Sang Gi Lee ¹, Byeonghee Lee ², Hanjo Jeong ²

Affiliations
1 School of Computer Science, University of Seoul and KISTI, KR
2 NTIS Center, KISTI, KR

Abstract

National Science and Technology Information Service (NTIS) is constructed and operated to provide a comprehensive data provider service of national R&D data for improving the performance of the national R&D projects and disseminating the co-utilization of the national R&D data nationwide in Korea. The national R&D data is collected from 17 government departments and agencies. With the spread of the Government 3.0 paradigm, the demand for data sharing and utilization is being increased. However, the immature of the data quality management process for the public data hinders the progress on the data openness. This research presents the problems of the data quality management via a study on the current data quality management process/system of the national R&D data and proposes an improvement plan for the data quality management. In addition, DQC-M (Database Quality Certification-Management), which is a representative evaluation and certification model for assessing the quality and maturity of data quality management, is used to find insufficiency through the evaluation of the quality and matureness of the data quality management of NTIS service.

Keywords

Data Openness, Data Quality Management, DQC-M, National R&D Data.

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Heeseok Jeong ¹, Hanjo Jeong ¹

Affiliations
1 NTIS Center, KISTI, KR

Abstract

Korean National Science and Technology Information Service (NTIS) provide a service of searching national R&D projects and their participating researcher information. It also provides a service of recommending and selecting evaluation committees for the R&D projects. Such R&D data and information are collected from 17 Korean government ministries and agencies and integrated into NTIS. Therefore, the duplicates of the R&D data and researcher information can be inserted because the titles of a researcher's R&D accomplishment data can be differently inserted from the different organizations. Furthermore, the names of researchers and other related objects such as organizations and journals can also be inserted vairously as the names have various aliases in general. In this research, we present an ontology-based data integration and refinement system for integrating such researcher information and their R&D accomplishment data, which would be useful for the recommendation and selection services. Also, we also used Jaro-Winkler distance algorithm to find and eliminate the duplicated accomplishment data. Furthermore, incorrectly entered data are also corrected from the duplicate elimination process with the information obtained from some authoritative science libraries.

Keywords

Data Integration, Data Refinement, Jaro-Winkler Distance, National R&D Data, Ontology.

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