Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/231411 
Year of Publication: 
2021
Series/Report no.: 
ZEW Discussion Papers No. 21-021
Publisher: 
ZEW - Leibniz-Zentrum für Europäische Wirtschaftsforschung, Mannheim
Abstract: 
Most bibliometric databases only provide names as the handle to their careers leading to the issue of namesakes. We introduce a universal method to assess the risk of linking documents of different individuals sharing the same name with the goal of collecting the documents into personalized clusters. A theoretical setup for the probability of drawing a namesake depending on the number of namesakes in the population and the size of the observed unit replaces the need for training datasets, thereby avoiding a namesake bias caused by the inherent underestimation of namesakes in training/benchmark data. A Poisson model based on a master sample of unambiguously identified individuals estimates the main component, the number of namesakes for any given name. To implement the algorithm, we reduce the complexity in the data by resolving similarity in properties. At the core of the implementation is a mechanism returning the unit size of the intersected mutual properties linking two documents. Because of the high computational demands of this mechanism, it is a necessity to discuss means to optimize the procedure.
Subjects: 
homonymy
namesakes
disambiguation
scientific careers
inventors
patents
publications
JEL: 
C18
C36
Document Type: 
Working Paper

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