摘要
时空知识图谱是当前多模态、多类型时空知识组织和服务的最有效方式,已成为地理信息科学发展的前沿和国家高质量发展与行业现代化治理的必然要求。然而,尽管时空知识图谱的构建研究已取得一定进展,其规模化、系统化的行业应用仍处于起步阶段,尤其在构建适合复杂时空特征与关系的时空知识图谱并提供智能化知识服务方面仍面临诸多挑战。因此,本文系统分析时空知识内涵、特征及类别;深入剖析时空知识图谱表达模型、时空数据获取与质量评估、时空信息抽取与对齐融合、时空知识图谱构建与更新补全四类构建关键问题,以及时空知识图谱计算推理与知识发现、时空知识图谱高效可视与检索、时空知识图谱多场景应用服务三类服务关键问题,并阐述相应的研究进展;同时,给出时空知识图谱的未来发展趋势。
The spatiotemporal knowledge graph(STKG)represents the most effective framework for organizing and disseminating multimodal,heterogeneous spatiotemporal knowledge.As a cutting-edge frontier in geographic information science,STKGs fulfill critical requirements for high-quality national development and modern industrial governance.Despite significant advances in STKG construction,large-scale systematic industrial applications remain nascent.Key challenges persist,particularly in modeling complex spatiotemporal features/relationships and delivering intelligent knowledge services.This paper systematically examines the connotations,characteristics,and classifications of spatiotemporal knowledge while conducting an in-depth analysis of seven core challenges:(1)STKG representation models,(2)spatiotemporal data acquisition and quality assessment,(3)spatiotemporal information extraction and alignment fusion,(4)STKG construction and dynamic updating,(5)STKG computational reasoning and knowledge discovery,(6)efficient visualization/retrieval mechanisms,and(7)multi-scenario application services.Corresponding methodological solutions are elaborated.Focusing on six spatiotemporal data types—professional spatial datasets,mobile trajectories,IoT sensor feeds,scientific literature,web text,and geo-semantic web resources—this research achieves automated construction and intelligent services spanning“data→information→knowledge.”Core advancements include:(1)Adaptive Representation Model:A spatiotemporal correlation-aware knowledge graph expression framework integrating embedded representations for diverse spatiotemporal features.(2)Quality Control System:Data source/content-oriented evaluation indices and quality factor extraction methods for spatiotemporal knowledge graph development.(3)Multimodal Information Mining:Hybrid shallow mapping/deep learning approaches for high-precision spatiotemporal information extraction,coupled with collaborative time-space-semantic modeling for tuple alignment/fusion.(4)Inference&Discovery:Ontology-constrained joint embedding for vector-based spatiotemporal reasoning,combined with textual inference and structural learning for new knowledge discovery.(5)Visualization Efficiency:Adaptive hierarchical nebula visualization and spatiotemporal index-integrated pruning algorithms to enhance large-scale STKG retrieval performance.(6)Engineering Applications:Three-tiered service architecture—(i)foundational spatiotemporal big data governance,(ii)business-process-oriented core knowledge services,and(iii)large-model-empowered knowledge fusion.To address urgent demands from AI advancement—particularly high-quality large-model dataset development—future STKG research must prioritize:(1)refining modeling methodologies and core technologies,(2)establishing large-scale high-fidelity STKG infrastructure,(3)deepening STKG-large model integration,and(4)accelerating engineering/business implementation.
作者
诸云强
徐柱
邓敏
甘小莺
刘万增
ZHU Yunqiang;XU Zhu;DENG Min;GAN Xiaoying;LIU Wanzeng(State Key Laboratory of Resources and Environmental Information System,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;School of Geosciences and Engineering,Southwest Jiaotong University,Chengdu 611756,China;School of Geosciences and Info-physics,Central South University,Changsha 410012,China;School of Electronic,Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;National Geomatics Center of China,Beijing 100830,China)
出处
《时空信息学报》
2025年第6期593-605,共13页
JOURNAL OF SPATIO-TEMPORAL INFORMATION
基金
国家重点研发计划项目(2022YFB3904200)。
关键词
时空知识
知识图谱
表达模型
知识图谱构建
知识服务
spatiotemporal knowledge
knowledge graph
expression model
knowledge graph construction
knowledge service
