An Ontology Alignment Hybrid Method Based on Decision Rules

N 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 MatcherBiomedical Ontologies (YAM-BIO), the proposed method gives good results (good recall, precision and F-measure). Experimental results show that the proposed approach is effective.