Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 re developed to compute mapping suggestions that can be corrected by a user. Hover, constructing and tuning match strategies still requires a high manual effort by matching experts as ll as correct mappings to evaluate generated mappings.  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 ll 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.  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.
['Eric Peukert', 'Julian Eberius', 'Erhard Rahm']
I am writing a research paper on a idea A Self-Configuring Schema Matching System
[{"from": "human", "value": "I am writing a research paper on a idea A Self-Configuring Schema Matching System"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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 re developed to compute mapping suggestions that can be corrected by a user. Hover, constructing and tuning match strategies still requires a high manual effort by matching experts as ll as correct mappings to evaluate generated mappings.  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 ll 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.  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."}]