大规模地球科学知识图谱构建与共享应用框架研究与实践

doi:10.12082/dqxxkx.2023.210696

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (6): 1215-1227.doi: 10.12082/dqxxkx.2023.210696

• 专刊：地理时空知识图谱理论方法与应用 • 上一篇下一篇

大规模地球科学知识图谱构建与共享应用框架研究与实践

诸云强¹^,²^,⁷(), 孙凯¹^,^*(), 胡修棉³, 闾海荣⁴^,⁵, 王新兵⁶, 杨杰¹, 王曙¹, 李威蓉¹^,⁷, 宋佳¹^,², 苏娜¹, 牟兴林⁸

1.中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京 100101
2.江苏省地理信息协同创新中心，南京 210023
3.南京大学地球科学与工程学院，南京 210023
4.清华大学自动化系，北京 100084
5.福州数据技术研究院，福州 350207
6.上海交通大学电子信息与电气工程学院上海 200240
7.中国科学院大学，北京 100049
8.自然资源部国土卫星遥感应用中心，北京 100048

收稿日期:2021-11-01 修回日期:2022-01-29 出版日期:2023-06-25 发布日期:2023-06-02
通讯作者: *孙凯（1990— ），男，山西长治人，博士后，研究方向是地学知识图谱构建及应用。E-mail: sunk@lreis.ac.cn
作者简介:诸云强（1977— ），男，江西广丰人，博士，研究员，研究方向为分布式数据共享关键技术、地理空间数据本体与应用、地学知识图谱及应用、资源环境信息系统。E-mail: zhuyq@lreis.ac.cn
基金资助:
国家自然科学基金项目(42050101);国家自然科学基金项目(41771430);国家自然科学基金项目(41631177);中国科学院战略性先导科技专项(XDA23100100)

Research and Practice on the Framework for the Construction, Sharing, and Application of Large-scale Geoscience Knowledge Graphs

ZHU Yunqiang¹^,²^,⁷(), SUN Kai¹^,^*(), HU Xiumian³, LV Hairong⁴^,⁵, WANG Xinbing⁶, YANG Jie¹, WANG Shu¹, LI Weirong¹^,⁷, SONG Jia¹^,², SU Na¹, MU Xinglin⁸

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3. School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
4. Department of Automation, Tsinghua University, Beijing 100084, China
5. Fuzhou Institute for Data Technology, Fuzhou 350207, China
6. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
7. University of Chinese Academy of Sciences, Beijing 100049, China
8. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China

Received:2021-11-01 Revised:2022-01-29 Online:2023-06-25 Published:2023-06-02
Contact: *SUN Kai, E-mail: sunk@lreis.ac.cn
Supported by:
National Natural Science Foundation of China(42050101);National Natural Science Foundation of China(41771430);National Natural Science Foundation of China(41631177);Strategic Priority Research Program of the Chinese Academy of Sciences(XDA23100100)

摘要/Abstract

摘要：

地球科学（以下简称地学）知识图谱具有强大的知识表示和语义推理能力，已成为地学大数据和地学人工智能发展必要的基础设施。然而，目前的地学知识图谱研究主要面向实验场景，缺乏面向实际应用的大规模地学知识图谱构建方法和共享应用框架研究，导致尚未真正在地学领域现实应用中得到使用。为此，本文面向地学大数据和人工智能研究与应用对地学知识图谱的迫切需求，首先研究了大规模地学知识图谱的构建技术，在此基础上，提出一种覆盖地学知识图谱构建、共享和应用全生命周期的总体框架。然后，以“深时数字地球（DDE）”国际大科学计划为例，开展了面向实际应用的知识图谱平台研发实践。最后，利用该平台，构建了DDE大规模地学知识图谱，开展了知识图谱开放共享，有效实现了知识图谱应用，证明本框架可有效支撑大规模地学知识图谱的构建与共享应用。本文对于地学知识图谱现实应用价值的实现具有重要的促进作用。

关键词: 地学知识图谱, 地学本体, 知识挖掘, 知识表达, 知识共享, 知识服务, 知识应用

Abstract:

Geoscience Knowledge Graph (GKG) has strong capabilities of knowledge representation and semantic reasoning, thereby becoming a required infrastructure for the development of geoscience big data and geoscience artificial intelligence. However, existing studies on GKG were mainly conducted under the experimental scenarios. Because of a lack of research on the general framework of construction methods, sharing, and application of large-scale GKG for practical applications, it has not been used in practical applications in the geoscience field. For this reason, towards the needs of research and applications of geoscience big data and artificial intelligence for GKG, this paper first studied the construction techniques of large-scale GKG. Then, a general framework for covering the lifecycle of GKG including its construction, sharing, and application was proposed. Taking the big science program “Deep-Time Digital Earth (DDE)” as an example, the practice of developing GKG platform towards the practical application of DDE was carried out. Using this platform, this paper realized the construction of DDE large-scale GKG, the open sharing and application of built GKG, proving that the proposed framework can effectively support the construction, sharing, and application of large-scale GKG. This paper plays an important role in promoting the realization of the practical application value of GKG.

