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With increasing data volume, automatically sharing data becomes more valuable because it decreases storage requirements by reducing data replication. Database integration is an important problem to address in current real world applications. The schema integration problem is as follows: given a group of schemas, the goal is to create a merged schema that represents all of the underlying schemas. The construction of the unified schema should not result in loss of information, and it should grant the capability to query the underlying databases either individually or collectively. Integration of multiple heterogeneous data sources usually includes an initial step to combine the schemas of the sources so that they form a unified view, which can be used to give users the illusion (and simplicity) of interacting with one single target combined from all the sources. Users are presented with a uniform logical representation of data that is physically spread over heterogeneous data sources. For this, an integrated schema) has to be created that incorporates all the data contents of the sources. Although there have been some projects on integration of the relational schemas but there is no universal tool for achieving the schema integration using ontologies.In this work, we propose an approach and develop a tool to achieve relational database schema integration using ontologies. Our focus is on improving the automation for integrating relational database schemas by using ontologies. An ontology represents a shared, explicit specification of a conceptualization of the domain of knowledge [G93]. Our schema integration approach is a combination of three different approaches that are adapted from previous research efforts. First we convert relations in the schema into their respective ontologies written in the ontology language OWL. After creating individual ontologies, we then merge them using ontology merging techniques to obtain a unified single ontology. Finally, the unified ontology is converted into a relational database schema. We develop a semi-automated tool that takes two or more relational schemas as input and converts them into their respective ontologies using pre-defined mapping rules. These ontologies are merged into a single unified ontology and we convert this merged ontology into a relational schema. Our approach is demonstrated using two example data sources and our integration tool RIO. Finally, we evaluate the quality of the merged schemas using the approach proposed by Pavlic et al. [PK+08].