An effective configuration learning algorithm for entity resolution

Entity resolution is the problem of finding co-referent instances, which at the same time describe the same topic. It is an important component of data integration systems and is indispensable in linked data publication process. Entity resolution has been a subject of extensive research; however, seeking for a perfect resolution algorithm remains a work in progress. Many approaches have been proposed for entity resolution. Among them, supervised entity resolution has been revealed as the most accurate approach [6, 2]. Meanwhile, configuration-based matching [2, 3, 5, 4] attracts most studies because of its advantages in scalability and interpretation. In order to match two instances of different repositories, configuration-based matching algorithms estimate the similarities between the values of the same attributes. After that, these similarities are aggregated into one matching score. This score is used to determine whether two instances are co-referent or not. The declarations of equivalent attributes, similarity measures, similarity aggregation, and acceptance threshold are specified by a matching configuration, which can be automatically optimized by a learning algorithm. Configuration learning using genetic algorithm has been a research topic of some studies [2, 5, 3]. The limitation of genetic algorithm is that it costs numerous iterations for reaching the convergence. We propose cLearn as a heuristic algorithm that is effective and more efficient. cLearn can be used to enhance the performance of any configuration-based entity resolution system.