During the last decades data integration has been a challenge for applications processing multiple heterogeneous data sources. It has been faced across the domains of schemas, ontologies, and metamodels, inevitably imposing the need for mapping specifications. Support for the development of such mappings has been researched intensively, producing matching systems that automatically propose mapping suggestions.
Since an overall relation between these systems is missing, we present a comparison and overview of 15 systems for schema, ontology, and metamodel matching. Thereby, we pursue a structured analysis of applied state-of-the art matching techniques and the internal models of matching systems.
The result is a comparison of matching systems, highlighting their commonalities and differences in terms of matching techniques and used information for matching, demonstrating significant similarities between the systems. Based on this, our work also identifies possible knowledge sharing between the domains, e.g. by describing techniques adoptable from another domain.