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Text analysis for constructing design representations
Affiliation:1. Department of Materials Engineering, The University of British Columbia, 309-6350 Stores Road, Vancouver, BC V6T 1Z4, Canada;2. Materials Science & Engineering Department, University of Washington, USA;1. Composites Research Network, Departments of Civil Engineering and Materials Engineering, The University of British Columbia, Vancouver, B.C. V6T 1Z4, Canada;2. Composite Vehicle Research Center, Department of Mechanical Engineering, Michigan State University, 2555 Engineering Building, East Lansing, MI 48824, United States;1. Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA;2. William E. Boeing Department of Aeronautics & Astronautics, University of Washington, Seattle, WA 98195-2400, USA
Abstract:An emerging model in concurrent product design and manufacturing is the federation of workgroups across traditional functional ‘silos’. Along with the benefits of this concurrency comes the complexity of sharing and accessing design information. The primary challenge in sharing design information across functional workgroups lies in reducing the complex expressions of associations between design elements. Collaborative design systems have addressed this problem from the perspective of formalizing a shared ontology or product model. We share the perspective that the design model and ontology are an expression of the ‘meaning’ of the design and provide a means by which information sharing in design may be achieved. However, in many design cases, formalizing an ontology before the design begins, establishing the knowledge sharing agreements or mapping out the design hierarchy is potentially more expensive than the design itself. This paper introduces a technique for inducing a representation of the design based upon the syntactic patterns contained in the corpus of design documents. The association between the design and the representation for the design is captured by basing the representation on terminological patterns at the design text. In the first stage, we create a ‘dictionary’ of noun-phrases found in the text corpus based upon a measurement of the content carrying power of the phrase. In the second stage, we cluster the words to discover inter-term dependencies and build a Bayesian belief network which describes a conceptual hierarchy specific to the domain of the design. We integrate the design document learning system with an agent-based collaborative design system for fetching design information based on our ‘smart drawings’ paradigm.
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