Data Cleaning publication categorizer

The reference reconciliation problem consists of deciding whether different identifiers refer to the same world entity. Some existing reference reconciliation approaches use key constraints to infer reconciliation decisions. In the context of the Linked Open Data, this knowledge is not available. We propose KD2R, a method which allows automatic discovery of key constraints associated to OWL2 classes. These keys are discovered from RDF data which can be incomplete. The proposed algorithm allows this discovery without having to scan all the data.

The reference reconciliation problem consists in deciding whether different identifiers refer to the same data, i.e. correspond to the same real world entity. In this article we present a reference reconciliation approach which combines a logical method for reference reconciliation called L2R and a numerical one called N2R. This approach exploits the schema and data semantics, which is translated into a set of Horn FOL rules of reconciliation. These rules are used in L2R to infer exact decisions both of reconciliation and non-reconciliation.