时空知识图谱研究进展与展望

doi:10.12082/dqxxkx.2023.230154

地球信息科学学报 ›› 2023, Vol. 25 ›› Issue (6): 1091-1105.doi: 10.12082/dqxxkx.2023.230154

• 专刊：地理时空知识图谱理论方法与应用 • 上一篇下一篇

时空知识图谱研究进展与展望

陆锋¹^,²^,⁴^,⁵^,^*(), 诸云强¹^,²^,⁴, 张雪英³^,⁴

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.中国科学院大学，北京 100049
3.南京师范大学虚拟地理环境教育部重点实验室，南京 210023
4.江苏省地理信息资源开发与利用协同创新中心，南京 210023
5.政务大数据应用省部共建协同创新中心，福州 350003

收稿日期:2023-03-27 修回日期:2023-04-19 出版日期:2023-06-25 发布日期:2023-06-02
作者简介:陆锋（1970— ），男，新疆乌鲁木齐人，博士，研究员，主要从事地理空间智能、地理大数据挖掘、时空知识图谱研究。E-mail: luf@lreis.ac.cn
基金资助:
国家重点研发计划项目(2022YFB3904200);国家重点研发计划项目(2021YFB3900900);国家自然科学基金项目(41631177);国家自然科学基金项目(42050101)

Spatiotemporal Knowledge Graph: Advances and Perspectives

LU Feng¹^,²^,⁴^,⁵^,^*(), ZHU Yunqiang¹^,²^,⁴, ZHANG Xueying³^,⁴

1. State Key Lab of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Beijing 100101, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Key Laboratory of Virtual Geographic Environment (Ministry of Education), Nanjing Normal University, Nanjing 210023, China
4. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
5. Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou 350003, China

Received:2023-03-27 Revised:2023-04-19 Online:2023-06-25 Published:2023-06-02
Contact: *LU Feng, E-mail: luf@lreis.ac.cn
Supported by:
National Key Research and Development Program of China(2022YFB3904200);National Key Research and Development Program of China(2021YFB3900900);National Natural Science Foundation of China(41631177);National Natural Science Foundation of China(42050101)

摘要/Abstract

摘要：

地理信息的不断泛化对经典的地理信息分析模式提出了巨大挑战，网络化的知识服务将逐渐成为地理信息应用的新模式，助力地理计算到社会计算的形态转变。地理知识服务需要打通人、机构、自然环境、地理实体、地域单元、社会事件之间的关联，促进知识辅助下的数据智能与计算智能。本文聚焦地理时空知识获取与形式化表达及分析的迫切需求，首先分析了时空知识图谱的基本概念与特征，认为时空知识图谱是指具有地理时空分布或位置隐喻的知识构成的有向图，即以时空分布特征为核心的知识图谱；然后提出了时空知识图谱的研究框架，该框架可实现时空大数据到时空知识服务应用的转变，包括泛在时空大数据、时空知识获取、时空知识管理、时空知识图谱、软件系统及行业应用等多个层次；接着从文本描述地理信息抽取、异构地理语义网对齐、时空知识表达与表示学习等方面，介绍了相关研究进展；结合应用实践，介绍了面向行业的时空知识图谱构建与应用途径；最后，讨论了时空知识图谱研究目前面临的关键科学问题与技术瓶颈，提出在大模型时代，构建显式的时空知识图谱，并针对行业需求开展知识推理，仍是时空知识服务的必由之路。

关键词: 时空知识, 知识图谱, 地理空间智能, 知识表达, 时空大数据, 信息抽取, 地理语义对齐, 研究进展

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

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.DOI:10.12082/dqxxkx.2023.230154

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

图/表 2

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时空知识图谱研究进展与展望

Spatiotemporal Knowledge Graph: Advances and Perspectives

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