Proceedings of the International Conference on
Product Lifecycle Management    PLM'08
Fostering the culture of innovation
PLM-SP4, 2008
 
(from Chapter 2: Frameworks and Reference Models)

 Full Citation and Abstract

Title: Semantic combination of matching methods for product data interoperability
  Author(s): Il Yeo, Lalit Patil, Debasish Dutta
  Address: Department of Mechanical Engineering University of Michigan 2350 Hayward St., 2250 G.G. Brown Ann Arbor, MI 48109, USA
yeoil @ umich.edu, lpatil @ umich.edu, dutta @ umich.edu
  Reference: PLM-SP4 - 2008 Proceedings  pp. 167 - 176
  Abstract/
Summary
Determining semantic correspondences across heterogeneous product representations is critical for seamless translation and, thereby, for integration in PLM. Multiple attributes are used to determine semantic similarities using different metrics or methods. However, there is no formal basis to define a correct approach, and the results are not reliable. Therefore, there is a need to consider a variety of features along with the variety of methods to approximate similarity. In this paper, we demonstrate the need for nonlinear combination of the matching approaches to reduce uncertainties in semantic mapping. We demonstrate the use of Support Vector Regression (SVR), to capture the nonlinear interrelationships and find semantic maps between product assembly ontologies in disparate representations.
 
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