时空知识图谱研究进展与展望

doi:10.12082/dqxxkx.2023.230154

Abstract

Abstract:

The continuous generalization of geographic information poses a huge challenge to the classic geographic information analysis modes. Networked knowledge services will gradually become a new mode for geographic information applications, facilitating to transform the form of geographic computing into social computing. Geographic knowledge services need to connect people, institutions, natural environments, geographical entities, geographical units and social events, so as to promote knowledge assisted data intelligence and computational intelligence. Facing the urgent need for spatiotemporal knowledge acquisition, formal expression and analysis, this paper firstly introduces the concepts and characteristics of spatiotemporal knowledge graph. The spatiotemporal knowledge graph is a directed graph composed of geographic spatiotemporal distribution or geo-locational metaphors of knowledge that is a knowledge graph centered on spatiotemporal distribution characteristics. Secondly we proposes a research framework for spatiotemporal knowledge graph. The framework includes various levels from multimodal spatiotemporal big data to spatiotemporal knowledge services that contain ubiquitous spatiotemporal big data layer, spatiotemporal knowledge acquisition technique layer, spatiotemporal knowledge management layer, spatiotemporal knowledge graph layer, software/tools layer, and industrial application layer. Thirdly this paper introduces relevant research progress from text implied geographic information retrieval, heterogeneous geographic semantic web alignment, spatiotemporal knowledge formalization and representation learning. Combined with application practice, we then enumerate the construction and application approaches of domain oriented spatiotemporal knowledge graph. Finally, it discusses the key scientific issues and technical bottlenecks currently faced in the research of spatiotemporal knowledge graph. It is argued that in the era of large models, constructing explicit spatiotemporal knowledge graph and conducting knowledge reasoning to meet domain needs is still the only way for spatiotemporal knowledge services.

Key words: spatial-temporal knowledge, knowledge graph, GeoAI, knowledge modeling, spatial-temporal big data, information extraction, geo-semantic alignment, advances

LU Feng, ZHU Yunqiang, ZHANG Xueying. Spatiotemporal Knowledge Graph: Advances and Perspectives[J].Journal of Geo-information Science, 2023, 25(6): 1091-1105.DOI:10.12082/dqxxkx.2023.230154

