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

Identifying real world persons by their name is a significant problem, especially for digital libraries like DBLP. Though there are a large number of algorithmic approaches, finding and correcting name-related inconsistencies is time-consuming and expensive. We introduce an extension to the DBLP collection which allows us to mine for modifications to name entities in a period of ten years. We use our findings to analyze how defective entities integrated into different dynamic social networks. Based on first results which showed that name errors are unevenly distributed in these networks we present and evaluate an approach to identify areas which are prone to name inconsistencies and require a more extensive monitoring.