.
[10]	邱芹军, 田苗, 吴麒瑞, 等. 基于多源异构数据的地质知识图谱构建与应用[J/OL]. 地学前缘, 2024, 31: 1-17[2025-05-22]. https://doi.org/10.13745/j.esf.sf.2024.11.69.
[11]	CHEN J, LIN H, HAN X, et al. Benchmarking large language models in retrieval-augmented generation[C]// Proceedings of the AAAI Conference on Artificial Intelligence. 2024, 38(16): 17754-17762.
[12]	SIRIWARDHANA S, WEERASEKERA R, WEN E, et al. Improving the domain adaptation of retrieval augmented generation (RAG) models for open domain question answering[J]. Transactions of the Association for Computational Linguistics, 2023, 11: 1-17.
[13]	文森, 钱力, 胡懋地, 等. 基于大语言模型的问答技术研究进展综述[J]. 数据分析与知识发现, 2024, 8(6): 16-29. DOI
[14]	EDGE D, TRINH H, CHENG N, et al. From local to global: A graph rag approach to query-focused summarization[J]. arXiv preprint, 2024. DOI: arXiv:2404.16130.
[15]	MARKUS J. BUEHLERCA1. Generative retrieval-augmented ontologic graph and multiagent strategies for interpretive large language model-based materials design[J]. ACS Engineering Au, 2024, 4(2): 241-277. DOI PMID
[16]	PATRICK L, ETHAN P, ALEKSANDRA P, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks[J]. Neural Information Processing Systems, 2020, 33: 9459-9474.
[17]	KHADIR A C, ALIANE H, GUESSOUM A. Ontology learning: grand tour and challenges[J]. Computer Science Review, 2021, 39: 100339.
[18]	周永章, 张前龙, 黄永健, 等. 钦杭成矿带斑岩铜矿知识图谱构建及应用展望[J]. 地学前缘, 2021, 28(3): 67-75. DOI
[19]	TORO S, ANAGNOSTOPOULOS A V, BELLO S M, et al. Dynamic retrieval augmented generation of ontologies using artificial intelligence (DRAGON-AI)[J]. Journal of Biomedical Semantics, 2024, 15(1): 19. DOI PMID
[20]	叶育鑫, 刘家文, 曾婉馨, 等. 基于本体指导的矿产预测知识图谱构建研究[J]. 地学前缘, 2024, 31(4): 16-25. DOI
[21]	HU X M, MA X G, MA C, et al. The geoscience knowledge system, ontology and knowledge graph for data-driven discovery: preface[J]. Geoscience Frontiers, 2023, 14(5): 332-333.
[22]	王岩, 王登红, 王成辉, 等. 基于地质大数据的中国金矿时空分布规律定量研究[J]. 地学前缘, 2024, 31(4): 438-455. DOI
[23]	刘向东, 丁正江, 邱昆峰, 等. 胶东台上—水旺庄巨型金矿床矿体空间分布规律及深部找矿预测[J/OL]. 地学前缘, 2024, 31: 1-19[2024-12-31]. https://doi.org/10.13745/j.esf.sf.2024.12.52.
[24]	王斌, 周明岭, 丁正江, 等. 胶西北蚀变岩型金矿构造控矿机制与三维深部定位预测[J]. 地学前缘, 2025, 32(4): 140-154.
[25]	张前龙, 周永章, 郭兰萱, 等. 找矿知识图谱的智能化应用: 以钦杭成矿带斑岩铜矿为例[J]. 地学前缘, 2024, 31(4): 7-15. DOI
[26]	DING Y J, FAN W Q, NING L B, et al. A survey on RAG meets LLMs: towards retrieval-augmented large language models[J]. arXiv preprint, 2024. DOI: arXiv:2405.06211v3.
[27]	WANG J, ZENG J, SHENG J. Enhancing and accelerating image-text retrieval with knowledge graphs and FAISS[C]// 2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT). IEEE, 2024: 1-6.
[28]	GUPTA S, RANJAN R, SINGH S N. A comprehensive survey of Retrieval-Augmented Generation (RAG): evolution, current landscape and future directions[J]. arXiv preprint, 2024. DOI: arXiv:2410.12837.
[29]	FANG F T, BAI Y L, NI S W, et al. Enhancing noise robustness of retrieval-augmented language models with adaptive adversarial training[J]. arXiv preprint, 2024. DOI: arXiv:2405.20978.
[30]	DONG Y, WANG S, ZHENG H, et al. Advanced RAG models with graph structures: optimizing complex knowledge reasoning and text generation[C]// 2024 5th International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). IEEE, 2024: 626-630.
[31]	HAN H Y, WANG Y, SHOMER H, et al. Retrieval-augmented generation with graphs (graphrag)[J]. arXiv preprint, 2024. DOI: arXiv:2501.00309.
[32]	韦一金, 任有强, 赵慧, 等. 基于GraphRAG的中国马铃薯新品种知识图谱构建[J]. 植物遗传资源学报, 2025, 26(6), 1229-1242.
[33]	WU J D, ZHU J Y, QI Y L, et al. Medical graph rag: towards safe medical large language model via graph retrieval-augmented generation[J]. arXiv preprint, 2024. DOI: arXiv:2408.04187.
[34]	PENG BC, ZHU Y, LIU Y C, et al. Graph retrieval-augmented generation: a survey[J]. arXiv preprint, 2024. DOI: arXiv:2408.08921v2.
[35]	ZHANG Q, CHEN S, BEI Y, et al. A Survey of graph retrieval-augmented generation for customized large language models[J]. arXiv preprint, 2025. DOI: arXiv:2501.13958.
[36]	QIU Q J, TIAN M, MA K, et al. A question answering system based on mineral exploration ontology generation: a deep learning methodology[J]. Ore Geology Reviews, 2023, 153: 105294.
[37]	宋明春, 李三忠, 伊丕厚, 等. 中国胶东焦家式金矿类型及其成矿理论[J]. 吉林大学学报(地球科学版), 2014, 44(1): 87-104.
[38]	宋明春. 胶东金矿深部找矿主要成果和关键理论技术进展[J]. 地质通报, 2015, 34(9): 1758-1771.
[39]	SARMAH B, MEHTA D, HALL B, et al. Hybridrag: integrating knowledge graphs and vector retrieval augmented generation for efficient information extraction[C]// Proceedings of the 5th ACM International Conference on AI in Finance. Brooklyn: New York University, 2024: 608-616.
[40]	ES S, JAMES J, ESPINOSA A L, et al. RAGAS: automated evaluation of retrieval augmented generation[C]// Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics:System Demonstrations. Malta: Association for Computational Linguistics, 2024: 150-158.