On generating benchmark data for entity matching

Entity matching has been a fundamental task in every major integration and data cleaning effort. It aims at identifying whether two different pieces of information are referring to the same real world object. It can also form the basis of entity search by finding the entities in a repository that best match a user specification. Despite the many different entity matching techniques that have been developed over time, there is still no widely accepted benchmark for evaluating and comparing them. This paper introduces EMBench, a principled system for the evaluation of entity matching systems. In contrast to existing similar efforts, EMBench offers a unique test case generation approach that combines different levels of types, complexity, and scales, allowing a complete and accurate evaluation of the different aspects of a matching system. After presenting the basic principles of EMBench and its functionality, a comprehensive evaluation is performed on some existing matching systems that showcases its discriminative power in highlighting their capabilities and limitations. EMBench has all the characteristics of a benchmark and can serve as a standard evaluation methodology provided that it gains popularity and wide acceptance.