Learning from the Past: An Analysis of Person Name Corrections in the DBLP Collection and Social Network Properties of Affected Entities

  • Florian ReitzEmail author
  • Oliver Hoffmann
Part of the Lecture Notes in Social Networks book series (LNSN, volume 6)


Many projects like the DBLP bibliography have to use names as identifiers for persons. Names however are neither unique nor is it guaranteed that a person is referred to by only one name. This causes inconsistencies which reduce the data quality of a collection. Though there are a large number of algorithmic approaches to solve this problem, little is known on the properties of the inconsistent entities. We show how to extract a large number of past name inconsistencies from the DBLP data set. We analyze the social network properties of these names and of the communities they belong to. We evaluate the usefulness of different properties to differentiate defective and none-defective names and present an approach which can predict the probability that a name will need correction in the future.


Negative Condition Relational Network Test Collection Data Entity Correction Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Manh Cuong Pham and Ralf Klamma for providing us with the thematic clustering data.


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Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  1. 1.University of TrierTrierGermany
  2. 2.Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbHWadernGermany

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