A Bayesian Learning, Greedy agglomerative clustering approach and evaluation techniques for Author Name Disambiguation Problem
It addresses ambiguity in author names for bibliometric analysis and digital libraries, but appears to be an incremental review rather than presenting new experimental results.
This paper reviews techniques for the Author Name Disambiguation problem, focusing on a Bayesian learning and greedy agglomerative clustering approach applied to a large real-world database to improve accuracy in associating scholarly works with authors.
Author names often suffer from ambiguity owing to the same author appearing under different names and multiple authors possessing similar names. It creates difficulty in associating a scholarly work with the person who wrote it, thereby introducing inaccuracy in credit attribution, bibliometric analysis, search-by-author in a digital library, and expert discovery. A plethora of techniques for disambiguation of author names has been proposed in the literature. I try to focus on the research efforts targeted to disambiguate author names. I first go through the conventional methods, then I discuss evaluation techniques and the clustering model which finally leads to the Bayesian learning and Greedy agglomerative approach. I believe this concentrated review will be useful for the research community because it discusses techniques applied to a very large real database that is actively used worldwide. The Bayesian and the greedy agglomerative approach used will help to tackle AND problems in a better way. Finally, I try to outline a few directions for future work