Machine Learning

Our system uses automatically discovered MDs, various dataset features, and known gold standards to train a model that selects MDs as duplicate detection rules. Once trained, the model can select useful MDs for duplicate detection on any new dataset.

In this paper, we study the problem of matching records that contain address information, including attributes such as Street-address and City. To facilitate this matching process we propose a domain-specific procedure to first enrich each record with a more complete representation of the address information through geocoding and reverse-geocoding, and second to select the best similarity measure per each address attribute, that will finally help the classifier to achieve the best f-measure.