Figures/Tables 2

References 65

[1]	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
[2]	Janowicz K, Gao S, McKenzie G, et al. GeoAI: Spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond[J]. International Journal of Geographical Information Science, 2020, 34(4):625-636. DOI:10.1080/13658816.2019.1684500 doi: 10.1080/13658816.2019.1684500
[3]	程学旗, 梅宏, 赵伟, 等. 数据科学与计算智能:内涵、范式与机遇[J]. 中国科学院院刊, 2020, 35(12):1470-1481.
	[ Mei H, Zhao W, et al. Data science and computing intelligence: Concept, paradigm and opportunities[J]. Bulletin of Chinese Academy of Sciences, 2020, 35(12):1470-1481. ] DOI:10.16418/j.issn.1000-3045.20201116005 doi: 10.16418/j.issn.1000-3045.20201116005
[4]	陈军, 刘万增, 武昊, 等. 基础地理知识服务的基本问题与研究方向[J]. 武汉大学学报·信息科学版, 2019, 44:38-47.
	[ Chen J, Liu W Z, Wu H, et al. Basic issues and research agenda of geospatial knowledge service[J]. Geomatics and Information Science of Wuhan University, 2019, 44:38-47. ] DOI:10.13203/j.wuugis20180441 doi: 10.13203/j.wuugis20180441
[5]	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
[6]	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]. Sci Sin Inform, 2020, 50(7):1019-1032. ] DOI:10.1360/SSI-2019-0269 doi: 10.1360/SSI-2019-0269
[7]	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
[8]	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, Wang 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
[9]	刘俊楠, 刘海砚, 陈晓慧, 等. 面向多源地理空间数据的知识图谱构建[J]. 地球信息科学学报, 2020, 22(7):1476-1486. doi: 10.12082/dqxxkx.2020.190565
	[ Liu J N, Liu H Y, Chen X H, et al. The Construction of knowledge graph towards multi-source geospatial data[J]. Journal of Geo-information Science, 2020, 22(7):1476-1486. ] DOI:10.12082/dqxxkx.2020.190565 doi: 10.12082/dqxxkx.2020.190565
[10]	张永军, 王飞, 李彦胜, 等. 遥感知识图谱创建及其典型场景应用技术[J]. 遥感学报, 2023, 27(2):249-266.
	[ Zhang Y J, Wang F, Li Y S, et al. Remote sensing knowledge graph construction and its application in typical scenarios[J]. National Remote Sensing Bulletin, 2023, 27(2):249-266. ] DOI:10.11834/jrs.20210469 doi: 10.11834/jrs.20210469
[11]	李彦胜, 张永军. 耦合知识图谱和深度学习的新一代遥感影像解译范式[J]. 武汉大学学报·信息科学版, 2022, 47(8):1176-1190.
	[ Li Y S, Zhang Y J. A new paradigm of remote sensing image interpretation by coupling knowledge graph and deep learning[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8):1176-1190. ] DOI:10.13203/j.whugis20210652 doi: 10.13203/j.whugis20210652
[12]	周成虎, 孙九林, 苏奋振, 等. 地理信息科学发展与技术应用[J], 地理学报, 2020, 75(12):2593-2609. doi: 10.11821/dlxb202012004
	[ Zhou C H, Sun J L, Su F Z, et al. Geographic information science development and technological application[J]. Acta Geographica Sinica, 2020, 75(12):2593-2609. ] DOI:10.11821/dlxb202012004 doi: 10.11821/dlxb202012004
[13]	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:DOI:10.1080/13658816.2017.1300805 doi: 10.1080/13658816.2017.1300805
[14]	Zhu Y Q, Zhu A X, Song J, et al. Multidimensional and quantitative interlinking approach for linked geospatial data[J]. International Journal of Digital Earth, 2017, 10(9):923-943. DOI:10.1080/17538947.2016.1266041 doi: 10.1080/17538947.2016.1266041
[15]	Zhu Y Q and 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
[16]	陈述彭, 岳天祥, 励惠国. 地学信息图谱研究及其应用[J]. 地理研究, 2000, 19(4):337-343. doi: 10.11821/yj2000040002
	[ Chen S P, Yue T X, Li H G. Studies on Geo-informatic Tupu and its application[J]. Geographical Research, 2000, 19(4):337-343. ] DOI: 10.11821/yj2000040002 doi: 10.11821/yj2000040002
[17]	陈述彭. 地学信息图谱探索研究[M]. 北京: 商务印书馆, 2001.
	[ Chen S P. Research on Geo-information Tupu[M]. Beijing: The Commercial Press, 2001. ]
[18]	陈述彭. 地学信息图谱的概念[M]// 陈述彭.地学的探索(第六卷):地球信息科学. 北京: 科学出版社, 2003:209-228.
	[ Chen S P. The concept of Geo-information Tupu[M]// Chen S P. Exploration of Geosciences (Volume VI):Earth Information Science. Beijing: Science Press, 2003:209-228. ]
