Efficient spatial knowledge queries of The National Map using Geospatial Semantic Web technologies

Authors: Chuanrong Zhang*, University of Connecticut, Department of Geography, Tian Zhao, Department of Computer Science, University of Wisconsin, Milwaukee, USA, Weidong Li, Department of Geography & Center of Environmental Sciences and Engineering, University of Connecticut, Storrs, USA
Topics: Geographic Information Science and Systems, Cyberinfrastructure, Quantitative Methods
Keywords: Spatial Big Data, Geospatial Semantic Web, The National Map, CyberGIS
Session Type: Paper
Day: 4/10/2018
Start / End Time: 4:40 PM / 6:20 PM
Room: Grand Ballroom A, Astor, 2nd Floor
Presentation File: No File Uploaded

With development of computer and Internet technologies, petabytes of spatial data were generated and used by thousands of scientists, citizens, and educators. Semantic interoperability, which targets for integrating, interlinking, and retrieving vast geo-referenced, multi-perspective geospatial knowledge through the Web, is one core research topic for using big spatial data. Geospatial Semantic Web offers the support of semantic interoperability to the Web and extends the Web from a data archive and infrastructure to a knowledge engine, which enables more powerful reasoning and information retrieving from heterogeneous and contradicting conceptual models and scientific data in the Web. The USGS (United States Geological Survey) have been working on developing ontologies for The National Map for many years and has published RDF triple data derived from The National Map to support geospatial knowledge queries. However, representing structured geospatial data in these languages can result in inefficient data access. The objective of this research is to develop new approaches for efficient spatial knowledge queries of The National Map using Geospatial Semantic Web technologies. We will further our existing work on Geospatial Semantic Web to improve the performance of spatial knowledge queries by separating spatial queries from attribute queries so that spatial queries can be processed in efficient platforms such as computing clusters and GPUs.

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