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ERIC Number: ED520301
Record Type: Non-Journal
Publication Date: 2010
Pages: 156
Abstractor: As Provided
Reference Count: 0
ISBN: ISBN-978-1-1241-4709-3
Ontology Mapping Neural Network: An Approach to Learning and Inferring Correspondences among Ontologies
Peng, Yefei
ProQuest LLC, Ph.D. Dissertation, University of Pittsburgh
An ontology mapping neural network (OMNN) is proposed in order to learn and infer correspondences among ontologies. It extends the Identical Elements Neural Network (IENN)'s ability to represent and map complex relationships. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The output of one network in response to a stimulus to another network can be interpreted as an analogical mapping. In a similar fashion, the networks can be explicitly trained to map specific items in one domain to specific items in another domain. Representation layer helps the network learn relationship mapping with direct training method. The OMNN approach is tested on family tree test cases. Node mapping, relationship mapping, unequal structure mapping, and scalability test are performed. Results show that OMNN is able to learn and infer correspondences in tree-like structures. Furthermore, OMNN is applied to several OAEI benchmark test cases to test its performance on ontology mapping. Results show that OMNN approach is competitive to the top performing systems that participated in OAEI 2009. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page:]
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Publication Type: Dissertations/Theses - Doctoral Dissertations
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A