Capturing and representing geospatial and spatiotemporal dimensions of geographic knowledge is a great challenge from both theoretical and applied perspectives. Nowadays formalized geospatial knowledge representations and reasoning in the form of ontologies and knowledge graphs powers search engines, discovery of geodata, and understanding of the crowdsourced information. New technologies like deep learning and advanced natural language processing open new possibilities for practical applications and research in this area.
|Presenter||Dalia Varanka*, United States Geological Survey, A Semantic Technology System for National Topographic Data||15||11:10 AM|
|Presenter||Kejin Cui*, George Mason University, A Vocabulary Recommendation Method for Spatiotemporal Data Discovery Based on Bayesian Network and Ontologies||15||11:25 AM|
|Presenter||Alexandre Sorokine*, Oak Ridge National Laboratory, Jason Kaufman, Oak Ridge National Laboratory, Robert Stewart, Oak Ridge National Laboratory, Jessie Piburn, Oak Ridge National Laboratory, Geographic data matching and conflation: challenges and an active learning solution||15||11:40 AM|
|Discussant||Dalia Varanka United States Geological Survey||15||11:55 AM|
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