One of the most challenging problems in the area of semantic data integration consists in finding correct semantic mappings between heterogeneous ontologies. Existing methods for automatic matching need to be corrected manually in order to guarantee the quality of generated mappings. The manual revision of mapping rules is a difficult task by itself. It requires expert knowledge of the mapping formalism and the underlying infrastructure and is thus error-prone.

Existing methods for the automatic induction of mapping rules must be corrected manually. This manual editing and adaption of the mapping rules requires a high amount of expert knowledge over the behavior of the rule system and the underlying infrastructure. Despite of this, the procedure is very error-prone.

Within the scope of the project MappingAssistant a tool will be developed, which will solve these problems with a new half automatic approach. This half automatic approach replaces the automatic induction of mapping rules with an interactive process in which the user controls the single induction steps.  The resulting tool will also allow non technical people to perform the data integration of heterogeneous data sources by themselves.

The induction rules will be derived semi-automatically with new methods of artificial intelligence. An intuitive and user-friendly visualization of the rules with the goal of supporting non-technical business experts in the task of revising automatically created mappings will be developed.