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Learning domain taxonomies: the TaxoLine approach

Omar El Idrissi Esserhrouchni (Université Moulay Ismail Ecole Nationale Supérieure d’Arts et Metiers (ENSAM), Morocco)
Bouchra Frikh (Université Sidi Mohamed Ben Abdallah Ecole Supérieur de Technologie (ESTF)–LTTI Lab Fès, Morocco)
Brahim Ouhbi (Université Moulay Ismail Ecole Nationale Supérieure d’Arts et Metiers (ENSAM), Meknès, Morocco)
Ismail Khalil Ibrahim (Department of Telecooperation, Johannes Kepler University Linz, Linz, Austria)

International Journal of Web Information Systems

ISSN: 1744-0084

Article publication date: 21 August 2017




The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically.


TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology.


The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods.


The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.



El Idrissi Esserhrouchni, O., Frikh, B., Ouhbi, B. and Ibrahim, I.K. (2017), "Learning domain taxonomies: the TaxoLine approach", International Journal of Web Information Systems, Vol. 13 No. 3, pp. 281-301.



Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

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