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An Al-based Spatial Knowledge Graph for Enhancing Spatial Data and Knowledge Search and Discovery

Published: 08 November 2021 Publication History

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.

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Cited By

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  • (2024)ESDC: 一种用于支持地学文献信息抽取的开放地球科学数据语料库SCIENTIA SINICA Terrae10.1360/N072023-0247Online publication date: 12-Nov-2024
  • (2023)A Review of Governance Frameworks for National Spatial Data InfrastructureInnovation, Strategy, and Transformation Frameworks for the Modern Enterprise10.4018/979-8-3693-0458-7.ch008(186-207)Online publication date: 29-Sep-2023
  • (2023)MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligencePLOS ONE10.1371/journal.pone.029303418:11(e0293034)Online publication date: 13-Nov-2023
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cover image ACM Conferences
GeoSearch'21: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data
November 2021
30 pages
ISBN:9781450391238
DOI:10.1145/3486640
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 08 November 2021

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Author Tags

  1. Artificial Intelligence
  2. Geographic Information System
  3. KEYWORDS
  4. Knowledge Graph
  5. Spatial Data Search

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Cited By

View all
  • (2024)ESDC: 一种用于支持地学文献信息抽取的开放地球科学数据语料库SCIENTIA SINICA Terrae10.1360/N072023-0247Online publication date: 12-Nov-2024
  • (2023)A Review of Governance Frameworks for National Spatial Data InfrastructureInnovation, Strategy, and Transformation Frameworks for the Modern Enterprise10.4018/979-8-3693-0458-7.ch008(186-207)Online publication date: 29-Sep-2023
  • (2023)MetaQA: Enhancing human-centered data search using Generative Pre-trained Transformer (GPT) language model and artificial intelligencePLOS ONE10.1371/journal.pone.029303418:11(e0293034)Online publication date: 13-Nov-2023
  • (2023)Location-Aware Social Network Recommendation via Temporal Graph NetworksProceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising10.1145/3615896.3628342(58-61)Online publication date: 13-Nov-2023

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