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Abstract-We present the Auto Mapping Core (AMC), a new framework that supports fast construction and tuning of schema matching approaches for specific domains such as ontology alignment, model matching or database-schema matching. Distinctive features of our framework are new visualisation techniques for modelling matching processes, stepwise tuning of parameters, intermediate result analysis and performanceoriented rewrites. Furthermore, existing matchers can be plugged into the framework to comparatively evaluate them in a common environment. This allows deeper analysis of behaviour and shortcomings in existing complex matching systems.

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Abstract-Mapping complex metadata structures is crucial in a number of domains such as data integration, ontology alignment or model management. To speed up the generation of such mappings, automatic matching systems were developed to compute mapping suggestions that can be corrected by a user. However, constructing and tuning match strategies still requires a high manual effort by matching experts as well as correct mappings to evaluate generated mappings. We therefore propose a self-configuring schema matching system that is able to automatically adapt to the given mapping problem at hand. Our approach is based on analyzing the input schemas as well as intermediate matching results. A variety of matching rules use the analysis results to automatically construct and adapt an underlying matching process for a given match task. We comprehensively evaluate our approach on different mapping problems from the schema, ontology and model management domains. The evaluation shows that our system is able to robustly return good quality mappings across different mapping problems and domains.