Ontology matching is a solution for managing heterogeneous information. Various methods have been proposed for matching problem. But matching large ontologies is a challenge yet. In this paper scalable ontology matching based on partitioning is proposed. The proposed method is consisted of three stages. At first, input ontologies are partitioned separately. In second stage anchors are identified in each partition pairs of two ontologies and then by using anchor distribution, similar partition pairs are identified. At third stage, concepts of each similar partition pairs are classified based on type of concept. Then scores of importance of concept are computed and arranged in descending order. Finally, matching is performed on concepts of similar partitions pair with same concept types. Name similarity metric and WordNet dictionary are used for matching operation. Experimental results show that the proposed method achieves relatively high precision and it competes with previously proposed partition-based strategies.