The identification of alignments between heterogeneous ontologies is one of the main research issues in the semantic web. The manual matching of the ontologies is a complex, time consuming and an error prone task. Therefore, ontology matching systems aims to automate this process. Usually, these systems perform the matching process by combining element and structural level matchers. Selecting the optimal string similarity measure associated with its threshold is an important issue in order to enhance the effectiveness of the element level matcher, which in turn will improve the whole ontology system results. In this paper, we present POMap, an ontology matching system based on a syntactic study covering element and structural levels. For the element level matcher we have adopted the best configuration based on the analysis of the performances of many string similarity measures associated with their thresholds. For the structural level, we have performed a syntactic study on both subclasses and siblings in order to infer the structural similarity. Our proposed matching system is validated and evaluated on the Anatomy, the Conference and the Large Biomedical tracks provided by the benchmark of OAEI 2016 ontology matching campaign.