Aggregating large-scale databases for PubMed author name disambiguation.

ABSTRACT:

Objective

PubMed has suffered from the author ambiguity problem for many years. Existing studies on author name disambiguation (AND) for PubMed only used internal metadata for development. However, some of them are incomplete (eg, a large number of names are only abbreviated and their full names are not available) or less discriminative. To this end, we present a new disambiguation method, namely AggAND, by aggregating information from external databases.

Materials and methods

We address this issue by exploring Microsoft Academic Graph, Semantic Scholar, and PubMed Knowledge Graph to enhance the built-in name metadata, and extend the internal metadata with some external and more discriminative metadata.

Results

Experimental results on enhanced name metadata demonstrat

SUBMITTER: Zhang L 

PROVIDER: S-EPMC8363810 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature