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L2R: A Logical Method for Reference Reconciliation

Fatiha Saïs 1, 2, * Nathalie Pernelle 1, 2 Marie-Christine Rousset 3
* Corresponding author
1 GEMO - Integration of data and knowledge distributed over the web
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : The reference reconciliation problem consists in decid- ing whether different identifiers refer to the same data, i.e., correspond to the same world entity. The L2R sys- tem 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 reconcilia- tion and no reconciliation. In contrast with other ap- proaches, 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.
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Fatiha Saïs, Nathalie Pernelle, Marie-Christine Rousset. L2R: A Logical Method for Reference Reconciliation. Twenty-Second AAAI Conference on Artificial Intelligence, Jul 2007, Vancouver, British Columbia, Canada. pp.2007. ⟨inria-00433004⟩

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