To support diversified uses of geographical information there is a need for enhanced spatial data infrastructures to create interoperability between users and prod.ucers of geographic data. One important interoperability problem is caused by dlfTerences in data semantics, for example heterogeneous land use/land cover ~lassification systems. A critical review of an existing method for semantic mteroperability 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.
Most conceptual modeling in geographic information science to date has used a symbolic approach with little or no recognition of the semantic uncertainty often found in geographic concepts. This work describes a concept model based on parameterized concept descriptions that uses a spatial metaphor, the conceptual space, as an organizing structure (Gärdenfors 2000). This cognitive theory of conceptual spaces is combined with a formal representation of semantic uncertainty based on rough fuzzy sets. The conceptual space then represents each concept as a collection of rough fuzzy property definitions with associated salience weights, where a property itself can be treated as a special case of a concept. Instead of explicitly defining concept hierarchies, we can allow different conceptual structures to emerge through measures of concept inclusion and similarity. A land use/land cover example demonstrates how the model represents concepts, concept similarity, hierarchical structures and the context dependence of concepts. The final section of the paper points to the need for further studies of context effects, concept similarity measures, and uncertainty representation using the proposed model.
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