Author name disambiguation of bibliometric data: A comparison of several unsupervised approaches

Adequately disambiguating author names in bibliometric databases is a precondition for conducting reliable analyses at the author level. In the case of bibliometric studies that include many researchers, it is not possible to disambiguate each single researcher manually. Several approaches have been proposed for author name disambiguation but there has not yet been a comparison of them under controlled conditions. In this study, we compare a set of unsupervised disambiguation approaches. Unsupervised approaches specify a model to assess the similarity of author mentions a priori instead of training a model with labelled data. In order to evaluate the approaches, we applied them to a set of author mentions annotated with a ResearcherID, this being an author identifier maintained by the researchers themselves. Apart from comparing the overall performance, we take a more detailed look at the role of the parametrization of the approaches and analyse the dependence of the results on the complexity of the disambiguation task. It could be shown that all of the evaluated approaches produce better results than those that can be obtained by using only author names. In the context of this study, the approach proposed by Caron and van Eck (2014) produced the best results.