Using ontologies for interoperability of data cleaning operations

The emergence of new business models, namely the establishment of partnerships between organizations, the possibility of companies to add existing data on the web, especially in the semantic web, to their information increase some problems already existing in the databases, particularly related to data quality. Poor data can lead to loss of competitiveness of the organizations holding these data and may even lead to their disappearance, since many of their decision-making are based on them. This makes data cleaning an essential process. The currently existing approaches to solve these problems are closely related with database schemas and specific domains. In order to use this process in different repositories, it is necessary that machines understand these data, i.e., it is necessary an associated semantic. The solution presented includes the use of ontologies: (i) for the specification of data cleaning operations and, (ii) as a way of solving the semantic heterogeneity problems of data stored in different databases. With the cleaning operations defined at the conceptual level and existing mappings between domain ontologies and an ontology associated with a database, they may be instantiated and then proposed to the user to be executed over that database, thus enabling their interoperability.