A Bayesian Learning, Greedy agglomerative clustering approach and evaluation techniques for Author Name Disambiguation Problem

TL;DR

This paper reviews author name disambiguation techniques, focusing on Bayesian learning and greedy agglomerative clustering, and evaluates their effectiveness on large real-world databases.

Contribution

It introduces a Bayesian and greedy agglomerative clustering approach for author disambiguation and discusses evaluation techniques and future research directions.

Findings

Effective disambiguation on large databases

Bayesian and greedy methods improve accuracy

Provides comprehensive review and future outlook

Abstract

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