Spatial entity mapping method in fuzzy linguistic environment

Abstract

The invention discloses a spatial entity mapping method in a fuzzy linguistic environment. Calculation is performed through concepts, attributes and spatial characteristic parameter similarities of a spatial entity and a geographic object, and spatial entity positioning and precise spatial entity mapping in a concept level are achieved; multi-scale classification banks of the spatial entity are built, fuzzy scale factors are extracted from the geographic object facing public services and matched with the multi-scale classification banks, and therefore semantic mapping, based on the fuzzy scale factors, of the spatial entity and the geographic object is achieved. Accordingly, a method which is higher in openness and adaptability can be supplied for the ever-increasing geographic information retrieval requirements.

Description

technical field [0001] The invention relates to a spatial entity mapping method, in particular to a spatial entity mapping method in a fuzzy context, and belongs to the technical field of information retrieval. Background technique [0002] In the application of public geographic information services, geographic information is mainly expressed in the form of text, showing significant semi-structured and unstructured features in form. In addition, compared with traditional professional geographic information services, the query and retrieval conditions entered by users in the form of text in public application services and the results returned by the service are quite uncertain and ambiguous in terms of semantics, time, and space. Traditional precise matching of text and values ​​cannot solve the accuracy of the semantic layer, often resulting in results returned by spatial information services that are too coarse or too fine in terms of semantic scale, time scale, and spatia...

AI Technical Summary

Problems solved by technology

[0004] For the diversity of geospatial information representations, the ambiguity of description, and the multi-scale nature of spatial entities, since the traditional exact matching method based on text and numerical values ​​can no longer solve the accurate mapping of the semantic layer of spatial entities, the purpose of the present invention is to Provide a spatial entity mapping method in a fuzzy context. This method realizes the positioning of spatial entities and precise mapping at the conceptual level through the calculation of the similarity between spatial entities and geographical objects, concepts, attributes, and spatial feature parameters. Scale classification library, which extracts fuzzy scale factors from public service-oriented geographic objects, and performs semantic matching with multi-scale classification libraries to realize the semantic mapping between spatial entities and geographic objects based on fuzzy scale factors, thereby providing a new dimension for the growing geographic information retrieval. Demand provides a more open and adaptable approach

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