Labeling design documents based on operators' consensus-A case study of robotic design

Sun Jie*, Lu Wen Feng, Loh Han Tong

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Designers usually begin with a database to look for historical design solution, available experience and techniques through design documents, when initiating a new design. This database is a collection of labeled design documents under a few of predefined categories. However, little work has been done on labeling a relatively small number of design documents for information organization, so that most of design documents in this database can be automatically categorized. This paper initiates a study on this topic and proposes a methodology in four steps: design document collection, documents labeling, finalization of documents labeling and categorization of design database. Our discussion in this paper focuses on the first three steps. The key of this method is to collect relatively small number of design documents for manual labeling operation, and unify the effective labeling results as the final labels in terms of labeling agreement analysis and text classification experiment. Then these labeled documents are utilized as training samples to construct classifiers, which can automatically give appropriate labels to each design document. With this method, design documents are labeled in terms of the consensus of operators' understanding, and design information can be organized in a comprehensive and universally accessible way. A case study of labeling robotic design documents is used to demonstrate the proposed methodology. Experimental results show that this method can significantly benefit efficient design information search.

Original languageEnglish
Pages (from-to)66-74
Number of pages9
JournalComputers in Industry
Volume61
Issue number1
DOIs
Publication statusPublished - Jan 2010
Externally publishedYes

Keywords

  • Hypothesis test
  • Knowledge retrieval
  • Product design
  • Text classification

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