Journal of Digital Information Management Nadia Alboukaey, Ammar Joukhadar 16 1 2018 https://doi.org/10.6025/jdim/2018/16/1/33-42 http://dline.info/fpaper/jdim/v16i1/jdimv16i1_4.pdf Ontology matching is one of the most important works to achieve the goal of the semantic web. To fulfill this task, element-level matching is an indispensable step to obtain the fundamental alignment. In element-level matching process, previous work generally utilizes multiple measures to compute the similarities among elements, and then combine these similarities using a weighted sum formula to determine later the semantic correspondences. The proper selection and combination of these similarities strongly influence the final quality of the matching system. In this paper, we introduce element-level ontology matcher as machine learning system which utilizes regression algorithms to automatically find many possible combination relations of similarity measures. We adopt different similarity measures to extract learning features, and train the system on sample data from Benchmark and Conference tracks OAEI 2015. To match two ontologies, after training, we measure different similarities for each entity pair, and predict the overall similarity of each pair using the learned regression model to get similarity matrix. After that we extract correspondences by applying naive descending algorithm on the similarity matrix. These correspondences are filtered by previously known semantic techniques to enhance matching precision while preserving matching recall. Experimental results, using dataset in conference track from OAEI 2015, show that our extracted similarity features are efficient in terms of f-measure evaluation criteria, and outperform the widely used measures in ontology matching systems. Moreover, our method for similarity combination which depends on regression model outperforms the present combination methods. Besides, in comparison to the matching systems participated in OAEI 2015 campaign, our matching system showed to be highly competitive and had a high ranking position.