基于本体的金矿知识图谱构建方法  

Knowledge Graph Construction Method of Gold Mine based on Ontology

作　　者：张春菊[1] 刘文聪 张雪英[2] 叶鹏 汪陈 朱少楠 张达玉[5] ZHANG Chunju;LIUWencong;ZHANG Xueying;YE Peng;WANG Chen;ZHU Shaonan;ZHANG Dayu(School of Civil Engineering,Hefei University of Technology,Hefei 230009,China;Institute of Geographical Science,Nanjing Normal University,Nanjing 210023,China;Urban Planning and Development Institute,Yangzhou University,Yangzhou 225127,China;School of Geographic and Biologic Information,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;School of Resources and Environmental Engineering,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]合肥工业大学土木与水利工程学院,合肥230009 [2]南京师范大学虚拟地理环境教育部重点实验室,南京210023 [3]扬州大学城市规划与发展研究院,扬州225127 [4]南京邮电大学生物与地理信息学院,南京210023 [5]合肥工业大学资源与环境工程学院,合肥230009

出　　处：《地球信息科学学报》2023年第7期1269-1281,共13页Journal of Geo-information Science

基　　金：国家自然科学基金项目(42171453、41971337);国家重点研发计划项目(2021YFB3900903)。

摘　　要：“地、物、化、遥”等地质矿产勘查和科研工作建立了海量的矿产调查数据,蕴含丰富的成矿构造背景、产出地质环境、矿床地质特征、矿床成因模式等与成矿和分布相关的知识。海量矿产资源相关数据向有效成矿规律知识的转换,已逐渐成为提升地质找矿精度的突破口。本文引入知识工程中本体知识表示技术,开展金矿知识图谱构建方法研究。首先,梳理了金矿成矿模式,确定了金矿概念、金矿实体以及地质特征、成矿特征等属性,运用自顶向下的领域本体知识表示方法构建金矿知识图谱的模式层;其次,基于结构化、半结构化和非结构化的多源异构地质数据源,采用深度学习模型实现金矿信息提取和语义解析,丰富金矿知识图谱的数据层,采用自底向上的方式构建金矿知识图谱;最后,基于图数据库开发了金矿知识管理系统,实现金矿数据管理、知识获取、金矿知识可视化表达、知识库管理、金矿找矿知识查询等功能。本文研究成果可形成“数据-知识”联合驱动的金矿找矿方法,为地质勘查工作中识别、控制和管理矿产资源、提升找矿精度提供参考。Geological and mineral resource survey and scientific research in"geology,geophysics,geochemistry,and remote sensing"have established a large amount of geological and mineral survey data,which contain rich knowledge related to mineralization and distribution of gold mine,such as the metallogenic and tectonic setting,geological environment of occurrence,geological characteristics of mineral mine,genesis and metallogenic model of mine,and so on.The transformation from massive mineral related data to effective metallogenic knowledge has become one of the most important breakthroughs to improve the accuracy of geological prospecting.To solve this problem,through the in-depth analysis of knowledge representation,information extraction,and knowledge fusion in knowledge engineering,this paper explores the knowledge graph construction method of gold mine based on ontology.Firstly,referring to industry norms,gold mine knowledge base,and reference material of geological and mineral resource exploration,the metallogenic model of gold mine is sorted out,and the gold mine concept,gold mine entity,gold mine relationship,gold mine geological attribute,and gold mine metallogenic attribute are determined.In addition,the schema layer of gold mine knowledge graph is constructed by using the top-down ontology knowledge representation method,which represents the conceptual model and logical basis of gold mine knowledge graph.Secondly,based on structured,semi-structured,and unstructured multi-source heterogeneous geological data,the deep learning model is used to realize gold mine knowledge extraction,semantic analysis,and knowledge fusion,which enriches the data layer of gold mine knowledge graph and provides data support for gold mine knowledge graph.The gold mine knowledge graph is constructed in a bottom-up way,and the gold mine knowledge triplet is stored by Neo4j graph database,in which nodes represent gold mine concept,gold mine entity,and gold mine attribute value,while edges represent relation and attribute.Finally,the gold m

关 键 词：知识表示 知识图谱 深度学习 本体 金矿 知识抽取 Neo4j图形数据库 知识管理 

分 类 号：P618.51[天文地球—矿床学]