발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.3] Data Standardization Verification and Transition Model Based on Public Data Common Standard Terminology |
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| 논문투고자 |
Daewon Shin, Jaeyoon Lim, Yongkwan Mun, Hoekyung Jung |
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| 논문내용 |
Recently, the number of cases of providing various web services and mobile services at home and
abroad is increasing using public data. It provides information on traditional markets, public facilities,
weather, medical information, public sale, stocks, lasers, real estate, and bicycles, and deep learning
technology is applied to these functions. Through this, rational decision-making and standardized
processes are derived and used for establishing and researching administrative services suitable for the
people. Securing public data is of paramount importance in the use of deep learning technology.
Existing public data is provided as open APIs, standard datasets, and file data(CSV, JSON, XML), and
public data registration by institution is increasing. To utilize data collected through various paths, public
data standardization is required, and to apply the data to the model, manpower is required to convert
the data. In this paper, we propose a model for converting pre-established data according to the
database standardization guidelines of public institutions. The public data common standard terminology
provided by the public data portal was standardized, and items not in the public data common standard
terminology dictionary were converted into public data common standard terminology using the seq2seq
model as a combination of administrative terminology dictionaries. It is believed that the converted data
can be provided as public field data and used in various application cases. |
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