Data Cleaning publication categorizer

Object identification is an important issue for integration of data from
different sources. The identification task is complicated, if no global and consistent
identifier is shared by the sources. Then, object identification can only be performed
through the identifying information, the objects data provides itself. Unfortunately
real-world data is dirty, hence identification mechanisms like natural keys fail mostly
—we have to take care of the variations and errors of the data. Consequently, object
identification can no more be guaranteed to be fault-free. Several methods tackle
the object identification problem, e.g. Record Linkage, or the Sorted Neighborhood
Method.
Based on a novel object identification framework, we assessed data quality and
evaluated different methods on real data. One main result is that scalability is
determined by the applied preselection technique and the usage of efficient data
structures. As another result we can state that Decision Tree Induction achieves
better correctness and is more robust than Record Linkage.