[19]	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
[20]	张春菊, 张雪英, 王曙, 等. 中文文本的事件时空信息标注[J]. 中文信息学报, 2016, 30(3):213-222.
	[ Zhang C J, Zhang X Y, Wang S, et al. Annotation of spatial-temporal information of event in Chinese text[J]. Journal of Chinese Information Processing, 2016, 30(3):213-222. ]
[21]	王姬卜, 陆锋, 吴升, 等. 基于自动回标的地理实体关系语料库构建方法[J]. 地球信息科学学报, 2018, 20(7):871-879. doi: 10.12082/dqxxkx.2018.180032
	[ Wang J B, Lu F, Wu S, et al. Constructing the corpus of geographical entity relations based on automatic annotation[J]. Journal of Geo-information Science, 2018, 20(7):871-879. ] DOI:10.12082/dqxxkx.2018.180032 doi: 10.12082/dqxxkx.2018.180032
[22]	Gao S, Li L, Li W, et al. Constructing gazetteers from volunteered big geo-data based on Hadoop[J]. Computer, Environment and Urban Systems, 2017, 61:172-186. doi: 10.1016/j.compenvurbsys.2014.02.004
[23]	Hu Y. Geo-text data and data-driven geospatial semantics[J]. Geography Compass, 2018, 12(11):e12404
[24]	Ju Y, Adams B, Janowicz K, et al. Things and strings: Improving place name disambiguation from short texts by combining entity co-occurrence with topic modeling[C]. European Knowledge Acquisition Workshop, Bologna, Italy, 2016.
[25]	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
[26]	张雪英, 闾国年, 杜咪, 等. 大数据驱动的地名信息获取与应用[J]. 现代测绘, 2017, 40(2):1-5.
	[ Zhang X Y, Lv G N, Du M, et al. Acquisition and application on geographical names information based on large data driving[J]. Modern Surveying and Mapping, 2017, 40(2):1-5. ]
[27]	Santos R, Murrieta-Flores P, Calado P, et al. Toponym matching through deep neural networks[J]. International Journal of Geographical Information Science, 2018, 32(2):324-348. doi: 10.1080/13658816.2017.1390119
[28]	Wang S, Zhang X Y, Ye P, et al. Deep belief networks based toponym recognition for Chinese text[J]. ISPRS International Journal of Geo-information, 2018, 7(6):217. DOI:10.3390/ijgi7060217 doi: 10.3390/ijgi7060217
[29]	张春菊, 陈玉冰, 张雪英, 等. 基于交互式与迭代式学习的地名语料库智能构建方法[P]. 中国:ZL201911029961.4, 2023年1月18日.
	[ Zhang C J, Chen Y B, Zhang X Y, et al. An intelligent construction method for place name corpus based on interactive and iterative learning[P]. China:ZL201911029961.4, 2023-01-18. ]
[30]	Wang F, Li P F, Zhu Q M. A hybrid model of classification and generation for spatial relation extraction[C]. In Proceedings of the 29^th International Conference on Computational Linguistics, 2022:1915-1924.
[31]	Zhang L, Wang R Z, Zhou J B, et al. Joint intent detection and entity linking on spatial domain queries[C]. In Findings of the Association for Computational Linguistics: EMNLP 2020, 2020:4937-4947.
[32]	Yang T Y, Lan A S, Narasimhan K. Robust and interpretable grounding of spatial references with relation networks[R]. 2020, DOI:10.48550/arXiv.2005.00696 doi: 10.48550/arXiv.2005.00696
[33]	Song C, Lin Y F, Guo S N, et al. Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting[C]. In Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1):914-921. DOI:10.1609/aaai.v34i01.5438 doi: 10.1609/aaai.v34i01.5438
[34]	Nichols E, Botros F. SpRL-CWW: Spatial relation classification with independent multi-class models[C]. In Proceedings of the 9^th International Workshop on Semantic Evaluation (SemEval 2015), 2015:895-901. DOI:10.18653/v1/S15-2150 doi: 10.18653/v1/S15-2150
[35]	D'Souza J, Ng V. Sieve-based spatial relation extraction with expanding parse trees[C]. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 758-768, Lisbon, Portugal. Association for Computational Linguistics. DOI:10.18653/v1/D15-1087 doi: 10.18653/v1/D15-1087
[36]	Ramrakhiyani N, Palshikar G, Varma V. A simple neural approach to spatial role labelling[C]. In: Azzopardi L, Stein B, Fuhr N, et al. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science, vol 11438. Springer, Cham. DOI:10.1007/978-3-030-15719-7_13 doi: 10.1007/978-3-030-15719-7_13
