By Topic

Ontology Fusion in High-Level-Architecture-Based Collaborative Engineering Environments

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Hongbo Sun ; National Research Council, Centre for Computer-assisted Construction Technologies, London, Canada ; Wenhui Fan ; Weiming Shen ; Tianyuan Xiao

In high-level-architecture (HLA)-based distributed heterogeneous collaborative engineering environments (CEEs), the construction of federation object model files is time consuming. This paper presents an ontology fusion approach aiming at establishing a common understanding in such collaborative environments. The proposed approach has three steps: ontology mapping, ontology alignment, and ontology merging. Ontology mapping employs a top-down approach to explore all bridge relations between two terms from different ontologies based on bridge axioms and deduction rules. Ontology alignment adopts a bottom-up approach to discover implicit bridge relations between two terms from different domain ontologies based on equivalent inference. Ontology merging generates a new collaboration ontology from discovered equivalent bridge relations. It adopts an axiom-based ontology fusion strategy and takes heavy-weighted ontologies into consideration. It can find all the explicit and derived interontology relations. In a typical CEE, the proposed approach has a great potential to improve the efficiency of preparation for HLA-based collaborative engineering processes, reduce the work load for adaptive adjustment of existing platforms, and enhance the reusability and flexibility of CEEs. A case study has been conducted to validate the feasibility of the proposed approach.

Published in:

IEEE Transactions on Systems, Man, and Cybernetics: Systems  (Volume:43 ,  Issue: 1 )