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钦杭成矿带斑岩铜矿知识图谱构建及应用展望 被引量:15

Constructing knowledge graph for the porphyry copper deposit in the Qingzhou-Hangzhou Bay area:Insight into knowledge graph based mineral resource prediction and evaluation
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摘要 知识图谱使用人与机器能共同理解的语言,以“图”的方式来描述真实世界,是人工智能研究的重要方向之一。本研究是构建单体矿床、成矿系列和重要成矿区(带)的知识图谱实验的一部分,收集了钦杭成矿带6个较为典型的斑岩铜矿、斑岩-夕卡岩型铜矿的原始文本数据,参照斑岩铜矿床概念模型进行知识获取,标注、抽提文本中的实体、关系、属性,构建了具备基本应用功能的斑岩铜矿床知识图谱。基于未来矿产资源预测评价应充分理解地球系统、成矿系统、勘查系统、预测评价系统相互关系的认识,讨论了建立“地球系统-成矿系统-勘查系统-预测评价系统”关联知识图谱体系需要解决的关键科学技术问题,包括“地-矿-勘-评系统”领域本体及知识图谱递进关联体系,大规模“地-矿-勘-评系统”领域本体及知识图谱的自动构建技术,基于多模态关联数据嵌入“地-矿-勘-评系统”知识图谱自演化、补全技术以及基于知识图谱、大数据挖掘和人工智能的地球系统资源预测理论与方法。 Knowledge graphs,fundamental to artificial intelligence,describe the real world in graphic forms using a language that can be understood by both humans and machines.This paper presents a case study on the construction of knowledge graph for porphyry copper deposit.The raw text data were collected and integrated from six selected porphyry and porphyry-skarn copper deposits in the Qinzhou-Hangzhou Bay metallogenic belt,one of the key metallogenic belts of China.The entities,relations and attributes in the text are labeled and extracted in reference to the conceptual model of porphyry copper deposit.The resulted knowledge graph has the basic application functions.As part of a planned integrated knowledge graph—from a single deposit,through upper-geared metallogenic series,to top metallogenic province(belt)—the present study may be extended toward understanding and improving future way of mineral resource prediction and evaluation.The interrelationship among the earth system,the metallogenic system,the exploration system,and the prediction and evaluation system(ES-MS-ES-PS)should be fully understood,and a knowledge graph for the ES-MS-ES-PS system is essential.The key scientific and technological challenges to attain such a large-scale knowledge graph for the ES-MS-ES-PS system thus include system of progressive association of domain ontology and knowledge graph,automation technology for constructing large-scale domain ontology and knowledge graph,self-evolution and complementary techniques for embedding multi-modal correlation data into knowledge graph,and ES-resource prediction theory and methods based on knowledge graph,big-data mining and artificial intelligence.
作者 周永章 张前龙 黄永健 杨威 肖凡 吉俊杰 韩枫 唐磊 欧阳冲 沈文杰 ZHOU Yongzhang;ZHANG Qianlong;HUANG Yongjian;YANG Wei;XIAO Fan;JI Junjie;HAN Feng;TANG Lei;OUYANG Chong;SHEN Wenjie(Center for Earth Environment&Resources,Sun Yat-sen University,Guangzhou 510275,China;School of Earth Sciences&Geological Engineering,Sun Yat-Sen University,Guangzhou 510275,China;Guangdong Xuanyuan Network Tech.Inc.,Guangzhou 510000,China;Guangdong Institute of High Quality Resources and Environment,Guangzhou 510000,China;Guangdong Provincial Key Lab of Geological Processes and Mineral Resource Survey,Guangzhou 510275,China)
出处 《地学前缘》 EI CAS CSCD 北大核心 2021年第3期67-75,共9页 Earth Science Frontiers
基金 国家自然科学基金项目(U1911202) 广东省重点研发计划项目(2020B1111370001) 国家重点研发计划项目(2016YFC0600506) 广东省地质过程与矿产资源探查重点实验室基金项目。
关键词 知识图谱 知识获取 地质大数据 矿产资源预测评价 地质领域本体 斑岩铜矿 钦杭成矿带 knowledge graph knowledge acquisition geological big data prediction and evaluation of mineral resource geological domain ontology porphyry copper deposit Qinzhou-Hangzhou Bay metallogenic belt(South China)
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