A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety-Related Accident Cases.
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...
| Publicado en: | Systems Research & Behavioral Science Vol. 31; no. 3; pp. 383 - 398 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
May/Jun2014
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=96200837&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 96200837 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10927026 2SN jtl: Systems Research & Behavioral Science issn: 10927026 maglogo: Y pubinfo: dt: May/Jun2014 vid: 31 iid: 3 pid: 480 pub: Wiley-Blackwell artinfo: ui: 96200837 10.1002/sres.2278 ppf: 383 ppct: 15 formats: tig: atl: A Knowledge Engineering Framework for Identifying Key Impact Factors from Safety-Related Accident Cases. aug: au: Pan, Shouhui Wang, Li Wang, Kaiyi Bi, Zhuming Shan, Siqing Xu, Bo affil: Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing China School of Economics and Management, Beihang University, Beijing China Department of Engineering, Indiana University Purdue University Fort Wayne, Fort Wayne IN, USA su: Theory of knowledge Consumer goods Consumers Health Accidents Product safety Random fields Safety sug: subj: Theory of knowledge Consumer goods Consumers Health All Other Consumer Goods Rental Accidents Product safety Random fields Safety keyword: Bayesian network consumer product safety Impact factors named entity recognition Bayesian network consumer product safety Impact factors named entity recognition ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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