  Author name ambiguity remains a critical open problem in digital libraries
due to synonymy and homonymy of names. In this paper, we propose a novel
approach to link author names to their real-world entities by relying on their
co-authorship pattern and area of research. Our supervised deep learning model
identifies an author by capturing his/her relationship with his/her co-authors
and area of research, which is represented by the titles and sources of the
target author's publications. These attributes are encoded by their semantic
and symbolic representations. To this end, Bib2Auth uses ~ 22K bibliographic
records from the DBLP repository and is trained with each pair of co-authors.
The extensive experiments have proved the capability of the approach to
distinguish between authors sharing the same name and recognize authors with
different name variations. Bib2Auth has shown good performance on a relatively
large dataset, which qualifies it to be directly integrated into bibliographic
indices.
