In order to join virtual sessions, you must be registered and logged-in(Were you registered for the in-person meeting in Denver? if yes, just log in.) 
Note: All session times are in Mountain Daylight Time.

AAG 2020 GeoAI and Deep Learning Symposium: Geospatial and Spatiotemporal Ontology and Semantics Session

Type: Paper
Sponsor Groups: Geographic Information Science and Systems Specialty Group, Cyberinfrastructure Specialty Group, Spatial Analysis and Modeling Specialty Group
Poster #:
Day: 4/10/2020
Start / End Time: 11:10 AM / 12:25 PM (MDT)
Room: Virtual Track 1
Organizers: Alexandre Sorokine, Jeon-Young Kang, Chen-Chieh Feng
Chairs: Alexandre Sorokine


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.


Type Details Minutes Start Time
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

To access contact information login