A query-based cross-language diagnosis tool for distributed decision making support

  • Authors:

  • Pilsung Choe;Mark R. Lehto;Hyung Jun Park;Jan Allebach

  • Affiliations:

  • School of Industrial Engineering, Purdue University, 259 Grissom Hall, 315 N. Grant Street, West Lafayette, IN 47907, USA;School of Industrial Engineering, Purdue University, 259 Grissom Hall, 315 N. Grant Street, West Lafayette, IN 47907, USA;School Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA;School Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA

  • Venue:
  • Computers and Industrial Engineering
  • Year:
  • 2009

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Abstract

A query translation-based Korean-English cross-language diagnosis (Q-KE-CLD) tool for assisting Korean users diagnosing print defects was developed and then evaluated as a case study of distributed decision making support for nonnative English users. The first step in developing the Q-KE-CLD tool involved collecting and analyzing print defect descriptions in Korean and English. A fuzzy Bayesian model was obtained from the descriptions and the Q-KE-CLD tool was developed. The tool was then experimentally evaluated in four different universities in South Korea. Results showed that Korean subjects generated Korean queries faster (p=0.008) when entering Korean queries. In addition, the subjects rated Korean queries as being easier to generate (p=0.004). Untrained subjects reported that use of the Korean language made it easier to generate queries and identify print defects. The overall results suggested that query translation-based cross-language diagnosis is a feasible approach for localizing troubleshooting websites.