Paper
15 October 2009 Ontology for cell-based geographic information
Bin Zheng, Lina Huang, Xinhai Lu
Author Affiliations +
Proceedings Volume 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining; 749222 (2009) https://doi.org/10.1117/12.837460
Event: International Symposium on Spatial Analysis, Spatial-temporal Data Modeling, and Data Mining, 2009, Wuhan, China
Abstract
Inter-operability is a key notion in geographic information science (GIS) for the sharing of geographic information (GI). That requires a seamless translation among different information sources. Ontology is enrolled in GI discovery to settle the semantic conflicts for its natural language appearance and logical hierarchy structure, which are considered to be able to provide better context for both human understanding and machine cognition in describing the location and relationships in the geographic world. However, for the current, most studies on field ontology are deduced from philosophical theme and not applicable for the raster expression in GIS-which is a kind of field-like phenomenon but does not physically coincide to the general concept of philosophical field (mostly comes from the physics concepts). That's why we specifically discuss the cell-based GI ontology in this paper. The discussion starts at the investigation of the physical characteristics of cell-based raster GI. Then, a unified cell-based GI ontology framework for the recognition of the raster objects is introduced, from which a conceptual interface for the connection of the human epistemology and the computer world so called "endurant-occurrant window" is developed for the better raster GI discovery and sharing.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Bin Zheng, Lina Huang, and Xinhai Lu "Ontology for cell-based geographic information", Proc. SPIE 7492, International Symposium on Spatial Analysis, Spatial-Temporal Data Modeling, and Data Mining, 749222 (15 October 2009); https://doi.org/10.1117/12.837460
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Cited by 2 scholarly publications.
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