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

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.