Whois? Deep Author Name Disambiguation Using Bibliographic Data

Abstract

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it challenging to assign newly published papers to their respective authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.

High‐degree penalty based global statistical network embedding for name disambiguation in anonymized graph

“…This poses a significant challenge for many person‐centric applications due to name ambiguity 1,2,4 . For example, there are over 200 authors with the same name and over 2000 articles related to them when we search for “Wei Wang” in the person search application DBLP 5 . It is therefore time‐consuming to find the literature of “Wei Wang” we are interested in.…”