地理知识图谱推理研究进展与展望  

Reasoning on Geographical Knowledge Graph:Advances and Perspectives

作　　者：段雨希 陈碧宇[1,2] 李岩 张雪英[3,4,5] 林黎[1,2] DUAN Yuxi;CHEN Biyu;LI Yan;ZHANG Xueying;LIN Li(State Key Laboratory of information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;The Geo-Computation Center for Social Sciences,Wuhan University,Wuhan 430079,China;School of Geography,Nanjing Normal University,Nanjing 210023,China;Key Laboratory of Virtual Geographic Environment,Ministry of Education,Nanjing Normal University,Nanjing 210023,China;Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application,Nanjing 210023,China)

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,武汉430079 [2]武汉大学社会地理计算联合研究中心,武汉430079 [3]南京师范大学地理科学院,南京210023 [4]南京师范大学虚拟地理环境教育部重点实验室,南京210023 [5]江苏省地理信息资源开发与利用协同创新中心,南京210023

出　　处：《地球信息科学学报》2025年第1期41-59,共19页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2021YFB3900903);国家自然科学基金项目(42271473)。

摘　　要：【目的】随着知识图谱技术在GIS领域应用和发展,地理知识图谱(GeoKG)近年来逐渐成为GIS领域的重要研究方向。GeoKG往往无法确保涵盖所有知识,知识的缺失和不一致性严重影响应用性能,需要采用GeoKG推理技术来自动补全缺失知识、识别矛盾知识、预测地理现象未来发展趋势。区别于通用知识图谱推理技术,GeoKG推理技术需要着重考虑地理知识的复杂时空特性。本文对近年来GeoKG的推理工作进行了全面介绍和总结。【分析】首先,介绍了GeoKG推理的相关概念与问题描述;其次,本文分析了GeoKG推理的二大核心任务:①面向知识补全的推理模型,主要用于填补图谱中的空白,确保知识的完整性;②面向预测任务的推理模型,旨在通过已有地理数据预测未来的趋势。两类模型各自针对不同的应用场景进行优化,并在地理数据的处理中各有侧重。【展望】展望了GeoKG推理的未来发展趋势,指出未来GeoKG推理技术的发展将更加关注时空数据的复杂关系处理、多尺度地理知识的推理、多模态数据的融合,以及提高推理模型的可解释性与智能化。此外,GeoKG与大规模预训练模型的结合也将成为关键方向。[Objectives]With the application of knowledge graph techniques in the field of Geographical Information Science(GIS),the Geographical Knowledge Graph(GeoKG)has become a key research direction.GeoKGs often lack sufficient geographic knowledge coverage,which can negatively impact downstream applications.Therefore,reasoning techniques are essential for GeoKG to complete missing knowledge,identify inconsistencies,and predict trends in geographic phenomena.Unlike reasoning techniques applied to general knowledge graphs,reasoning on GeoKGs must handle the unique and complex spatial and temporal characteristics of geographic phenomena.This paper comprehensively introduces and summarizes recent advances in GeoKG reasoning.[Analysis]First,it introduces the relevant concepts and problem definitions of GeoKG reasoning.Second,it analyzes the two core tasks of GeoKG reasoning:knowledge completion and prediction.The reasoning model for knowledge completion primarily fills gaps in the graph to ensure knowledge integrity,while the reasoning model for prediction aims to forecast future trends based on existing geographic data.These two models are optimized for different application scenarios,with different focuses in processing geographic data.[Prospect]Finally,the paper explores future development trends in GeoKG reasoning,highlighting areas such as processing complex relationships in spatiotemporal data,reasoning with multi-scale geographic knowledge,fusing multimodal data,and enhancing the interpretability and intelligence of reasoning models.Additionally,the integration of GeoKGs with large-scale pre-trained models is expected to become a key area of focus.

关 键 词：地理知识图谱 地理知识推理 研究进展 地理知识表达 时空数据推理 

分 类 号：P208[天文地球—地图制图学与地理信息工程]