[37]	Shin H J, Park J Y, Yuk D B, et al. BERT-based spatial information extraction[C]. In Proceedings of the Third International Workshop on Spatial Language Understanding, 2020:10-17. DOI:10.18653/v1/2020.splu-1.2 doi: 10.18653/v1/2020.splu-1.2
[38]	Qiu Q, Xie Z, Ma K, et al. Spatially oriented convolutional neural network for spatial relation extraction from natural language texts[J]. Transactions in GIS, 2022, 26(2):839-866. DOI:10.1111/tgis.12887 doi: 10.1111/tgis.12887
[39]	Yu L, Qiu P Y, Gao J L, et al. A knowledge-based filtering method for open relations among geo-entities[J]. ISPRS International Journal of Geo-information, 2019, 8(2):59. DOI:10.3390/ijgi8020059 doi: 10.3390/ijgi8020059
[40]	高嘉良, 余丽, 仇培元, 等. 基于通用知识库的地理实体开放关系过滤方法[J]. 地球信息科学学报, 2019, 21(9):1392-1401. doi: 10.12082/dqxxkx.2019.190005
	[ Gao J L, Yu L, Qiu P Y, et al. A knowledge-based method for filtering Geo-entity relations[J]. Journal of Geo-information Science, 2019, 21(9):1392-1401. ] DOI:10.12082/dqxxkx.2019.190005 doi: 10.12082/dqxxkx.2019.190005
[41]	仇培元, 张恒才, 余丽, 等. 微博客蕴含交通事件信息抽取的自动标注方法[J]. 中文信息学报, 2017, 31(2):107-116.
	[ Qiu P Y, Zhang H C, Yu L, et al. An automatic labeling method for extracting traffic event information from microblog messages[J]. Journal of Chinese Information Processing, 2017, 31(2):107-116. ]
[42]	黄宗财, 仇培元, 陆锋, 等. 基于联合主题特征的网络新闻文本蕴含环境污染事件检测[J]. 地球信息科学学报, 2019, 21(10):1510-1517. doi: 10.12082/dqxxkx.2019.190037
	[ Huang Z C, Chou PY, Lu F, et al. Detection of environmental pollution events in news corpora based on joint thematic features[J]. Journal of Geo-information Science, 2019, 21(10):1510-1517. ] DOI:10.12082/dqxxkx.2019.190037 doi: 10.12082/dqxxkx.2019.190037
[43]	黄宗财, 仇培元, 王海波, 等. 结合事件和语境特征的台风事件信息抽取方法[J]. 测绘科学技术学报, 2019. 36(2):209-214.
	[ Huang Z C, Qiu P Y, Wang H B, et al. Typhoon event information extraction method based on event and context characteristics[J]. Journal of Geomatics Science and Technology, 2019, 36(2):209-214. ] DOI:10.3969/j.issn.1673-6338.2019.02.018 doi: 10.3969/j.issn.1673-6338.2019.02.018
[44]	Ye P, Zhang X Y, Zhang C J, et al. Positioning localities for vague spatial location description: A supervaluation semantics approach[J]. ISPRS International Journal of Geo-information, 2022, 11(1):68. DOI:10.3390/ijgi11010068 doi: 10.3390/ijgi11010068
[45]	Wang X N, Du S H, Feng C C, et al. Interpreting the fuzzy semantics of natural-language spatial relation terms with the fuzzy random forest algorithm[J]. ISPRS International Journal of Geo-information, 2018, 7(2):58. DOI:10.3390/ijgi7020058 doi: 10.3390/ijgi7020058
[46]	王圣音, 刘瑜, 陈泽东, 等. 大众点评数据下的城市场所范围感知方法[J]. 测绘学报, 2018, 47(8):1105-1113.
	[ Wang S Y, Liu Y, Chen Z D, et al. Representing multiple urban places' footprints from Dianping.com Data[J]. Acta Geodaetica et Cartographica Sinica, 2018, 47(8):1105-1113. ] DOI:10.11947/j.AGCS.2018.20180110 doi: 10.11947/j.AGCS.2018.20180110
[47]	Liu K, Qiu P Y, Gao S, et al. Investigating urban metro stations as cognitive places in cities using points of interest[J]. Cities, 2020, 97:102561. DOI:10.1016/j.cities.2019.102561 doi: 10.1016/j.cities.2019.102561
[48]	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. DOI:10.3390/ijgi8020077 doi: 10.3390/ijgi8020077
[49]	Sun K, Hu Y J, Song J, et al. Aligning geographic entities from historical maps for building knowledge graphs[J]. International Journal of Geographical Information Science, 2021, 35(10):2078-2107. DOI:10.1080/13658816.2020.1845702 doi: 10.1080/13658816.2020.1845702
[50]	徐召华, 诸云强, 宋佳, 等. 基于词嵌入的地理知识库实体类别对齐方法研究[J]. 地球信息科学学报, 2021, 23(8):1372-1381. doi: 10.12082/dqxxkx.2021.200566
