Whois? Deep Author Name Disambiguation using Bibliographic Data
This addresses a critical open problem in digital libraries for researchers and librarians by improving author identification accuracy, though it appears incremental as it builds on existing methods like grouping by name and using co-author relations.
The paper tackles the problem of Author Name Ambiguity (ANA) by proposing a neural network-based Author Name Disambiguation (AND) approach that links author names to real-world entities using co-authors and research domains, validated on a dataset of over 5 million bibliographic records from DBLP.
As the number of authors is increasing exponentially over years, the number of authors sharing the same names is increasing proportionally. This makes it challenging to assign newly published papers to their adequate 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 a collection 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, which is 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.