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Learning Different Concept Hierarchies and the Relations Between them from Classified Data

Learning Different Concept Hierarchies and the Relations Between them from Classified Data

Fernando Benites, Elena Sapozhnikova
Copyright: © 2012 |Pages: 17
ISBN13: 9781466618060|ISBN10: 146661806X|EISBN13: 9781466618077
DOI: 10.4018/978-1-4666-1806-0.ch002
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MLA

Benites, Fernando, and Elena Sapozhnikova. "Learning Different Concept Hierarchies and the Relations Between them from Classified Data." Intelligent Data Analysis for Real-Life Applications: Theory and Practice, edited by Rafael Magdalena-Benedito, et al., IGI Global, 2012, pp. 18-34. https://doi.org/10.4018/978-1-4666-1806-0.ch002

APA

Benites, F. & Sapozhnikova, E. (2012). Learning Different Concept Hierarchies and the Relations Between them from Classified Data. In R. Magdalena-Benedito, M. Martínez-Sober, J. Martínez-Martínez, J. Vila-Francés, & P. Escandell-Montero (Eds.), Intelligent Data Analysis for Real-Life Applications: Theory and Practice (pp. 18-34). IGI Global. https://doi.org/10.4018/978-1-4666-1806-0.ch002

Chicago

Benites, Fernando, and Elena Sapozhnikova. "Learning Different Concept Hierarchies and the Relations Between them from Classified Data." In Intelligent Data Analysis for Real-Life Applications: Theory and Practice, edited by Rafael Magdalena-Benedito, et al., 18-34. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1806-0.ch002

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Abstract

Methods for the automatic extraction of taxonomies and concept hierarchies from data have recently emerged as essential assistance for humans in ontology construction. The objective of this chapter is to show how the extraction of concept hierarchies and finding relations between them can be effectively coupled with a multi-label classification task. The authors introduce a data mining system which performs classification and addresses both issues by means of association rule mining. The proposed system has been tested on two real-world datasets with the class labels of each dataset coming from two different class hierarchies. Several experiments on hierarchy extraction and concept relation were conducted in order to evaluate the system and three different interestingness measures were applied, to select the most important relations between concepts. One of the measures was developed by the authors. The experimental results showed that the system is able to infer quite accurate concept hierarchies and associations among the concepts. It is therefore well suited for classification-based reasoning.

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