摘要
地理空间知识的本质在于揭示地理事物和现象的时空分布、时空变化过程及其相互作用规律.地理空间知识库管理系统(GeoKGMS)以"图-文-数"一体化的地理空间知识库引擎为核心,致力于支撑地理空间知识资源的高效汇聚、地理空间知识图谱的自动构建和一站式地理空间知识工程建设,旨在形成新一代地理信息系统(GIS)的重要基础平台.本文重点阐述了基于云原生的地理空间知识库管理关键技术,包括云原生的地理空间知识库微服务统一调度技术、人机协同的地理空间知识图谱构建技术、地理空间知识图谱时空混合编码技术、以及多模态地理空间知识存储技术.在此基础上,设计了GeoKGMS的服务框架,实现了地理空间知识库管理、多模态地理空间知识抽取、地理空间知识图谱人机协同构建、地理空间知识推理、地理空间知识图谱质量评估和地理空间知识可视化六大管理服务功能.以喀斯特地貌知识图谱为例,充分发挥机器挖掘和专家知识的优势,实现了可持续的地理空间知识图谱工程化协同共建.
Abstract
The essence of geospatial knowledge lies in unveiling the spatiotemporal distribution,dynamics of change,and interaction patterns of geographical entities and phenomena.However,existing knowledge base management platforms often overlook the specific needs of geospatial knowledge representation and lack the capability to handle the unique attributes of geospatial data,making it challenging to meet the requirements for constructing and applying geospatial knowledge graphs.The Geospatial Knowledge Base Management System(GeoKGMS)is designed on the basis of an integrated geospatial knowledge base engine that efficiently aggregates geospatial knowledge resources across various modalities—'Image-Text-Number'—automates the construction of geospatial knowledge graphs,and facilitates a one-stop geospatial knowledge engineering process.This paper elucidates four key technologies for managing geospatial knowledge bases.First,the cloud-native geospatial knowledge base microservice unified scheduling technology decomposes the large geospatial knowledge base management system into fine-grained,independently operable,and deployable microservices.By comprehensively managing the lifecycle of the geospatial knowledge base,service classification and orchestration methods are determined to achieve unified scheduling of these microservices.Second,a human-computer collaborative geospatial knowledge graph construction method is proposed,supporting the sustainable,collaborative construction of geospatial knowledge graph engineering.Third,the spatiotemporal hybrid encoding technology of the geospatial knowledge graph achieves unified representation of geospatial knowledge by integrating multimodal geospatial data and spatiotemporal information.Fourth,a multimodal geospatial knowledge integrated storage and large-scale spatiotemporal graph partitioning technology is proposed to address the challenges of efficiently managing complex structured geospatial knowledge and retrieving large-scale spatiotemporal knowledge tuples.Based on these key technologies,an application service framework for GeoKGMS has been designed,featuring six functional modules:geospatial knowledge base management,multimodal geospatial knowledge extraction,human-computer collaborative construction of geospatial knowledge graphs,geospatial knowledge reasoning,geospatial knowledge graph quality assessment,and geospatial knowledge visualization.To demonstrate GeoKGMS's capabilities,the Karst landform knowledge graph is used as a case study.The Karst landform knowledge graph is an integrated'Image-Text-Number'geospatial knowledge graph,constructed based on geospatial knowledge extracted from the texts,schematic diagrams,and related maps in geomorphology textbooks.Through a collaborative pipeline,geomorphology experts and computers jointly perform tasks such as mapping,alignment,supplementation,and conflict resolution of geospatial knowledge.This collaboration ultimately leads to the automated construction of the Karst landform knowledge graph by GeoKGMS.The resulting graph is highly consistent with expert knowledge models,ensuring the interpretability of knowledge-driven geocomputation and reasoning in practical applications.
基金项目
国家重点研发计划项目(2021YFB3900903)
维普
万方数据