Ontology Building Using Classification Rules and Discovered Concepts

Henrihs Gorskis, Arkady Borisov


Building an ontology is a difficult and time- consuming task. In order to make this task easier and faster, some automatic methods can be employed. This paper examines the feasibility of using rules and concepts discovered during the classification tree building process in the C4.5 algorithm, in a completely automated way, for the purposes of building an ontology from data. By building the ontology directly from continuous data, concepts and relations can be discovered without specific knowledge about the domain. This paper also examines how this method reproduces the classification capabilities of the classification three within an ontology using concepts and class expression axioms.


Building; classification tree; ontology

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A. Nicola, M. Missikoff, R. Navigli, “A software engineering approach to ontology building. Information Systems,” vol. 34, issue 2, April 2009, pp. 258–275. http://dx.doi.org/10.1016/j.is.2008.07.002

H. Gorskis, J. Čižovs, “Ontology Building Using Data Mining Techniques,” Information Technology and Management Science, vol. 15, 2012, pp.183–188. http://dx.doi.org/10.2478/v10313-012-0024-5

I. Polaka, A. Kirshners, H. Gorskis, M. Leja, “The use and modification of decision tree classification algorithm for gastric cancer risk stratification,” Expert Systems with Applications. Submitted for publication, 2015.

F. Kharbat, H. Ghalayini, “New Algorithm for Building Ontology from Existing Rules: A Case Study,” In: Information Management and Engineering. ICIME '09, 2009, pp. 12–16. http://dx.doi.org/10.1109/icime.2009.16

A. Elsayed, S.R. El-beltagy, M. Rafea, O. Hegazy, “Applying data mining for ontology building,” In: Proc. of the 42nd Annual Conf. on Statistics, Computer Science and Operations Research, 2007.

F. Saïs, R. Thomopoulos, “Ontology-aware prediction from rules: A reconciliation-based approach,” Knowledge-Based Systems, vol. 67, Sept. 2014, pp. 117–130. http://dx.doi.org/10.1016/j.knosys.2014.05.023

D. Kang, M. Kim, “Propositionalized attribute taxonomies from data for data-driven construction of concise classifiers,” Expert Systems with Applications, vol. 38, issue 10, 15 September 2011, pp. 12739–12746. http://dx.doi.org/10.1016/j.eswa.2011.04.062

R. Thomopoulos, S. Destercke, B. Charnomordic, L. Johnson, J. Abécassis, “An iterative approach to build relevant ontology-aware data-driven models,” Information Sciences, vol. 221, 1 Feb. 2013, pp. 452–472. http://dx.doi.org/10.1016/j.ins.2012.09.015

H. Wimmer, R. Rada, “Good versus bad knowledge: Ontology guided evolutionary algorithms,” Expert Systems with Applications, vol. 42, issue 21, 30 Nov. 2015, pp. 8039–8051. http://dx.doi.org/10.1016/j.eswa.2015.04.064

Y. Kassahun, R. Perrone, E. De Momi, E. Berghöfer, L. Tassi,

M.P. Canevini, R. Spreafico, G. Ferrigno, F. Kirchner, “Automatic classification of epilepsy types using ontology-based and genetics-based machine learning” Artificial Intelligence in Medicine, vol. 61, issue 2, June 2014, pp. 79–88. http://dx.doi.org/10.1016/j.artmed.2014.03.001

Y. Kuo, A. Lonie, L. Sonenberg, K. Paizis, “Domain ontology driven data mining: a medical case study,” Proc. of the 2007 int.workshop on Domain driven data mining, 2007, pp. 11–17. http://dx.doi.org/10.1145/1288552.1288554

J. Hastings, D. Magka, C. Batchelor, L. Duan, R. Stevens, M. Ennis, C. Steinbeck, “Structure-based classification and ontology in chemistry,” Journal of Cheminformatics, 2012. http://dx.doi.org/10.1186/1758-2946-4-8


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