Learning domain taxonomies: the TaxoLine approach
Omar El Idrissi Esserhrouchni, Bouchra Frikh, Brahim Ouhbi, Ismail Khalil Ibrahim · International Journal of Web Information Systems · 2017
Purpose The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically. Design/methodology/approach 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. Findings 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. Originality/value 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.