Learning the "Whys": Discovering design rationale using text mining - An algorithm perspective

  • Authors:

  • Yan Liang;Ying Liu;Chun Kit Kwong;Wing Bun Lee

  • Affiliations:

  • Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China;Department of Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore;Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China;Department of Industrial and Systems Engineering, Hong Kong Polytechnic University, Hong Kong SAR, China

  • Venue:
  • Computer-Aided Design
  • Year:
  • 2012

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Abstract

Collecting design rationale (DR) and making it available in a well-organized manner will better support product design, innovation and decision-making. Many DR systems have been developed to capture DR since the 1970s. However, the DR capture process is heavily human involved. In addition, with the increasing amount of DR available in archived design documents, it has become an acute problem to research a new computational approach that is able to capture DR from free textual contents effectively. In our previous study, we have proposed an ISAL (issue, solution and artifact layer) model for DR representation. In this paper, we focus on algorithm design to discover DR from design documents according to the ISAL modeling. For the issue layer of the ISAL model, we define a semantic sentence graph to model sentence relationships through language patterns. Based on this graph, we improve the manifold-ranking algorithm to extract issue-bearing sentences. To discover solution-reason bearing sentences for the solution layer, we propose building up two sentence graphs based on candidate solution-bearing sentences and reason-bearing sentences respectively, and propagating information between them. For artifact information extraction, we propose two term relations, i.e. positional term relation and mutual term relation. Using these relations, we extend our document profile model to score the candidate terms. The performance and scalability of the algorithms proposed are tested using patents as research data joined with an example of prior art search to illustrate its application prospects.