Abstract. Record matching or linking is one of the phases of the data quality improvement process, in which, records from different sources, are cleansed and integrated in a centralized data store to be used for various purposes. Both, earlier and recent studies in data quality and record linkage focus on various statistical models, which make strong assumptions on the probabilities of attribute errors. In this study, we evaluate different models for record linkage, which are built based on data only. We use a program that generates data with known error distributions and we train classification models, which we use to estimate the accuracy and the completeness of the record linking process. The results indicate that the automated learning techniques are adequate for this process and that both their accuracy and their completeness are comparable to the accuracy and the completeness of other, mostly manual, processes
