Abstract
The Open Geospatial Consortium (OGC) Geography Markup Language (GML) explicitly represents geographical spatial knowledge in text mode. All kinds of fuzzy problems will inevitably be encountered in spatial knowledge expression. Especially for those expressions in text mode, this fuzziness will be broader. Describing and representing fuzziness in GML seems necessary. Three kinds of fuzziness in GML can be found: element fuzziness, chain fuzziness, and attribute fuzziness. Both element fuzziness and chain fuzziness belong to the reflection of the fuzziness between GML elements and, then, the representation of chain fuzziness can be replaced by the representation of element fuzziness in GML. On the basis of vague soft set theory, two kinds of modeling, vague soft set GML Document Type Definition (DTD) modeling and vague soft set GML schema modeling, are proposed for fuzzy modeling in GML DTD and GML schema, respectively. Five elements or pairs, associated with vague soft sets, are introduced. Then, the DTDs and the schemas of the five elements are correspondingly designed and presented according to their different chains and different fuzzy data types. While the introduction of the five elements or pairs is the basis of vague soft set GML modeling, the corresponding DTD and schema modifications are key for implementation of modeling. The establishment of vague soft set GML enables GML to represent fuzziness and solves the problem of lack of fuzzy information expression in GML.
Highlights
All kinds of fuzzy problems will inevitably be encountered because many natural phenomena have fuzzy characteristics in the expression of spatial knowledge [1]
The main objective of this paper is to develop a fuzzy Geography Markup Language (GML) model to represent fuzziness in GML based on the vague soft set theory, which includes a vague soft set GML Document Type Definition (DTD) model and a vague soft set GML schema model
In lines 4–7, the fuzzy data represented by a vague soft set are introduced between the landmarkMembers element and the landmarkMember element in order to express the degree of identity of the Elephant Trunk Hilllandmark, where TrueMembership and FalseMembership are two attributes belonging to the VagueSet element
Summary
All kinds of fuzzy problems will inevitably be encountered because many natural phenomena have fuzzy characteristics in the expression of spatial knowledge [1]. Due to its explicit expressions of geographic spatial knowledge, GML has become an important tool to deal with geographic spatial information, and is a standard for spatial data representation and exchange over the Web [2]. Chen [22] defined GXQuery, based on XQuery, and presented its applicability through typical examples These studies discussed GML from its modeling, parsing, storage, transmission, and visualization to its applications. For example, in a strange city, it is hard to clearly express a position because of this imperfect knowledge. In this regard, or to derive better results, and for a better understanding of the real world [2], a fuzzy representation method for GML should be developed.
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