An al-based spatial knowledge graph for enhancing spatial data and knowledge search and discovery

Zhe Zhang, Zhangyang Wang, Angela Li, Xinyue Ye, E. Lynn Usery, Diya Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Geospatial data has been widely used in Geographic Information Systems to understand spatial relationships in various application domains such as disaster response, agriculture risk management, environmental planning, and water resource protection. Many data sharing platforms such as NASA Open Data Portal and USGS Geo Data portal have been developed to enhance spatial data sharing services. However, enabling intelligent and efficient spatial data sharing and communication across different domains and stakeholders (e.g., data producers, researchers, and domain experts) is a formidable task. The challenges appear in building meaningful semantics between data products using spatiotemporal similarity measures to efficiently help users find all the relevant data and information at the space-Time scale. In this paper, we developed a novel AI-based graph embedding algorithm to build semantic relationships between different spatial data sets to enable efficient and accurate data search. We applied the graph embedding algorithm to 30,000 NASA metadata records to test our algorithm's performance. In the end, we visualized the knowledge graph using the Neo4j database graphical user interface.

Original languageEnglish (US)
Title of host publicationProceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, GeoSearch 2021
EditorsGabriele Cavallaro, Dora B. Heras, Dalton Lunga, Martin Werner, Andreas Zufle
PublisherAssociation for Computing Machinery, Inc
Pages13-17
Number of pages5
ISBN (Electronic)9781450391238
DOIs
StatePublished - Nov 2 2021
Externally publishedYes
Event1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, GeoSearch 2021 - Beijing, China
Duration: Nov 2 2021Nov 2 2021

Publication series

NameProceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, GeoSearch 2021

Conference

Conference1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data, GeoSearch 2021
Country/TerritoryChina
CityBeijing
Period11/2/2111/2/21

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Keywords

  • Artificial intelligence
  • Geographic information system
  • Keywords
  • Knowledge graph
  • Spatial data search

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