Data Cleaning publication categorizer

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.