Abstract In this paper, we propose a hybrid approach based on the extraction of decision rules to refine the alignment results due to the use of three alignment strategies. This approach contains two phases: training phase which uses structural similarity, element similarity, instance-based similarity and C4.5 algorithms to extract decision rules, and evaluation phase which refines discovered alignment by three alignment strategies using extracted decision rules. This approach is compared with the best systems according to benchmark OAEI 2016: Framework for Ontology Alignment and Mapping (FOAM), A Dynamic Multistrategy Ontology Alignment Framework (RIMOM), AgreementMakerLight and Yet Another Matcher- Biomedical Ontologies (YAM-BIO), the proposed method gives good results (good recall, precision and F-measure). Experimental results show that the proposed approach is effective.

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[24] Zhang Y., Jin H., Pan L., and Li J., “RiMOM results for OAEI 2016,” in ISWC, Japan, pp. 210- 216, 2016. Mohamed Biniz received his master's degree in business intelligence in 2014 and PhD’s degree in computer science in 2018 from the Faculty of Science and Technology, University Sultan Moulay Sliman Beni Mellal. He is currently a professor at Polydisciplinary Faculty of Beni Mellal. His research activities are located in the area of the semantic web engineering specifically, it deals with the research question of the evolution of ontology, big data, natural language processing, dynamic programming, etc. Mohamed Fakir obtained a degree in Master of Electrical Engineering from Nagaoka University of Technology in 1991 and a PhD degree in electrical engineering from the University of Cadi Ayyad, Morocco. He was a team member in Hitachi Ltd., Japan between 1991 and 1994. He is currently a professor at the Faculty of Science and Technology, University Sultan Moulay Slimane, Morocco. His research interest includes image processing, pattern recognition, semantic web, big data and artificial intelligence.