Entity Matching: How Similar Is Similar

Entity matching that finds records referring to the same entity is an important operation in data cleaning and integration. Existing studies usually use a given similarity function to quantify the similarity of records, and focus on devising index structures and algorithms for efficient entity matching. However it is a big challenge to define “how similar is similar” for real applications, since it is rather hard to automatically select appropriate similarity functions. In this paper we attempt to address this problem. As there are a large number of similarity functions, and even worse thresholds may have infinite values, it is rather expensive to find appropriate similarity functions and thresholds. Fortunately, we have an observation that different similarity functions and thresholds have redundancy, and we have an opportunity to prune inappropriate similarity functions. To this end, we propose effective optimization techniques to eliminate such redundancy, and devise efficient algorithms to find the best similarity functions. The experimental results on both real and synthetic datasets show that our method achieves high accuracy and outperforms the baseline algorithms.