In recent years it has become apparent that schema matching is a labor intensive process that is very costly in resources; this has led to the development of various automated tools to substitute the human experts involved in it. To this end we propose two new ideas. The first is the separation of matching techniques into strong and weak ones, in what we call two phase schema matching. The second is using information a human expert can provide to the system during the process of schema matching, that is used to determine how to combine the various matching techniques. A system encompassing both our ideas is easily tunable and allows the human expert to become part of the matching process and help the system choose the best techniques to use. In extensive experiments we demonstrate that this approach is better than contemporary state of the art systems in relational databases. We also demonstrate that single purpose (or niche) matchers can be helpful in such a system where the system can opt to use them if appropriate.