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http://dx.doi.org/10.14346/JKOSOS.2022.37.1.41

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

Park, Sang I. (Research Institute for Safety Performance, Korea Authority of Land & Infrastructure Safety)
Kwon, Tae-Ho (School of Civil & Environmental Engineering, Yonsei University)
Park, Junwon (Department of Civil Engineering, Myongji College)
Seo, Kyung-Wan (School of Civil & Environmental Engineering, Yonsei University)
Yoon, Young-Cheol (Department of Civil Engineering, Myongji College)
Publication Information
Journal of the Korean Society of Safety / v.37, no.1, 2022 , pp. 41-48 More about this Journal
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 schema-matching method showed superiority in terms of accuracy and efficiency compared with the previous study on XML schema matching for bridge engineering documents.
Keywords
bridge engineering document; XML; schema matching; artificial neural network; weight factor; matching accuracy;
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Times Cited By KSCI : 2  (Citation Analysis)
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