Key words: geoscience knowledge graph, geoscience ontology, knowledge mining, knowledge representation, knowledge sharing, knowledge service, knowledge application

诸云强, 孙凯, 胡修棉, 闾海荣, 王新兵, 杨杰, 王曙, 李威蓉, 宋佳, 苏娜, 牟兴林. 大规模地球科学知识图谱构建与共享应用框架研究与实践[J]. 地球信息科学学报, 2023, 25(6): 1215-1227.DOI:10.12082/dqxxkx.2023.210696

ZHU Yunqiang, SUN Kai, HU Xiumian, LV Hairong, WANG Xinbing, YANG Jie, WANG Shu, LI Weirong, SONG Jia, SU Na, MU Xinglin. Research and Practice on the Framework for the Construction, Sharing, and Application of Large-scale Geoscience Knowledge Graphs[J]. Journal of Geo-information Science, 2023, 25(6): 1215-1227.DOI:10.12082/dqxxkx.2023.210696

图/表 6

图1

图2

图3

图4

图5

图6

参考文献 59

[1]	孙鸿烈. 地学大辞典[M]. 北京: 科学出版社, 2017.
	[ Sun H L. Dictionary of geoscience[M]. Beijing: Science Press, 2017. ]
[2]	郭华东, 王力哲, 陈方, 等. 科学大数据与数字地球[J]. 科学通报, 2014, 59(12):1047-1054.
	[ Guo H D, Wang L Z, Chen F, et al. Scientific big data and digital Earth[J]. Chinese Science Bulletin. 2014, 59(12):1047-1054. ] DOI:10.1360/972013-1054 doi: 10.1360/972013-1054
[3]	Miller H J, Goodchild M F. Data-driven geography[J]. GeoJournal, 2015, 80(4):449-461. DOI:10.1007/s10708-0 14-9602-6 doi: 10.1007/s10708-014-9602-6
[4]	周成虎, 王华, 王成善, 等. 大数据时代的地学知识图谱研究[J]. 中国科学:地球科学, 2021, 51(7):1070-1079.
	[ Zhou C H, Wang H, Wang C S, et al. Prospects for the research on geoscience knowledge graph in the big data era[J]. Science China Earth Sciences, 2021, 51(7):1070-1079. ] DOI:10.1360/SSTe-2020-0337 doi: 10.1360/SSTe-2020-0337
[5]	林珲, 游兰, 胡传博, 等. 时空大数据时代的地理知识工程展望[J]. 武汉大学学报·信息科学版, 2018, 43(12):2205-2211.
	[ Lin H, You L, Hu C B, et al. Prospect of geo-knowledge engineering in the era of spatio-temporal big data[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):452-458. ] DOI:10.13203/j.whugis20180318 doi: 10.13203/j.whugis20180318
[6]	Hey A J, Tansley S, Tolle K M. The fourth paradigm: Data-intensive scientific discovery[M]. WA: Microsoft research Redmond, 2009.
[7]	Zhu Y Q, Zhu A-X, Feng M, et al. A similarity-based automatic data recommendation approach for geographic models[J]. International Journal of Geographical Information Science, 2017, 31(7):1403-1424. DOI:10.1080/13658816.2017.1300805 doi: 10.1080/13658816.2017.1300805
[8]	Zhu Y Q, Yang J. Automatic data matching for geospatial models: a new paradigm for geospatial data and models sharing[J]. Annals of GIS, 2019, 25(4):283-298. DOI:10.1080/19475683.2019.1670735 doi: 10.1080/19475683.2019.1670735
[9]	黄恒琪, 于娟, 廖晓, 等. 知识图谱研究综述[J]. 计算机系统应用, 2019, 28(6):1-12.
	[ Huang H Q, Yu J, Liao X, et al. Review on Knowledge Graphs[J]. Computer Systems and Applications, 2019, 28(6):1-12. ] DOI:10.15888/j.cnki.csa.006915 doi: 10.15888/j.cnki.csa.006915
[10]	Lample G, Ballesteros M, Subramanian S, et al. Neural architectures for named entity recognition[C]. Proceedings of NAACL, San Diego, USA, 2016. DOI:10.18653/v1/N16-1030 doi: 10.18653/v1/N16-1030
[11]	Zhang Z Z, Sun L, Han X P. A joint model for entity set expansion and attribute extraction from web search queries[C]. Proceedings of the AAAI Conference on Artificial Intelligence, Phoenix, USA. 2016. DOI:10.5555/3016100.3016336 doi: 10.5555/3016100.3016336
[12]	Pawar S, Palshikar G K, Bhattacharyya P. Relation extraction: A survey[preprint]. 2017-12-14.
[13]	Zheng S C, Hao Y X, Lu D Y, et al. Joint entity and relation extraction based on a hybrid neural network[J]. Neurocomputing, 2017, 257:59-66. DOI:10.1016/j.neucom.2016.12.075 doi: 10.1016/j.neucom.2016.12.075
[14]	Sun Z Q, Hu W, Zhang Q H, et al. Bootstrapping entity alignment with knowledge graph embedding[C]. Proceedings of the IJCAI, Stockholm, Sweden, 2018. DOI:10.24963/ijcai.2018/611 doi: 10.24963/ijcai.2018/611
