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Big Earth Data Volume 3, 2019 - Issue 3: Geospatial semantics, ontology and knowledge graphs for big Earth data

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A vocabulary recommendation method for spatiotemporal data discovery based on Bayesian network and ontologies

Kejin CuiDepartment of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USACorrespondence[email protected]
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Yongyao JiangDepartment of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USA;Environmental Systems Research Institute, Redlands, CA, USAView further author information

Yun LiDepartment of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USAView further author information

Dieter PfoserDepartment of Geography and Geoinformation Science, George Mason University, Fairfax, VA, USAView further author information

Pages 220-231 | Received 30 May 2019, Accepted 30 Jul 2019, Published online: 20 Aug 2019

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https://doi.org/10.1080/20964471.2019.1652431
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Samantha T. Arundel & E. Lynn Usery. 2021. Handbook of Big Geospatial Data. Handbook of Big Geospatial Data 119 134 .

Chi Yunxian, Li Renjie, Zhao Shuliang & Guo Fenghua. (2020) Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing. PLOS ONE 15:4, pages e0228175.
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