	[ Xu Z H, Zhu Y Q, Song J, et al. Word embedding-based method for entity category alignment of geographic knowledge base[J]. Journal of Geo-information Science, 2021, 23(8):1372-1381. ] DOI:10.12082/dqxxkx.2021.200566 doi: 10.12082/dqxxkx.2021.200566
[51]	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
[52]	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. DOI:10.3390/ijgi8040184 doi: 10.3390/ijgi8040184
[53]	Zhang X Y, Huang Y, Zhang C J, et al. Geoscience Knowledge Graph (GeoKG): Development, construction and challenges[J]. Transactions in GIS, 2022, 26(6):1-15. DOI:10.1111/tgis.12985 doi: 10.1111/tgis.12985
[54]	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. DOI:10.3390/ijgi8060254 doi: 10.3390/ijgi8060254
[55]	黄挚文. 台风灾害事件知识图谱构建方法研究[D]. 南京: 南京师范大学, 2021.
	[ Huang Z W. Research on the construction method of knowledge graph for the typhoon disaster events[D]. Nanjing: Nanjing Normal University, 2021. ]
[56]	王晓爽, 李吉东, 徐海红, 等. 顾及时空特征的大气污染执法事理图谱构建方法研究[J]. 地理与地理信息科学, 2022, 38(3):1-8.
	[ Li J D, Xu H H, et al. Logic graph construction of air pollution law enforcement event considering the spatiotemporal features[J]. Geography and Geo-information Science, 2022, 38(3):1-8. ] DOI:10.3969/j.issn.1672-0504.2022.03.001 doi: 10.3969/j.issn.1672-0504.2022.03.001
[57]	Wang X, Zhu Y Q, Zeng H Y, et al. Spatialized analysis of air pollution complaints in Beijing using the BERT + CRF model[J]. Atmosphere 2022, 13:1023. DOI:10.3390/atmos13071023 doi: 10.3390/atmos13071023
[58]	朱庆, 王所智, 丁雨淋, 等. 铁路隧道钻爆法施工智能管理的安全质量进度知识图谱构建方法[J]. 武汉大学学报·信息科学版, 2022, 47(8):1155-1164.
	[ Zhu Q, Wang S Z, Ding Y L, et al. A method of safety-quality-schedule knowledge graph for intelligent management of drilling and blasting construction of railway tunnels[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8):1155-1164. ] DOI:10.13203/j.whugis20210573 doi: 10.13203/j.whugis20210573
[59]	张永军, 程鑫, 李彦胜, 等. 利用知识图谱的国土资源数据管理与检索研究[J]. 武汉大学学报·信息科学版, 2022, 47(8):1165-1175.
	[ Zhang Y J, Cheng X, Li Y S, et al. Research on land and resources management and retrieval using knowledge graph[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8):1165-1175. ] DOI:10.13203/j.whugis20210714 doi: 10.13203/j.whugis20210714
[60]	高嘉良, 陆锋, 彭澎, 等. 基于网络文本迁移学习的旅游知识图谱构建[J]. 武汉大学学报·信息科学版, 2022, 47(8),1191-1200,1219.
	[ Gao J L, Lu F, Peng P, et al. Construction of tourism attraction knowledge graph based on web text and transfer learning[J]. Geomatics and Information Science of Wuhan University, 2022, 47(8):1191-1200,1219. ] DOI:10.13203/j.whugiS20220120 doi: 10.13203/j.whugiS20220120
[61]	Qiu P Y, Gao J L, Lu F. Identifying the relatedness between tourism attractions from online reviews with heterogeneous information network embedding[J]. ISPRS International Journal of Geo-information, 2021, 10(12):797. DOI:10.3390/ijgi10120797 doi: 10.3390/ijgi10120797
[62]	秦川, 祝恒书, 庄福振, 等. 基于知识图谱的推荐系统研究综述[J]. 中国科学:信息科学, 2020, 50(7):937-956.
	[ Qin C, Zhu H S, Zhuang F Z, et al. A survey on knowledge graph-based recommender systems[J]. Sci Sin Inform, 2020, 50(7):937-956. ] DOI:10.1360/SSI-2019-0274 doi: 10.1360/SSI-2019-0274
[63]	高嘉良, 仇培元, 余丽, 等. 基于旅游知识图谱的可解释景点推荐[J]. 中国科学:信息科学版, 2020, 50(7):1055-1068.
	[ Gao J L, Qiu P Y, Yu L, et al. An interpretable attraction recommendation method based on knowledge graph[J]. SCIENTIA SINICA Informationis, 2020, 50(7):1055-1068. ] DOI:10.1360/SSI-2019-0268 doi: 10.1360/SSI-2019-0268
[64]	官赛萍, 靳小龙, 贾岩涛, 等. 面向知识图谱的知识推理研究进展[J]. 软件学报, 2018, 29(10):2966-2994.
	[ Guan S P, Jin X L, Jia Y T, et al. Knowledge reasoning over knowledge graph: A survey[J]. Journal of Software, 2018, 29(10):2966-2994. ] DOI:10.13328/j.cnki.jos.005551 doi: 10.13328/j.cnki.jos.005551
[65]	Zhou J, Ke P, Qiu X P, et al. ChatGPT: Potential, prospects, and limitations[J]. Frontiers of Information Technology & Electronic Engineering. 2023. DOI:10.1631/fitee.2300089 doi: 10.1631/fitee.2300089

