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Evaluate the Performance of Ontology Alignment by Providing a Novel Machine Learning Technique

Foruzan Amiri Davani, Bita Shadgar, Alireza Osareh
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Abstract

Wide Web as one of the greatest achievements of technology, the economy, and more substantially influenced modern societies. ¬ The present situation is not satisfactory because many of the essential tasks of daily living are not supported by automated tools. Most of today's Web content designed for human use, while ¬ machines are only capable peers and manipulates data in the dictionary. In this regard, the concept of the Semantic Web has been proposed that the data be ¬ computers is incomprehensible. Help ontology semantic technologies such as machine can understand and be smart. Powerful tool to show and express knowledge in a specific domain ontology, the format is informal and can be processed by machines. Following this finding correspondences between ontology entities using machine learning techniques. This similarity indices calculated at various levels of the ontology pairs, then sets of data alignment is to classify products .

References

Shadgar B. Asareh AR, Haratian Nejadi A. Semantec Web: Concepts and techniques. 2010, Armaghan Publications: Tehran.

Cruz F, Alessio Fabiani A. Automatic Configuration Selection Using Ontology Matching Task Profiling, 17th European Conference on Artificial Intelligence, International Workshop on Context and Ontologies (C&O' 2008), 2008. Trento, ITALY.

Bagheri Hariri B, Sayyadi H, Abolhassani H. A Neural-Networks- Based Approach for Ontology Alignment. Joint 3rd International Conference on Soft Computing and Intelligent Systems and 7th International Symposium on advanced Intelligent Systems, 2006, JAPAN.

Mao M. Ontology Mapping: Towards Semantic Interoperability in Distributed and Heterogeneous Environment. [Ph.D. Thesis] 2008.

Ichise R. Machine Learning Approach for Ontology Mapping using Multiple Concept Similarity Measures. ICIS. 2008.

Eckert K, Meilicke C, Stuckenschmidt H. Improving Ontology Matching using Meta-level Learning. ESWC. 2009.

Svab O, Svatek V. Combining ontology mapping methods using Bayesian networks. the ISWC 2006 Workshop on Ontology Matching, 2006, Athens, USA.

Jérôme DA, Jérôme Euzenat A. The Alignment API 4.0. Springer-Verlag Berlin, HeidelbergUndefined: 2010.

Langley P, Sage S. Induction of Selective Bayesian Classifiers. 10th Conf. on Artificial Intelligence, 1994.

Mahmoodi A, Naganjaneyulu S, Mrithyumjaya R. An Overview of Recent and Traditional Decision Tree Classifiers in Machine Learning. International Journal of Research and Reviews in Ad Hoc Networks, 2011;1(1).

Lavanya D, Usha Rani K. Performance Evaluation of Decision Tree Classifiers on Medical Datasets. International Journal of Computer Applications. 2011;26(4): 0975 – 8887.

Vapnik V. Statistical Learning Theory. John Wiley & Sons, New York: 1998.

Olariu S, Zomaya AY. Handbook of Bioinspired Algorithms and Applications. Taylor & FrancisGroup, LLC Press: 2006.

Ontology Alignment Evaluation Initiative. 2012. Available at: http://oaei.ontologymatching.org.


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