Decision trees in automatic ontology matching Online publication date: Mon, 16-Jan-2017
by Siham Amrouch; Sihem Mostefai; Muhammad Fahad
International Journal of Metadata, Semantics and Ontologies (IJMSO), Vol. 11, No. 3, 2016
Abstract: The semantic web progress gave rise to a growing number of ontologies in various fields. In order to allow knowledge reuse, ontology matching is an interesting option. In this paper, we propose an ontology matching system that performs class, property and instance matching. This latter is usually achieved by means of Natural Language Processing (NLP) techniques, which are context dependent. To avoid the limits of NLP, we use a decision tree-based instance matching scheme. Decision tree is one of the most widely used learning algorithms for inductive inference and classification. Our system works on OWL-DL ontologies, that is ontologies expressed in the Description Logic version of the Ontology Web Language. This ensures the maximum of expressiveness, completeness and decidability. Our approach is tested with the benchmark and conference tracks of the OAEI'2015 campaign. It shows very promising results since it outperforms other matching systems in most of the test cases.
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