Spatiotemporal Knowledge Graph: Advances and Perspectives

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 2

References 65

Related Articles 15

Metrics

Comments

Recommended 0

[1]	CAO Qiaozhuoran, WANG Sisi, CHEN Zugang, LI Guoqing, LI Jing. The Method of Extracting Names of Geo-science Data based on Regular Expressions [J]. Journal of Geo-information Science, 2023, 25(8): 1601-1610.
[2]	ZHANG Chunju, LIU Wencong, ZHANG Xueying, YE Peng, WANG Chen, ZHU Shaonan, ZHANG Dayu. Knowledge Graph Construction Method of Gold Mine based on Ontology [J]. Journal of Geo-information Science, 2023, 25(7): 1269-1281.
[3]	LUO Qiuyu, YUE Yang, GU Yanyan. Hyperparameter Selection for Urban Metro Travel Knowledge Graph Embedding [J]. Journal of Geo-information Science, 2023, 25(6): 1164-1175.
[4]	TANG Zengyang, AI Tinghua, XU Haijiang. Reasoning of Spatial Distribution Pattern of Building Cluster based on Geographic Knowledge Graph [J]. Journal of Geo-information Science, 2023, 25(6): 1202-1214.
[5]	WANG Yipeng, ZHANG Xueying, DANG Yulong, YE Peng. Knowledge Graph Representation of Typhoon Disaster Events based on Spatiotemporal Processes [J]. Journal of Geo-information Science, 2023, 25(6): 1228-1239.
[6]	YANG Yuying, ZHAO Xuesheng, LIU Huiyuan, PENG Shu, LV Yuanxin. Building a Knowledge Graph for Wetlands based on Landcover Data [J]. Journal of Geo-information Science, 2023, 25(6): 1240-1251.
[7]	LIU Jianxiang, CHEN Xiaohui, LIU Haiyan, ZHANG Bing, XU Li, LIU Tao, FU Yumeng. Construction of Ship Activity Knowledge Graph Using Trajectory Semantics [J]. Journal of Geo-information Science, 2023, 25(6): 1252-1266.
[8]	JIANG Bingchuan, HUANG Zihang, REN Yan, SUN Yong, FAN Aimin. Multi-level Knowledge Modeling Method of Battlefield Environment based on Temporal Knowledge Hypergraph Model [J]. Journal of Geo-information Science, 2023, 25(6): 1148-1163.
[9]	CHEN Huixuan, GUO Danhuai, GE Shiyin, WANG Jing, WANG Yangang, CHEN Feng, YANG Weishi. M2T: A Framework of Spatial Scene Description Text Generation based on Multi-source Knowledge Graph Fusion [J]. Journal of Geo-information Science, 2023, 25(6): 1176-1185.
[10]	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.
[11]	LIU Yu, LI Yong. Methodology for Constructing Urban Business Area/Region Knowledge Graph and the Applications in Urban Sustainable Development [J]. Journal of Geo-information Science, 2023, 25(12): 2374-2386.
[12]	SHU Mi, DU Shihong. Forty Years' Progress and Challenges of Remote Sensing in National Land Survey [J]. Journal of Geo-information Science, 2022, 24(4): 597-616.
[13]	CHEN Xiaoling, TANG Liyu, HU Ying, JIANG Feng, PENG Wei, FENG Xianchao. Extracting Entity and Relation of Landscape Plant's Knowledge based on ALBERT Model [J]. Journal of Geo-information Science, 2021, 23(7): 1208-1220.
[14]	ZHENG Xiangtian, GUAN Siping, REN Hongge, XU Rongle, XIANG Bo. Application of Using Knowledge Graph to Explore the Knowledge Pedigree of the Environmental Researches in Northeast Land [J]. Journal of Geo-information Science, 2021, 23(6): 1002-1016.
[15]	SHI Haixia, WEI Yuchun, XU Hanzeyu, ZHOU Shuang, CHENG Qi. Automatic Extraction and Optimal Selection of the Pseudo Invariant Features for the Relative Radiometric Normalization in High-Resolution Remote Sensing Imagery [J]. Journal of Geo-information Science, 2021, 23(5): 903-917.