Author Name Disambiguation Based on Rule and Graph Model

Abstract

Author name disambiguation has long been viewed as a challenging problem in scientific literature management, and with the substantial growth of the scientific literature, the solution to this problem has become increasingly difficult and urgency. In this paper, we conduct research on the author name disambiguation problem in large-scale academic papers. In our method, we combine the paper feature information and the relation information between the papers for disambiguation. Based on the Aminer’s disambiguation framework, we present a novel method to constructing the paper relation graph based on atomic cluster and propose an efficient post processing algorithm, aiming to improve the disambiguation performance by rule-based clustering, this algorithm utilizes similarity features based on metadata information and implement two types of disambiguation rules. We carefully evaluate the proposed disambiguation method on real-world large data and experimental result shows that our method achieves clearly better performance than the state-of-the-art methods.