Ontologies are metadata describing properties of a domain, instance data and relationships between properties, developed for many different purposes. But, they can be different names or properties even within the same domain. Ontology matching can be a solution to these differences. Ontology matching is a method for finding the same things in between existing ontologies by looking at semantic similarities. In this paper, we investigate whether current subgraph mining techniques can be used for ontology matching. In the literature, these subgraph mining techniques have not used for ontology matching, before. With this work, we are introducing subgraph-based approaches to ontology matching methods. For this purpose, we have tested two subgraph mining algorithm, GraMi and Gspan, and used human and adult mouse anatomy ontology. We have compared GraMi, and Gspan algorithms and found that Gspan is better than GraMi about matching results.