Bib2Auth: Deep Learning Approach for Author Disambiguation using Bibliographic Data

TL;DR

Bib2Auth employs deep learning on bibliographic data to effectively resolve author ambiguity by analyzing co-authorship and research areas, improving author identification accuracy in digital libraries.

Contribution

The paper introduces a novel deep learning method that leverages semantic and symbolic representations of co-authors and research areas for author disambiguation.

Findings

Successfully distinguishes authors with same names

Recognizes authors with different name variations

Performs well on large bibliographic datasets

Abstract

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.