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Extraction, Transformation and Loading (ETL) is a key process of data warehouse building. It integrates data sources with diverse features and structures. Numerous approaches and implementations of ETL have been introduced. However, they still have the following disadvantages: human-dependence, information integration only in syntactic levels, incomplete the homogeneity solution, difficulty to install and configure, etc. In this paper, we propose an alternative approach to the ETL process by attacking the homogeneity in data sources with an ontology-based methodology. Our approach can overcome the drawbacks of most existing approaches; as it automates the key activities of the process, such as: extraction of metainformation, generation of logical and physical data models, and transformation of information.