L2R: a logical method for reference reconciliation

Authors:
Fatiha Saïs;Nathalie Pernelle;Marie-Christine Rousset
Affiliations:
LRI, Paris-Sud 11 University, and INRIA Futurs, Orsay, France;LRI, Paris-Sud 11 University, and INRIA Futurs, Orsay, France;LSR-IMAG, St Martin D'Heres, France
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Year:
2007

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Abstract

The reference reconciliation problem consists in deciding whether different identifiers refer to the same data, i.e., correspond to the same world entity. The L2R system exploits the semantics of a rich data model, which extends RDFS by a fragment of OWL-DL and SWRL rules. In L2R, the semantics of the schema is translated into a set of logical rules of reconciliation, which are then used to infer correct decisions both of reconciliation and no reconciliation. In contrast with other approaches, the L2R method has a precision of 100% by construction. First experiments show promising results for recall, and most importantly significant increases when rules are added.