Geoscience knowledge graph in the big data era
Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery...
Gespeichert in:
Veröffentlicht in: | Science China. Earth sciences 2021-07, Vol.64 (7), p.1105-1114 |
---|---|
Hauptverfasser: | , , , , , , , , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1114 |
---|---|
container_issue | 7 |
container_start_page | 1105 |
container_title | Science China. Earth sciences |
container_volume | 64 |
creator | Zhou, Chenghu Wang, Hua Wang, Chengshan Hou, Zengqian Zheng, Zhiming Shen, Shuzhong Cheng, Qiuming Feng, Zhiqiang Wang, Xinbing Lv, Hairong Fan, Junxuan Hu, Xiumian Hou, Mingcai Zhu, Yunqiang |
description | Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph. Based on adopting the graph pattern of general knowledge representation, the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge, and integrates geoscience knowledge elements, such as map, text, and number, to establish an all-domain geoscience knowledge representation model. A federated, crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here, which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists. We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis, which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph. A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis, but also advance the construction of the high-precision geological time scale driven by big data, the compilation of intelligent maps driven by rules and data, and the geoscience knowledge evolution and reasoning analysis, among others. It will further expand the new directions of geoscience research driven by both data and knowledge, break new ground where geoscience, information science, and data science converge, realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research. |
doi_str_mv | 10.1007/s11430-020-9750-4 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2549838222</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2549838222</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2314-6a537c76d0f38f982458b15fd84bc45f195b8b837d376fce30ea1885670d5dcd3</originalsourceid><addsrcrecordid>eNp1kEtLAzEQx4MoWGo_gLeA59RMHpvsUYrWQsGLnkM2j-3WuluTLeK3N2UFT85lBub_gB9Ct0CXQKm6zwCCU0IZJbWSlIgLNANd1QR0rS7LXSlBFAd-jRY572kZXj5MzdByHYbsutC7gN_74esQfBtwm-xxh7sej7uAm67F3o4Wh2Rv0FW0hxwWv3uO3p4eX1fPZPuy3qwetsQxDoJUVnLlVOVp5DrWmgmpG5DRa9E4ISPUstGN5spzVUUXOA0WtJaVol565_kc3U25xzR8nkIezX44pb5UGiZFrblmjBUVTCqXhpxTiOaYug-bvg1QcyZjJjKmkDFnMkYUD5s8uWj7NqS_5P9NP9f8Y0U</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2549838222</pqid></control><display><type>article</type><title>Geoscience knowledge graph in the big data era</title><source>SpringerNature Journals</source><source>Alma/SFX Local Collection</source><creator>Zhou, Chenghu ; Wang, Hua ; Wang, Chengshan ; Hou, Zengqian ; Zheng, Zhiming ; Shen, Shuzhong ; Cheng, Qiuming ; Feng, Zhiqiang ; Wang, Xinbing ; Lv, Hairong ; Fan, Junxuan ; Hu, Xiumian ; Hou, Mingcai ; Zhu, Yunqiang</creator><creatorcontrib>Zhou, Chenghu ; Wang, Hua ; Wang, Chengshan ; Hou, Zengqian ; Zheng, Zhiming ; Shen, Shuzhong ; Cheng, Qiuming ; Feng, Zhiqiang ; Wang, Xinbing ; Lv, Hairong ; Fan, Junxuan ; Hu, Xiumian ; Hou, Mingcai ; Zhu, Yunqiang</creatorcontrib><description>Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph. Based on adopting the graph pattern of general knowledge representation, the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge, and integrates geoscience knowledge elements, such as map, text, and number, to establish an all-domain geoscience knowledge representation model. A federated, crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here, which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists. We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis, which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph. A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis, but also advance the construction of the high-precision geological time scale driven by big data, the compilation of intelligent maps driven by rules and data, and the geoscience knowledge evolution and reasoning analysis, among others. It will further expand the new directions of geoscience research driven by both data and knowledge, break new ground where geoscience, information science, and data science converge, realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.