Classification rule learning for data linking

Nathalie Pernelle, Fatiha Saïs

Abstract: Many approaches have been defined to link data items automatically. Nevertheless, when data are numerous and when the schema is unknown, most of these approaches are too time-consuming. We propose an approach where classification rules are learnt thanks to a training set made of linked data. These classification rules can then be applied in order to classify data items and reduce the linking space i. e the space made of data item pairs that have to be compared. First experiments have been conducted on RDF data sets describing electronic products.