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Title: Comparing English, Mandarin, and Russian hydrographic and terrain categories
Authors: Feng, Chen-Chieh 
Sorokine, Alexandre
Issue Date: Jun-2014
Publisher: Taylor & Francis Ltd.
Citation: Feng, Chen-Chieh, Sorokine, Alexandre (2014-06). Comparing English, Mandarin, and Russian hydrographic and terrain categories. International Journal of Geographical Information Science 28 (6) : 1294-1315. ScholarBank@NUS Repository.
Abstract: The paper compares hydrographic and terrain categories in the geospatial data standards of the United States, Taiwan, and Russian Federation where the dominant languages used are from different language families. It aims to identify structural and semantic differences between similar categories across three geospatial data standards. By formalizing the data standard structures and identifying the properties that differentiate sibling categories in each geospatial data standard using well-known formal relations and quality universals, we develop a common basis on which hydrographic and terrain categories in the three data standards can be compared. The result suggests that all the three data standards structure categories with a mixture of relations even though most of them are well-known relations in top-level ontologies. Similar categories can be found across all the three standards. Cases of categories from different standards carrying identical meaning are rare. Partial overlaps in the meaning of the similar categories can be a direct result of different quality universals at work in defining and distinguishing these categories, or in the case of these categories being ordered by size, the threshold values for distinguishing the categories are ambiguous and language-dependent. Understanding these differences avoids incorrect mappings of categories in multilingual applications. More importantly, it provides a starting point for more effective mapping between hydrographic and terrain categories between English, Mandarin, and Russian.
Source Title: International Journal of Geographical Information Science
ISSN: 13658816
DOI: 10.1080/13658816.2013.831420
Appears in Collections:Staff Publications

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