• Journal of Korean Library and Information Science Society
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• v.44 no.1
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• pp.385-408
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• 2013

The purpose of this study is to build the tax ontology which can be used to manage imposables by the state or local governments. In this, the tax and related concepts were analyzed and then concept hierarchy i.e., taxonomies were formed. Especially, in the concept hierarchy, after multiple inherits were decomposed as 'primitive concepts' and then Rector's 'methodology of ontology implementation normalization', in which defined concepts were recombined, was used. The methodology employed was that the tax system, which was entangled with the direct taxes, local taxes, and property taxes that has multiple-inherits, was expressed explicitly and logically. After that, automatic classification was carried out through the inference engine, consistency was verified. Finally, some practical cases of ontology created were enumerated.

• Journal of KIISE:Databases
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• v.35 no.3
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• pp.231-242
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• 2008

W3C has selected OWL as a standard for Web ontology description and a necessity of research on storage models that can store OWL ontologies effectively has been issued. Until now, relational model-based storage systems such as Jena, Sesame, and DLDB, have been developed, but there still remain several issues. Especially, they lead inefficient query processing performance. The structural problems of their low query processing performance are as follow: Jena has a simple structure which is not normalized and also stores most information in a single table. It exponentially decreases the performance because of comparison with unnecessary information for processing queries requiring join operations as well as simple search. The structures of storages(e.g., Sesame) have been completely normalized. Therefore it executes many join operations for query processing. The storages require many join operations to find simply a specific class. This paper proposes a storage model to resolve the problems that the query processing performance is decreased because of non-normalization or complete normalization of the existing storages. To achieve this goal, we analyze the problems of existing storage models as well as relations of OWL elements and query patterns. The proposed model, defined with the analysis results, provides an optimal normalized structure to minimize join operations or unnecessary information comparison. For the experiment of query processing performance, a LUBM data sets are used and query patterns are defined considering search targets and their hierarchical relations. In addition, this paper conducts experiments on correctness and completeness of query results to verify data loss of the proposed model, and the results are described. With the comparative evaluation results, our proposal showed a better performance than the existing storage models.