Critical Technologies and Service Approaches for Cloud-Native Geospatial Knowledge Base Management
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.
万方数据
维普