[15]	Wang Q, Mao Z D, Wang B, et al. Knowledge graph embedding: A survey of approaches and applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(12):2724-2743. DOI:10.1109/TKDE.2017.2754499 doi: 10.1109/TKDE.2017.2754499
[16]	Lin Y K, Liu Z Y, Sun M S, et al. Learning entity and relation embeddings for knowledge graph completion[C]. Proceedings of the AAAI conference on artificial intelligence, Austin, USA, 2015. DOI:10.5555/2886521.2886624 doi: 10.5555/2886521.2886624
[17]	Ji S X, Pan S R, Cambria E, et al. A survey on knowledge graphs: Representation, acquisition, and applications[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021:1-21. DOI:10.1109/TNNLS.2021.3070843 doi: 10.1109/TNNLS.2021.3070843
[18]	Mendes P N, Jakob M, Bizer C. DBpedia: A multilingual cross-domain knowledge base[J]. Speech Communication, 2012:1813-1817.
[19]	Tanon T P, Weikum G, Suchanek F. Yago 4: A reason-able knowledge base[C]. Proceedings of the European Semantic Web Conference, Heraklion, Greece, 2020. DOI:10.1007/978-3-030-49461-2_34 doi: 10.1007/978-3-030-49461-2_34
[20]	VrandeČiĆ D, Krötzsch M. Wikidata: A free collaborative knowledgebase[J]. Communications of the ACM, 2014, 57(10):78-85. DOI:10.1145/2629489 doi: 10.1145/2629489
[21]	徐增林, 盛泳潘, 贺丽荣, 等. 知识图谱技术综述[J]. 电子科技大学学报, 2016, 45(4):589-606.
	[ Xu Z L, Sheng Y P, He L R, et al. Review on knowledge graph techniques[J]. Journal of University of Electronic Science and Technology of China, 2016, 45(4):589-606. ] DOI:10.3969/j.issn.1001-0548.2016.04.012 doi: 10.3969/j.issn.1001-0548.2016.04.012
[22]	Shao B L, Li X J, Bian G Q. A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph[J]. Expert Systems with Applications, 2021,165,113764. DOI:10.1016/j.eswa.2020.113764 doi: 10.1016/j.eswa.2020.113764
[23]	Chen X J, Jia S B, Xiang Y. A review: Knowledge reasoning over knowledge graph[J]. Expert Systems with Applications, 2020,141,112948. DOI: 10.1016/j.eswa.2019.112948 doi: 10.1016/j.eswa.2019.112948
[24]	Zhang Y Y, Dai H J, Kozareva Z, et al. Variational reasoning for question answering with knowledge graph[C]. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, USA, 2018.
[25]	齐浩, 董少春, 张丽丽, 等. 地球科学知识图谱的构建与展望[J]. 高校地质学报, 2020, 26(1):2-10. doi: 10.16108/j.issn1006-7493.2019099
	[ Qi H, Dong S C, Zhang L L, et al. Construction of earth science knowledge Graph and Its Future Perspectives[J]. Geological Journal of China Universities, 2020, 26(1):2-10. ] DOI:10.16108/j.issn1006-7493.2019099 doi: 10.16108/j.issn1006-7493.2019099
[26]	Zhou C H, Wang H, Wang C S, et al. Prospects for the research on geoscience knowledge graph in the Big Data Era[J]. Science China Earth Sciences, 2021, 64:1105-1114. DOI: 10.1007/s11430-020-9750-4 doi: 10.1007/s11430-020-9750-4
[27]	高松. 地理空间人工智能的近期研究总结与思考[J]. 武汉大学学报·信息科学版, 2020, 45(12):1865-1874.
	[ Gao S. A review of recent researches and reflections on geospatial artificial intelligence[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12):1865-1874. ] DOI:10.13203/j.whugis20200597 doi: 10.13203/j.whugis20200597
[28]	张雪英, 张春菊, 吴明光, 等. 顾及时空特征的地理知识图谱构建方法[J]. 中国科学:信息科学, 2020, 50(7):1019-1032.
	[ Zhang X Y, Zhang C J, Wu M G, et al. Spatio-temporal features based geographical knowledge graph construction[J]. SCIENTIA SINICA Informationis, 2020, 50(7):1019-1032. ] DOI:10.1360/SSI-2019-0269 doi: 10.1360/SSI-2019-0269
[29]	Wang S, Zhang X Y, Ye P, et al. Geographic Knowledge Graph (GeoKG): A formalized geographic knowledge representation[J]. ISPRS International Journal of Geo-information, 2019, 8(4):184-207. DOI:10.3390/ijgi8040184 doi: 10.3390/ijgi8040184
[30]	陆锋, 余丽, 仇培元. 论地理知识图谱[J]. 地球信息科学学报, 2017, 19(6):723-734. doi: 10.3724/SP.J.1047.2017.00723
	[ Lu F, Yu L, Qiu P Y. On geographic knowledge graph[J]. Journal of Geo-information Science, 2017, 19(6):723-734. ] DOI:10.3724/SP.J.1047.2017.00723 doi: 10.3724/SP.J.1047.2017.00723
