About ontology matching approach based on adaptive machine learning
DOI:
https://doi.org/10.15587/1729-4061.2011.1060Abstract
The problem of elimination of heterogeneity among different ontologies is considered. General description of the main ontology matching approaches is given. The optimal by performance algorithm for automatic ontology matching, based on principles of artificial neural network learning, is presented
References
- Huang J. Ontology matching using an artificial neural network to learn weights / J. Huang, J. Dang, J.M. Vidal, M.N. Huhns // Proc. IJCAI Workshop on Semantic Web for Collaborative Knowledge Acquisition (SWeCKa-07). – Hyderabad, India, January 2007. – P. 80-85.
- Huang J. Use artificial neural network to align biological ontologies / J. Huang, J. Dang, M.N. Huhns, W.J. Zheng // BMC Genomics. – 2008. – Vol. 9 (Suppl. 2): S16.
- Widrow B. 30 years of adaptive neural networks: perceptron, adaline and backpropagation / B. Widrow, M. Lee //Proc. IEEE. – 1990. – 78. – P. 1415-1442.
- Kaczmarz S. Approximate solution of systems of linear equations / S. Kaczmarz // Intern. J. Control. – 1993. – Vol. 57, No 6. – P. 1269-1271.
- Райбман Н.С. Построение моделей процессов производства [Текст] / Н.С. Райбман, В.М. Чадеев. – М.: Энергия, 1975. – 376 с.
- Бодянский Е.В. Адаптивное обобщенное прогнозирование многомерных случайных последовательностей [Текст] / Е.В. Бодянский, И.П. Плисс, Т.В. Соловьева // Доклады АН УССР. – 1989. – А., № 9. – С.73-75.
- Bodyanskiy Ye. Adaptive generalized forecasting of multivariate stochastic signals / Ye. Bodyanskiy, I. Pliss // Proc. of Latvian Sign. Process. Int. Conf. – Riga, 1990. – Vol.2. – P. 80-83.
- Bodyanskiy Ye. An adaptive learning algorithm for a neuro-fuzzy network / Ye. Bodyanskiy, V. Kolodyazhniy, A. Stephan // Ed. by B. Reusch. Сomputational Intelligence. Theory and Application. – Berlin-Heidelberg-New York: Springer, 2001. – P. 68-75.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2014 Евгений Владимирович Бодянский, Наталья Владимировна Рябова, Наталья Алексеевна Волошина
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.
