Ontology-Based Classification System Development Methodology

Peter Grabusts, Arkady Borisov, Ludmila Aleksejeva


The aim of the article is to analyse and develop an ontology-based classification system methodology that uses decision tree learning with statement propositionalized attributes. Classical decision tree learning algorithms, as well as decision tree learning with taxonomy and propositionalized attributes have been observed. Thus, domain ontology can be extracted from the data sets and can be used for data classification with the help of a decision tree. The use of ontology methods in decision tree-based classification systems has been researched. Using such methodologies, the classification accuracy in some cases can be improved.


Classification; decision tree; ontology; propositionalization; taxonomy

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T.M. Mitchell, Machine learning. McGraw-Hill, 1997, 414 p.

J.R. Quinlan, C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, 1993.

L. Rokach and O. Maimon, Data mining with decision trees: theory and applications. World Scientific Pub Co Inc., 2008.

L. Breiman, J.H. Friedman, R. Olshen and C.J. Stone, Classification and regression trees. Belmont, CA: Wadsworth, 1984.

D. Gašević, D. Djurić and V. Devedžić, Model driven architecture and ontology development. Springer-Verlag, 2006.

F. Kharbat and H. El-Ghalayini, “Building Ontology from Knowledge Base Systems,” in Proc. of Int. Arab Conf. on Information Technology, 2011.

A. Suyama and T. Yamaguchi, “Specifying and learning inductive learning systems using ontologies,” Working Notes from the 1998 AAAI Workshop on the Methodology of Applying Machine Learning: Problem Definition, Task Decomposition and Technique Selection, pp. 29–36, 1998.

N. Guarino, “Formal Ontology in Information Systems,” in 1st Int. Conf. on Formal Ontology in Information Systems, FOIS, Trento, Italy, IOS Press, 1998, pp. 3–15.

R. Kohavi and F. Provost, “Applications of data mining to electronic commerce,” in Applications of data mining to electronic commerce, US: Springer, Data Mining Knowledge Discovery 5/1-2, 2001, pp. 5–10. http://dx.doi.org/10.1023/A:1009840925866

T.R. Gruber, “A translation approach to portable ontologies,” Knowledge Acquisition, vol. 5(2), pp. 199–220, 1993. http://dx.doi.org/10.1006/knac.1993.1008

D. Kang and M. Kim, “Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers,” Expert Systems with Applications, vol. 38, no. 10, pp. 12739–12746, 2011. http://dx.doi.org/10.1016/j.eswa.2011.04.062

D. Kang and K. Sohn, “Learning decision trees with taxonomy of propositionalized attributes,” Pattern Recognition, vol. 42, no. 1, pp. 84– 92, 2009. http://dx.doi.org/10.1016/j.patcog.2008.07.009

J.R. Quinlan, “Induction of Decision Trees,” Machine learning, vol. 1, no. 1, pp. 81–106, 1986. http://dx.doi.org/10.1007/BF00116251

“Matlab: The language of technical computing,” [Online]. Available: http://se.mathworks.com/products/matlab/ [Accessed: September 30, 2015].

“UCI Machine learning repository: Iris data set,” [Online]. Available: https://archive.ics.uci.edu/ml/datasets/Iris [Accessed: September 30, 2015].

“Weka: Data mining software,” [Online]. Available: http://www.cs.waikato.ac.nz/ml/weka/ [Accessed: September 30, 2015].


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