[31]	王志华, 杨晓梅, 周成虎. 面向遥感大数据的地学知识图谱构想[J]. 地球信息科学学报, 2021, 23(1):16-28. doi: 10.12082/dqxxkx.2021.200632
	[ Wang Z H, Yang X M, Zhou C H. Geographic knowledge graph for remote sensing big data[J]. Journal of Geo-information Science, 2021, 23(1):16-28. ] DOI:10.12082/dqxxkx.2021.200632 doi: 10.12082/dqxxkx.2021.200632
[32]	Lozano M G, Schreiber J, Brynielsson J. Tracking geographical locations using a geo-aware topic model for analyzing social media data[J]. Decision Support Systems, 2017, 99:18-29. DOI:10.1016/j.dss.2017.05.006 doi: 10.1016/j.dss.2017.05.006
[33]	Wang J M, Hu Y J, Joseph K. NeuroTPR: A neuro-net toponym recognition model for extracting locations from social media messages[J]. Transactions in GIS, 2020, 24(3):719-735. DOI:10.1111/tgis.12627 doi: 10.1111/tgis.12627
[34]	余丽, 陆锋, 刘希亮. 开放式地理实体关系抽取的Bootstrapping方法[J]. 测绘学报, 2016, 45(5):616-622.
	[ Yu L, Lu F, Liu X L. A bootstrapping based approach for open geo-entity relation extraction[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(5):616-622. ] DOI:10.11947/j.AGCS.2016.20150181 doi: 10.11947/j.AGCS.2016.20150181
[35]	余丽, 陆锋, 刘希亮, 等. 稀疏地理实体关系的关键词提取方法[J]. 地球信息科学学报, 2016, 18(11):1465-1475. doi: 10.3724/SP.J.1047.2016.01465
	[ Yu L, Lu F, Liu X L, et al. A method of context enhanced keyword extraction for sparse geo-entity relation[J]. Journal of Geo-information Science, 2016, 18(11):1465-1475. ] DOI:10.3724/SP.J.1047.2016.01465 doi: 10.3724/SP.J.1047.2016.01465
[36]	Trisedya B D, Qi J Z, Zhang R. Entity alignment between knowledge graphs using attribute embeddings[C]. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, USA, 2019. DOI:10.1609/aaai.v33i01.3301297 doi: 10.1609/aaai.v33i01.3301297
[37]	Yu L, Qiu P Y, Liu X L, et al. A holistic approach to aligning geospatial data with multidimensional similarity measuring[J]. International journal of digital earth, 2018, 11(8):845-862. DOI:10.1080/17538947.2017.1359688 doi: 10.1080/17538947.2017.1359688
[38]	Mai G C, Janowicz K, Yan B, et al. Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells[C]. Proceedings of the International Conference on Learning Representations. Addis Ababa, Ethiopia, 2020.
[39]	Mai G C, Janowicz K, Cai L, et al. SE-KGE: A location-aware Knowledge Graph Embedding model for Geographic question answering and spatial semantic lifting[J]. Transactions in GIS, 2020, 24(3):623-655. DOI:10.1111/tgis.12629 doi: 10.1111/tgis.12629
[40]	Qiu P Y, Gao J L, Yu L, et al. Knowledge embedding with geospatial distance restriction for geographic knowledge graph completion[J]. ISPRS International Journal of Geo-information, 2019, 8(6):254-276. DOI:10.10.3390/ijgi8060254 doi: 10.10.3390/ijgi8060254
[41]	Ma X G. Knowledge graph construction and application in geosciences: A review[preprint]. 2021-04-30. DOI:10.31223/x5z898 doi: 10.31223/x5z898
[42]	陈晓慧, 刘俊楠, 徐立, 等. COVID-19病例活动知识图谱构建——以郑州市为例[J]. 武汉大学学报·信息科学版, 2020, 45(6):816-825.
	[ Chen X H, Liu J N, Xu L, et al. Construction of the COVID-19 epidemic cases activity knowledge graph: A case study of Zhengzhou City[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6):816-825. ] DOI:10.13203/j.whugis20200201 doi: 10.13203/j.whugis20200201
[43]	蒋秉川, 游雄, 李科, 等. 利用地理知识图谱的COVID-19疫情态势交互式可视分析[J]. 武汉大学学报·信息科学版, 2020, 45(6):836-845.
	[ Jiang B C, You X, Li K, et al. Interactive visual analysis of COVID-19 epidemic situation using geographic knowledge graph[J]. Geomatics and Information Science of Wuhan University, 2020, 45(6):836-845. ] DOI:10.13203/j.whugis20200153 doi: 10.13203/j.whugis20200153
[44]	杜志强, 李钰, 张叶廷, 等. 自然灾害应急知识图谱构建方法研究[J]. 武汉大学学报·信息科学版, 2020, 45(9):1344-1355.
