Ontology-driven relational data mapping for constructing a knowledge graph of porphyry copper deposits

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Earth Science Informatics Pub Date : 2024-04-13 DOI:10.1007/s12145-024-01307-5
Chengbin Wang, Liangquan Tan, Yuanjun Li, Mingguo Wang, Xiaogang Ma, Jianguo Chen
{"title":"Ontology-driven relational data mapping for constructing a knowledge graph of porphyry copper deposits","authors":"Chengbin Wang, Liangquan Tan, Yuanjun Li, Mingguo Wang, Xiaogang Ma, Jianguo Chen","doi":"10.1007/s12145-024-01307-5","DOIUrl":null,"url":null,"abstract":"<p>Geoscience knowledge graph has become a popular topic in recent years. A series of studies have been reported to introduce the construction and application of geoscience knowledge graphs from different views. The relational geoscience dataset with high knowledge density and data quality is an important digital heritage of geoscience. The relational dataset has not been taken seriously in the geoscience knowledge graph research. In this study, we proposed a quick method of building a geoscience knowledge graph using relational data mapping to triples. First, the use-case-driven method was applied to design the ontology of porphyry copper deposits. Second, the mapping rules were built based on the porphyry copper ontology. Third, the knowledge graph of the porphyry copper deposit was constructed based on relational data mapping and knowledge fusion. Based on the resulting knowledge graph, several exploratory cases were conducted to make knowledge reasoning and discovery. It is indicated that the solution proposed in this study is a fast batch-processing geoscience knowledge graph construction method. The experiences from this study can benefit the construction of knowledge graphs in other geoscience disciplines and promote knowledge discovery.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":null,"pages":null},"PeriodicalIF":2.7000,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01307-5","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Geoscience knowledge graph has become a popular topic in recent years. A series of studies have been reported to introduce the construction and application of geoscience knowledge graphs from different views. The relational geoscience dataset with high knowledge density and data quality is an important digital heritage of geoscience. The relational dataset has not been taken seriously in the geoscience knowledge graph research. In this study, we proposed a quick method of building a geoscience knowledge graph using relational data mapping to triples. First, the use-case-driven method was applied to design the ontology of porphyry copper deposits. Second, the mapping rules were built based on the porphyry copper ontology. Third, the knowledge graph of the porphyry copper deposit was constructed based on relational data mapping and knowledge fusion. Based on the resulting knowledge graph, several exploratory cases were conducted to make knowledge reasoning and discovery. It is indicated that the solution proposed in this study is a fast batch-processing geoscience knowledge graph construction method. The experiences from this study can benefit the construction of knowledge graphs in other geoscience disciplines and promote knowledge discovery.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于构建斑岩型铜矿床知识图谱的本体驱动关系数据映射
近年来,地球科学知识图谱已成为一个热门话题。一系列研究报告从不同角度介绍了地球科学知识图谱的构建和应用。关系型地球科学数据集知识密度高、数据质量好,是地球科学的重要数字遗产。在地球科学知识图谱研究中,关系数据集尚未得到重视。在本研究中,我们提出了一种利用关系数据映射为三元组快速构建地球科学知识图谱的方法。首先,采用用例驱动法设计斑岩铜矿床本体。其次,根据斑岩铜矿本体建立映射规则。第三,基于关系数据映射和知识融合构建斑岩铜矿床知识图谱。基于生成的知识图谱,进行了多个探索性案例的知识推理和发现。结果表明,本研究提出的解决方案是一种快速批量处理的地球科学知识图谱构建方法。本研究的经验可有益于其他地球科学学科知识图谱的构建,促进知识发现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
期刊最新文献
Ontology-driven relational data mapping for constructing a knowledge graph of porphyry copper deposits A novel machine learning approach for interpolating seismic velocity and electrical resistivity models for early-stage soil-rock assessment ENSO dataset & comparison of deep learning models for ENSO forecasting Groundwater level estimation using improved deep learning and soft computing methods CEDG-GeoQA: Knowledge base question answering for the geoscience domain via Chinese entity description graph
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1