Data Cleaning publication categorizer

This paper deals with the issue of data fusion, which arises once reconciliations between references have been determined. The objective of this task is to fusion the descriptions of references that refer to the same real world entity so as to obtain a unique representation. In order to deal with the problem of uncertainty in the values associated with the attributes, we have chosen to represent the results of the fusion of references in a formalism based on fuzzy sets. We indicate how the confidence degrees are computed.

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.