	[ Du Z Q, Li Y, Zhang Y T, et al. Knowledge graph construction method on natural disaster emergency[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9):1344-1355. ] DOI:10.13203/j.whugis20200047 doi: 10.13203/j.whugis20200047
[45]	陶坤旺, 赵阳阳, 朱鹏, 等. 面向一体化综合减灾的知识图谱构建方法[J]. 武汉大学学报·信息科学版, 2020, 45(8):1296-1302.
	[ Tao K W, Zhao Y Y, Zhu P, et al. Knowledge graph construction for integrated disaster reduction[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8):1296-1302. ] DOI:10.13203/j.whugis20200125 doi: 10.13203/j.whugis20200125
[46]	赵红伟, 诸云强, 侯志伟, 等. 地理空间元数据关联网络的构建[J]. 地理科学, 2016, 36(8):1180-1189. doi: 10.13249/j.cnki.sgs.2016.08.008
	[ Zhao H W, Zhu Y Q, Hou Z W, et al. Construction of geospatial metadata association network[J]. Scientia Geographica Sinica, 2016, 36(8):1180-1189. ] DOI:10.13249/j.cnki.sgs.2016.08.008 doi: 10.13249/j.cnki.sgs.2016.08.008
[47]	Marc Wick, GeoNames Ontology[EB/OL]. www.geonames.org/ontology/documentation.html, 2021-07-25.
[48]	Ballatore A, Bertolotto M, Wilson D C. Geographic knowledge extraction and semantic similarity in OpenStreetMap[J]. Knowledge and Information Systems, 2013, 37(1):61-81. DOI: 10.1007/s10115-012-0571-0 doi: 10.1007/s10115-012-0571-0
[49]	蒋秉川, 万刚, 许剑, 等. 多源异构数据的大规模地理知识图谱构建[J]. 测绘学报, 2018, 47(8):1051-1061.
	[ Jiang B C, Wan G, Xu J, et al. Geographic Knowledge Graph Building Extracted from Multi-sourced Heterogeneous Data[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(8):1051-1061. ] DOI: 10.11947/j.AGCS.2018.20180113 doi: 10.11947/j.AGCS.2018.20180113
[50]	张洪岩, 周成虎, 闾国年, 等. 试论地学信息图谱思想的内涵与传承[J]. 地球信息科学学报, 2020, 22(4):653-661. doi: 10.12082/dqxxkx.2020.200167
	[ Zhang H Y, Zhou C H, Lv G N, et al. The connotation and inheritance of Geo-information Tupu[J]. Journal of Geo-information Science, 2020, 22(4):653-661. ] DOI:10.12082/dqxxkx.2020.200167 doi: 10.12082/dqxxkx.2020.200167
[51]	Gruber T R. Toward principles for the design of ontologies used for knowledge sharing?[J]. International journal of human-computer studies, 1995, 43(5-6):907-928. DOI: 10.1006/ijhc.1995.1081 doi: 10.1006/ijhc.1995.1081
[52]	Noy N F, Mcguinness D L. Ontology development 101: A guide to creating your first ontology[R]. California, 2001.
[53]	Sun K, Zhu Y Q, Song J. Progress and challenges on entity alignment of geographic knowledge bases[J]. ISPRS International Journal of Geo-Information, 2019, 8(2):77-101. DOI:10.3390/ijgi8020077 doi: 10.3390/ijgi8020077
[54]	Acheson E, Volpi M, Purves R S. Machine learning for cross-gazetteer matching of natural features[J]. International Journal of Geographical Information Science, 2020, 34(4):708-734. DOI:10.1080/13658816.2019.1599123 doi: 10.1080/13658816.2019.1599123
[55]	Qiu Q J, Xie Z, Wu L, et al. BiLSTM-CRF for geological named entity recognition from the geoscience literature[J]. Earth Science Informatics, 2019, 12(4):565-579. DOI: 10.1007/s12145-019-00390-3 doi: 10.1007/s12145-019-00390-3
[56]	Qiu Q J, Xie Z, Wu L, et al. Geoscience keyphrase extraction algorithm using enhanced word embedding[J]. Expert Systems with Applications, 2019, 125:157-169. DOI: 10.1016/J.ESWA.2019.02.001 doi: 10.1016/j.eswa.2019.02.001
[57]	Wang C B, Ma X G, Chen J G, et al. Information extraction and knowledge graph construction from geoscience literature[J]. Computers & geosciences, 2018, 112:112-120. DOI:10.1016/j.cageo.2017.12.007 doi: 10.1016/j.cageo.2017.12.007
[58]	Stephenson M H, Cheng Q M, Wang C S, et al. Progress towards the establishment of the IUGS Deep-time Digital Earth (DDE) programme[J]. Episodes, 2020, 43(4):1057-1062. DOI:10.18814/epiiugs/2020/020057 doi: 10.18814/epiiugs/2020/020057
[59]	Wang C S, Hazen R M, Cheng Q M, et al. The deep-time digital earth program: Data-driven discovery in geosciences[J]. National Science Review, 2021, 8(9):nwab 027. DOI:10.1093/nsr/nwab027 doi: 10.1093/nsr/nwab027

