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.

通过利用合作者关系和研究领域，提出了一种新的方法，使用受监督的深度学习模型对图书馆作者名称歧义问题进行消歧，其能够在数字图书馆中有效地区分共享相同名称的作者和具有不同名称变体的作者。

Bib2Auth：利用文献数据的深度学习方法进行作者消歧