Using uncertain conceptual spaces to translate between land cover categories
To support diversified uses of geographical information there is a need for enhanced spatial data infrastructures to create interoperability between users and producers of geographic data. One important interoperability problem is caused by differences in data semantics, for example heterogeneous land use/land cover classification systems. A critical review of an existing method for semantic interoperability between land cover classifications is used to motivate and introduce a modified framework based on conceptual spaces and rough fuzzy sets. Land cover categories are defined by a set of defining attributes formally represented as a collection of rough fuzzy membership functions and importance weights. This parameterized representation is used to translate between the US Natural Vegetation Classification Standard and the European CORINE Land Cover system based on evaluations of different aspects of semantic similarity between categories. The results demonstrate that the use of different similarity metrics in a conceptual space, together with the explicit rough fuzzy uncertainty representation, increases the semantic separation between land cover categories. Diagrams and maps illustrate the information that can be gained from the semantic similarity assessment. These developments open new possibilities to explore semantic relationships between concepts, both within a classification and between classifications used in different contexts.
conceptual spaces interoperability semantic uncertainty land cover classes fuzzy categories ontology