Artificial Neural Network-based Weight Factor Determination Method for the Enhanced XML Schema Matching of Bridge Engineering Documents 


Vol. 37,  No. 1, pp. 34-47, Feb.  2022
10.14346/JKOSOS.2022.37.1.41


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  Abstract

Bridge engineering documents have essential contents that must be referenced continuously throughout a structure's entire life cycle, but research related to the quality of the contents is still lacking. XML schema matching is an excellent technique to improve the quality of stored data; however, it takes excessive computing time when applied to documents with many contents and a deep hierarchical structure, such as bridge engineering documents. Moreover, it requires a manual parametric study for matching elements' weight factors, maintaining a high matching accuracy. This study proposes an efficient weight-factor determination method based on an artificial neural network (ANN) model using the simplified XML schema-matching method proposed in a previous research to reduce the computing time. The ANN model was generated and verified using 580 data of document properties, weight factors, and matching accuracy. The proposed ANN-based schemamatching method showed superiority in terms of accuracy and efficiency compared with the previous study on XML schema matching for bridge engineering documents.

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  Cite this article

[IEEE Style]

박상일, 권태호, 박준원, 서경완, 윤영철, "Artificial Neural Network-based Weight Factor Determination Method for the Enhanced XML Schema Matching of Bridge Engineering Documents," Journal of the Korean Society of Safety, vol. 37, no. 1, pp. 34-47, 2022. DOI: 10.14346/JKOSOS.2022.37.1.41.

[ACM Style]

박상일, 권태호, 박준원, 서경완, and 윤영철. 2022. Artificial Neural Network-based Weight Factor Determination Method for the Enhanced XML Schema Matching of Bridge Engineering Documents. Journal of the Korean Society of Safety, 37, 1, (2022), 34-47. DOI: 10.14346/JKOSOS.2022.37.1.41.

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