</description><identifier>ISSN: 1674-7313</identifier><identifier>EISSN: 1869-1897</identifier><identifier>DOI: 10.1007/s11430-020-9750-4</identifier><language>eng</language><publisher>Beijing: Science China Press</publisher><subject>Analysis ; Big Data ; Collaboration ; Construction ; Data ; Data analysis ; Data mining ; Earth and Environmental Science ; Earth science ; Earth Sciences ; Geological mapping ; Geological time ; Graphical representations ; Intelligence ; Knowledge representation ; Research Paper</subject><ispartof>Science China. Earth sciences, 2021-07, Vol.64 (7), p.1105-1114</ispartof><rights>Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021</rights><rights>Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c2314-6a537c76d0f38f982458b15fd84bc45f195b8b837d376fce30ea1885670d5dcd3</citedby><cites>FETCH-LOGICAL-c2314-6a537c76d0f38f982458b15fd84bc45f195b8b837d376fce30ea1885670d5dcd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11430-020-9750-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11430-020-9750-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Zhou, Chenghu</creatorcontrib><creatorcontrib>Wang, Hua</creatorcontrib><creatorcontrib>Wang, Chengshan</creatorcontrib><creatorcontrib>Hou, Zengqian</creatorcontrib><creatorcontrib>Zheng, Zhiming</creatorcontrib><creatorcontrib>Shen, Shuzhong</creatorcontrib><creatorcontrib>Cheng, Qiuming</creatorcontrib><creatorcontrib>Feng, Zhiqiang</creatorcontrib><creatorcontrib>Wang, Xinbing</creatorcontrib><creatorcontrib>Lv, Hairong</creatorcontrib><creatorcontrib>Fan, Junxuan</creatorcontrib><creatorcontrib>Hu, Xiumian</creatorcontrib><creatorcontrib>Hou, Mingcai</creatorcontrib><creatorcontrib>Zhu, Yunqiang</creatorcontrib><title>Geoscience knowledge graph in the big data era</title><title>Science China. Earth sciences</title><addtitle>Sci. China Earth Sci</addtitle><description>Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph. Based on adopting the graph pattern of general knowledge representation, the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge, and integrates geoscience knowledge elements, such as map, text, and number, to establish an all-domain geoscience knowledge representation model. A federated, crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here, which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists. We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis, which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph. A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis, but also advance the construction of the high-precision geological time scale driven by big data, the compilation of intelligent maps driven by rules and data, and the geoscience knowledge evolution and reasoning analysis, among others. It will further expand the new directions of geoscience research driven by both data and knowledge, break new ground where geoscience, information science, and data science converge, realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.</description><subject>Analysis</subject><subject>Big Data</subject><subject>Collaboration</subject><subject>Construction</subject><subject>Data</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Earth and Environmental Science</subject><subject>Earth science</subject><subject>Earth Sciences</subject><subject>Geological mapping</subject><subject>Geological time</subject><subject>Graphical representations</subject><subject>Intelligence</subject><subject>Knowledge representation</subject><subject>Research Paper</subject><issn>1674-7313</issn><issn>1869-1897</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kEtLAzEQx4MoWGo_gLeA59RMHpvsUYrWQsGLnkM2j-3WuluTLeK3N2UFT85lBub_gB9Ct0CXQKm6zwCCU0IZJbWSlIgLNANd1QR0rS7LXSlBFAd-jRY572kZXj5MzdByHYbsutC7gN_74esQfBtwm-xxh7sej7uAm67F3o4Wh2Rv0FW0hxwWv3uO3p4eX1fPZPuy3qwetsQxDoJUVnLlVOVp5DrWmgmpG5DRa9E4ISPUstGN5spzVUUXOA0WtJaVol565_kc3U25xzR8nkIezX44pb5UGiZFrblmjBUVTCqXhpxTiOaYug-bvg1QcyZjJjKmkDFnMkYUD5s8uWj7NqS_5P9NP9f8Y0U</recordid><startdate>20210701</startdate><enddate>20210701</enddate><creator>Zhou, Chenghu</creator><creator>Wang, Hua</creator><creator>Wang, Chengshan</creator><creator>Hou, Zengqian</creator><creator>Zheng, Zhiming</creator><creator>Shen, Shuzhong</creator><creator>Cheng, Qiuming</creator><creator>Feng, Zhiqiang</creator><creator>Wang, Xinbing</creator><creator>Lv, Hairong</creator><creator>Fan, Junxuan</creator><creator>Hu, Xiumian</creator><creator>Hou, Mingcai</creator><creator>Zhu, Yunqiang</creator><general>Science China Press</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope></search><sort><creationdate>20210701</creationdate><title>Geoscience knowledge graph in the big data era</title><author>Zhou, Chenghu ; Wang, Hua ; Wang, Chengshan ; Hou, Zengqian ; Zheng, Zhiming ; Shen, Shuzhong ; Cheng, Qiuming ; Feng, Zhiqiang ; Wang, Xinbing ; Lv, Hairong ; Fan, Junxuan ; Hu, Xiumian ; Hou, Mingcai ; Zhu, Yunqiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2314-6a537c76d0f38f982458b15fd84bc45f195b8b837d376fce30ea1885670d5dcd3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Analysis</topic><topic>Big Data</topic><topic>Collaboration</topic><topic>Construction</topic><topic>Data</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Earth and Environmental Science</topic><topic>Earth science</topic><topic>Earth Sciences</topic><topic>Geological