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Research Papers

Knowledge Representation and Ontology Mapping Methods for Product Data in Engineering Applications

[+] Author and Article Information
Pei Zhan

VRCIM Laboratory, School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164zhanpei@hotmail.com

Uma Jayaram

VRCIM Laboratory, School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164ujayaram@wsu.edu

OkJoon Kim

VRCIM Laboratory, School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164ojkim@mail.wsu.edu

Lijuan Zhu

VRCIM Laboratory, School of Mechanical and Materials Engineering, Washington State University, Pullman, WA 99164zhuliice@wsu.edu

J. Comput. Inf. Sci. Eng 10(2), 021004 (Apr 22, 2010) (11 pages) doi:10.1115/1.3330432 History: Received August 28, 2008; Revised October 20, 2009; Published April 22, 2010; Online April 22, 2010

This paper presents a semantic approach that uses ontologies to share knowledge related to product data in CAD/CAE applications and for integrating the design evaluation information that these applications individually provide. Our overall approach is the ontology-based adaptive design evaluation, also coined as OADE. This paper reports a piece of our ongoing work in the area of knowledge representation and ontology mapping methods. Here we design ontologies for representing product design and analysis, instantiate a source ontology with the product data, create formal ontology mapping methods, and then apply these methods to transfer the product data from the source ontology to the target one. A prototype implementation has been created using technologies such as OWL (representation language), JENA (ontology API), and PROTÉGÉ (ontology editor) to demonstrate the approach for integrating product design and assembly simulation analysis applications. This work is significant because heuristic methods based on geometry attributes, composition, and inheritance for determining mapped concepts in engineering ontologies is still very new, and not much work has been done in this area. This work will lead to the ability to create, share, and exchange knowledge for solving design evaluation challenges involving multiple applications and viewpoints.

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Copyright © 2010 by American Society of Mechanical Engineers
Topics: Ontologies , Design
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References

Figures

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Figure 1

Key elements of semantic level integration for product data

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concept level and instance level of product data semantics

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Three-tier structure of engineering ontologies

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Figure 4

Two way to create a cylinder feature

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Figure 5

Composition similarity of nodes in two paths

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Figure 6

Example of mapping based on geometry data conversion

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Figure 7

Calculating attribute similarity

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Figure 8

Composition similarity mapping

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Figure 9

Inheritance hierarchies for two matched concepts

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Figure 10

Test assembly to demonstrate approach

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Figure 11

Concepts and relations represented in PRO-AO using PROTÉGÉ

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