Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution. However, the process of matching large ontologies has high space and time complexities. Therefore, for a tool to efficiently and accurately match these large ontologies within the limited computing resources, it must have techniques that can significantly reduce the high space and time complexities associated with the ontology matching process. This article provides a review of the state-of-the-art techniques being applied by ontology matching tools to achieve scalability and produce high-quality mappings when matching large ontologies. In addition, we provide a direct comparison of the techniques to gauge their effectiveness in achieving scalability. A review of the state-of-the-art ontology matching tools that employ each strategy is also provided. We also evaluate the state-of-the-art tools to gauge the progress they have made over the years in improving alignment’s quality and reduction of execution time when matching large ontologies.