Ontology Mapping based on Similarity Measure and Fuzzy Logic
Suphakit Niwattanakul, L3i, University of La Rochelle, France ; Philippe Martin, Griffith University, Australia ; Michel Eboueya, L3i, University of La Rochelle, France ; Kanit Khaimook, Suranaree University of Technology, Thailand
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Quebec City, Canada ISBN 978-1-880094-63-1 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
In this paper, we present a method of an ontology mapping based on a similarity measure and Fuzzy logic in order to classify (i) the similarity of the ontology structure of learning object repositories and (ii) LOR which stores metadata of learning objects based on our ontology model. In this model, values of the ontology similarity are computed for concepts, properties, and relations. The ontology similarity uses parameters based on the Fuzzy Control Language (FCL) which consists of a fuzzy set of the ontology similarity (“Less”, “Same”, “More”), 7 classes of ontology similarity, and rules of the classification of ontologies. The formula of similarity measure by the Jaccard's coefficient is applied to map a similarity of ontology structures. At the end of the article, we show an experience of implementation this model as a prototype.
Niwattanakul, S., Martin, P., Eboueya, M. & Khaimook, K. (2007). Ontology Mapping based on Similarity Measure and Fuzzy Logic. In T. Bastiaens & S. Carliner (Eds.), Proceedings of E-Learn 2007--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 6383-6387). Quebec City, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved March 26, 2019 from https://www.learntechlib.org/primary/p/26800/.
© 2007 Association for the Advancement of Computing in Education (AACE)
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