The proliferation of biomedical applications, which rely on different knowledge organization systems, such as ontologies and thesauri raises the issue of the automated identification of the correspondences between these models, in particular for the data integration need. A significant effort has been conducted for tackling this issue of ontology alignment. However, few systems are able to deal with ontologies containing tens of thousands of entities, as it may be the case in the biomedical domain where resources such as SNOMED-CT, the FMA or the NCI thesaurus are commonly used. We present in this paper ServOMap, an efficient system for large-scale ontology alignment. It relies on an Ontology Server (ServO) and uses Information Retrieval techniques for computing similarity between entities. The system participated with two configurations in the 2012 Ontology Alignment Evaluation Initiative campaign. We report the very promising results obtained by the system for large biomedical ontologies alignment. ServOMap is freely available for download at servo/.