mapping</topic><topic>Geological time</topic><topic>Graphical representations</topic><topic>Intelligence</topic><topic>Knowledge representation</topic><topic>Research Paper</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Chenghu</creatorcontrib><creatorcontrib>Wang, Hua</creatorcontrib><creatorcontrib>Wang, Chengshan</creatorcontrib><creatorcontrib>Hou, Zengqian</creatorcontrib><creatorcontrib>Zheng, Zhiming</creatorcontrib><creatorcontrib>Shen, Shuzhong</creatorcontrib><creatorcontrib>Cheng, Qiuming</creatorcontrib><creatorcontrib>Feng, Zhiqiang</creatorcontrib><creatorcontrib>Wang, Xinbing</creatorcontrib><creatorcontrib>Lv, Hairong</creatorcontrib><creatorcontrib>Fan, Junxuan</creatorcontrib><creatorcontrib>Hu, Xiumian</creatorcontrib><creatorcontrib>Hou, Mingcai</creatorcontrib><creatorcontrib>Zhu, Yunqiang</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>Science China. Earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Chenghu</au><au>Wang, Hua</au><au>Wang, Chengshan</au><au>Hou, Zengqian</au><au>Zheng, Zhiming</au><au>Shen, Shuzhong</au><au>Cheng, Qiuming</au><au>Feng, Zhiqiang</au><au>Wang, Xinbing</au><au>Lv, Hairong</au><au>Fan, Junxuan</au><au>Hu, Xiumian</au><au>Hou, Mingcai</au><au>Zhu, Yunqiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Geoscience knowledge graph in the big data era</atitle><jtitle>Science China. Earth sciences</jtitle><stitle>Sci. China Earth Sci</stitle><date>2021-07-01</date><risdate>2021</risdate><volume>64</volume><issue>7</issue><spage>1105</spage><epage>1114</epage><pages>1105-1114</pages><issn>1674-7313</issn><eissn>1869-1897</eissn><abstract>Since the beginning of the 21st century, the geoscience research has been entering a significant transitional period with the establishment of a new knowledge system as the core and with the drive of big data as the means. It is a revolutionary leap in the research of geoscience knowledge discovery from the traditional encyclopedic discipline knowledge system to the computer-understandable and operable knowledge graph. Based on adopting the graph pattern of general knowledge representation, the geoscience knowledge graph expands the unique spatiotemporal features to the Geoscience knowledge, and integrates geoscience knowledge elements, such as map, text, and number, to establish an all-domain geoscience knowledge representation model. A federated, crowd intelligence-based collaborative method of constructing the geoscience knowledge graph is developed here, which realizes the construction of high-quality professional knowledge graph in collaboration with global geo-scientists. We also develop a method for constructing a dynamic knowledge graph of multi-modal geoscience data based on in-depth text analysis, which extracts geoscience knowledge from massive geoscience literature to construct the latest and most complete dynamic geoscience knowledge graph. A comprehensive and systematic geoscience knowledge graph can not only deepen the existing geoscience big data analysis, but also advance the construction of the high-precision geological time scale driven by big data, the compilation of intelligent maps driven by rules and data, and the geoscience knowledge evolution and reasoning analysis, among others. It will further expand the new directions of geoscience research driven by both data and knowledge, break new ground where geoscience, information science, and data science converge, realize the original innovation of the geoscience research and achieve major theoretical breakthroughs in the spatiotemporal big data research.</abstract><cop>Beijing</cop><pub>Science China Press</pub><doi>10.1007/s11430-020-9750-4</doi><tpages>10</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1674-7313 |
ispartof | Science China. Earth sciences, 2021-07, Vol.64 (7), p.1105-1114 |
issn | 1674-7313 1869-1897 |
language | eng |
recordid | cdi_proquest_journals_2549838222 |
source | SpringerNature Journals; Alma/SFX Local Collection |
subjects | Analysis Big Data Collaboration Construction Data Data analysis Data mining Earth and Environmental Science Earth science Earth Sciences Geological mapping Geological time Graphical representations Intelligence Knowledge representation Research Paper |
title | Geoscience knowledge graph in the big data era |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T21%3A11%3A47IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Geoscience%20knowledge%20graph%20in%20the%20big%20data%20era&rft.jtitle=Science%20China.%20Earth%20sciences&rft.au=Zhou,%20Chenghu&rft.date=2021-07-01&rft.volume=64&rft.issue=7&rft.spage=1105&rft.epage=1114&rft.pages=1105-1114&rft.issn=1674-7313&rft.eissn=1869-1897&rft_id=info:doi/10.1007/s11430-020-9750-4&rft_dat=%3Cproquest_cross%3E2549838222%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2549838222&rft_id=info:pmid/&rfr_iscdi=true |