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A Heuristic Approach for Configuration Learning of Supervised Instance Matching

Instance matching is the problem of finding instances that co-describe the same topic. The performance of an instance matching system relies on the specified configuration, which is optimally designed for each particular dataset. We propose cLearn, a heuristic-based algorithm that automatically searches for the most appropriate matching configuration for the given repositories. cLearn is effective when being tested on OAEI benchmarks and outperforms previous supervised systems.