Data Cleaning publication categorizer

Object Matching (OM) is the problem of identifying
pairs of data-objects coming from different sources and representing
the same real world object. Several methods have been
proposed to solve OM problems, but none of them seems to be at
the same time fully automated and very effective. In this paper
we present a fundamentally new suite of methods that instead
possesses both these abilities.
We adopt a statistical approach based on mixture models,
which structures an OM process into two consecutive tasks.
First, mixture parameters are estimated by fitting the model to

We propose a class of constraints, referred to as conditional functional dependencies (CFDs), and study their applications in data cleaning. In contrast to traditional functional dependencies (FDs) that were developed mainly for schema design, CFDs aim at capturing the consistency of data by incorporating bindings of semantic ally related values. For CFDs we provide an inference system analogous to Armstrong's axioms for FDs, as well as consistency analysis.