Similarity metrics analysis for principal concepts detection in ontology creation

Issue title: Special Section: Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering

Guest editors: David Pinto and Vivek Singh

Article type: Research Article

Authors: Alemán, Yuridiana | Somodevilla, María J.^{; *} | Vilariño, Darnes

Affiliations: Faculty of Computer Science, Benemérita Universidad Autónoma de Puebla (BUAP), México

Correspondence: [*] Corresponding author. María J. Somodevilla. E-mail: [email protected]. Tel: +52 2222295500, Ext 7228

Abstract: In this paper an analysis, based on similarity metrics, was carried out in order to detect main concepts related to the superclasses in a pedagogical domain ontology. A semi-automatic corpus containing articles in Spanish was built. Afterward, the corpus was lemmatized and three representations were extracted. Four textual similarity metrics based on terms and Pointwise Mutual Information were implemented. A list of words, which was evaluated using a gold standard built by an expert in the domain, was retrieved from each experiment according to establish thresholds for the metrics. Precision and recall were used for evaluation step, where a detailed discussion by representation and class was presented. Results showed a higher precision in types of intelligences class and 5-grams representation.

Keywords: Ontology learning, pedagogical domain, NLP.

DOI: 10.3233/JIFS-179024

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4753-4764, 2019