Journal of the Korean Society of Safety (한국안전학회지)

The Korean Society of Safety (한국안전학회)
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Artificial Neural Network-based Weight Factor Determination Method for the Enhanced XML Schema Matching of Bridge Engineering Documents

교량 건설 문서의 강화된 XML 스키마 매칭을 위한 인공신경망 기반의 요소 가중치 선정 방안

  • 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)
  • 박상일 (국토안전관리원 안전성능연구소) ;
  • 권태호 (연세대학교 건설환경공학과) ;
  • 박준원 (명지전문대학 토목공학과) ;
  • 서경완 (연세대학교 건설환경공학과) ;
  • 윤영철 (명지전문대학 토목공학과)
  • Received : 2022.01.18
  • Accepted : 2022.02.21
  • Published : 2022.02.28
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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 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

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1048259).

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