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A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases

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  • Shouhui Pan
  • Li Wang
  • Kaiyi Wang
  • Zhuming Bi
  • Siqing Shan
  • Bo Xu

Abstract

Consumer product safety closely relates to consumer health. In this paper, a knowledge engineering framework is proposed for data mining to identify key safety factors from a large number of consumer product safety cases. Data mining in the framework is performed in three steps. The first step is to collect consumer product safety cases, a case can be semistructured or unstructured, and cases can be collected either manually or automatically by a web spider crawling certain websites. The second step is to extract all safety factors from a number of consumer product safety cases. A new method based on linear chain conditional random field is developed to extract safety factors. The effectiveness of the method has been validated on product cases. The third step is to identify a set of key factors from all safety factors by knowledge reasoning. To illustrate the process of knowledge reasoning, a set of 3192 safety cases of electric products with electric shock accidents is chosen as the case study; a Bayesian network based model is developed to retrieve key safety factors relating to electric shock accidents. The performance of the reasoning model has been verified by a combination of experts' evaluation and experiments, and it has shown the proposed reasoning model can help identify key safety factors of electric shock accidents successfully. Overall, the proposed framework is capable of identifying key safety factors from a large number of consumer product safety cases. Copyright © 2014 John Wiley & Sons, Ltd.

Suggested Citation

  • Shouhui Pan & Li Wang & Kaiyi Wang & Zhuming Bi & Siqing Shan & Bo Xu, 2014. "A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety‐Related Accident Cases," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(3), pages 383-397, May.
    • Handle: RePEc:bla:srbeha:v:31:y:2014:i:3:p:383-397
    DOI: 10.1002/sres.2278
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    References listed on IDEAS

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    Cited by:

    1. Lixin Shen & Hong Wang & Li Da Xu & Xue Ma & Sohail Chaudhry & Wu He, 2016. "Identity management based on PCA and SVM," Information Systems Frontiers, Springer, vol. 18(4), pages 711-716, August.
    2. Shouhui Pan & Kaiyi Wang & Li Wang & Zhibin Wang & Yanyun Han, 2017. "Risk Assessment System Based on WebGIS for Heavy Metal Pollution in Farmland Soils in China," Sustainability, MDPI, vol. 9(10), pages 1-22, October.

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