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.
| Identifer | oai:union.ndltd.org:PITT/oai:PITTETD:etd-04062010-184159 |
| Date | 12 May 2010 |
| Creators | Peng, Yefei |
| Contributors | Michael Spring, Bambang Parmanto, Hassan Karimi, Daqing He, Paul Munro |
| Publisher | University of Pittsburgh |
| Source Sets | University of Pittsburgh |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.pitt.edu/ETD/available/etd-04062010-184159/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to University of Pittsburgh or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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