大规模地球科学知识图谱构建与共享应用框架研究与实践

Research and Practice on the Framework for the Construction, Sharing, and Application of Large-scale Geoscience Knowledge Graphs

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 59

相关文章 6

Metrics

本文评价

推荐阅读 0

[1]	王益鹏, 张雪英, 党玉龙, 叶鹏. 顾及时空过程的台风灾害事件知识图谱表示方法[J]. 地球信息科学学报, 2023, 25(6): 1228-1239.
[2]	陆锋, 诸云强, 张雪英. 时空知识图谱研究进展与展望[J]. 地球信息科学学报, 2023, 25(6): 1091-1105.
[3]	尹文萍, 高宸, 樊辉, 谢菲, 张鑫. 一种融合文本中地理位置和土地利用/覆被信息的野生动物活动细粒度定位方法[J]. 地球信息科学学报, 2022, 24(7): 1363-1374.
[4]	王志华, 杨晓梅, 周成虎. 面向遥感大数据的地学知识图谱构想[J]. 地球信息科学学报, 2021, 23(1): 16-28.
[5]	刘馨蕊, 张伟峰. 基于多维多值概念格的矿山生产地学本体构建研究[J]. 地球信息科学学报, 2018, 20(2): 176-185.
[6]	许珺, 裴韬, 姚永慧. 地学知识图谱的定义、内涵和表达方式的探讨[J]. 地球信息科学学报, 2010, 12(4): 496-502,509.