  In this paper, we present a method to automatically generate a large-scale
labeled dataset for author name disambiguation (AND) in the academic world by
leveraging authoritative sources, ORCID and DOI. Using the method, we built
LAGOS-AND, a large, gold standard dataset for AND, which is substantially
different from existing ones. It contains 7.5M citations authored by 797K
unique authors and shows close similarities to the entire Microsoft Academic
Graph (MAG) across six gold standard validations. In building the dataset, we
investigated the long-standing name synonym problem and revealed the degree of
variation in the last name for the first time. Evidence from PubMed, MAG, and
Semantic Scholar all suggests that there are ~7.5% of authorships who have
varied their last names from the credible last names in the ORCID system when
ignoring the variants introduced by special characters. Furthermore, we
provided a classification-based AND benchmark on the new dataset and released
our model for disambiguation in general scenarios. If this work is helpful for
future studies, we believe it will challenge (1) the widely accepted
block-based disambiguation framework in production environment and, (2) the
state-of-the-art methods or models on AND. The code, dataset, and pre-trained
model are publicly available.
