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A Hybrid Classification Matching Method for Geospatial Services

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Abstract

With the development of the Internet and GIS, large volumes of spatial data and powerful computing resources are increasingly published in the form of Web services. Given the variety and number of geospatial services advertised online, finding appropriate geospatial services has become a tremendous challenge for potential users. Geospatial service classification provides a basis for developing matching criteria to improve the efficiency of service discovery. At present, most classification‐based matching methods require users to provide classification descriptions using a specified taxonomy. These requirements seriously limit the application of classification‐based matching. To solve these kinds of problems, this article presents a hybrid geospatial service classification‐matching method. Based on the differences in classification descriptions, three strategies are proposed: (1) the existing classification matching method is used for requests with classifications described using homogeneous taxonomies; (2) a formal‐concept‐analysis‐based service classification matching method is proposed for service requests with classifications described using heterogeneous taxonomies; and (3) an interface‐similarity‐based service classification decision method is proposed for requests without classification descriptions. The feasibility of the hybrid geospatial service classification matching method is verified by two sets of experiments. The results reveal that this method can effectively broaden the application of classification‐based matching in geospatial service discovery.
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Document Type: Research Article

Publication date: 2012-12-01

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