  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.
