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A Study on Conversion Methods for Generating RDF Ontology from Structural Terminology Net (STNet) based on RDB

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2015, 32(2), pp.131~152
  • DOI : 10.3743/KOSIM.2015.32.2.131
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : May 27, 2015
  • Accepted : June 19, 2015
  • Published : June 30, 2015

KO, YOUNG MAN 1,  Seung-Jun Lee 2,  Song min-sun 2

1성균관대학교
2성균관대학교 정보관리연구소

Accredited

ABSTRACT

This study described the results of converting RDB to RDF ontology by each of R2RML method and Non-R2RML method. This study measured the size of the converted data, the conversion time per each tuple, and the response speed to queries. The STNet, a structured terminology dictionary based on RDB, was served as a test bed for converting to RDF ontology. As a result of the converted data size, Non-R2RML method appeared to be superior to R2RML method on the number of converted triples, including its expressive diversity. For the conversion time per each tuple, Non-R2RML was a little bit more faster than R2RML, but, for the response speed to queries, both methods showed similar response speed and stable performance since more than 300 numbers of queries. On comprehensive examination it is evaluated that Non-R2RML is the more appropriate to convert the dynamic RDB system, such as the STNet in which new data are steadily accumulated, data transformation very often occurred, and relationships between data continuously changed.

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This paper was written with support from the National Research Foundation of Korea.
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