Geospatial Information Categories Mapping in a Cross-lingual Environment: A Case Study of "Surface Water" Categories in Chinese and American Topographic Maps

• Published in: ISPRS international journal of geo-information Vol. 5; no. 6; p. 90
• Main Authors: Kuai, Xi, Li, Lin, Luo, Heng, Hang, Shen, Zhang, Zhijun, Liu, Yu
• Format: Journal Article
• Language: English
• Published: 01.01.2016
• Subjects: Categories; Classification; Knowledge representation; Mapping; Mathematical models; Satellite navigation systems; Semantics; Similarity
• ISSN: 2220-9964
• DOI: 10.3390/ijgi5060090(registeringDOI)

The need for integrating geospatial information (GI) data from various heterogeneous sources has seen increased importance for geographic information system (GIS) interoperability. Using domain ontologies to clarify and integrate the semantics of data is considered as a crucial step for successful semantic integration in the GI domain. Nevertheless, mechanisms are still needed to facilitate semantic mapping between GI ontologies described in different natural languages. This research establishes a formal ontology model for cross-lingual geospatial information ontology mapping. By first extracting semantic primitives from a free-text definition of categories in two GI classification standards with different natural languages, an ontology-driven approach is used, and a formal ontology model is established to formally represent these semantic primitives into semantic statements, in which the spatial-related properties and relations are considered as crucial statements for the representation and identification of the semantics of the GI categories. Then, an algorithm is proposed to compare these semantic statements in a cross-lingual environment. We further design a similarity calculation algorithm based on the proposed formal ontology model to distance the semantic similarities and identify the mapping relationships between categories. In particular, we work with two GI classification standards for Chinese and American topographic maps. The experimental results demonstrate the feasibility and reliability of the proposed model for cross-lingual geospatial information ontology mapping.