Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Quality Measures in Data Mining (Studies in Computational Intelligence)
Quality Measures in Data Mining (Studies in Computational Intelligence)
Adaptive sorted neighborhood methods for efficient record linkage
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
A declarative framework for semantic link discovery over relational data
Proceedings of the 18th international conference on World wide web
Combining a Logical and a Numerical Method for Data Reconciliation
Journal on Data Semantics XII
Interlinking Music-Related Data on the Web
IEEE MultiMedia
Discovering and Maintaining Links on the Web of Data
ISWC '09 Proceedings of the 8th International Semantic Web Conference
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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.