  Ontology matching finds correspondences between similar entities of different
ontologies. Two ontologies may be similar in some aspects such as structure,
semantic etc. Most ontology matching systems integrate multiple matchers to
extract all the similarities that two ontologies may have. Thus, we face a
major problem to aggregate different similarities. Some matching systems use
experimental weights for aggregation of similarities among different matchers
while others use machine learning approaches and optimization algorithms to
find optimal weights to assign to different matchers. However, both approaches
have their own deficiencies. In this paper, we will point out the problems and
shortcomings of current similarity aggregation strategies. Then, we propose a
new strategy, which enables us to utilize the structural information of
ontologies to get weights of matchers, for the similarity aggregation task. For
achieving this goal, we create a new Ontology Matching system which it uses
three available matchers, namely GMO, ISub and VDoc. We have tested our
similarity aggregation strategy on the OAEI 2012 data set. Experimental results
show significant improvements in accuracies of several cases, especially in
matching the classes of ontologies. We will compare the performance of our
similarity aggregation strategy with other well-known strategies
