Large-scale author name disambiguation using approximate network structures

How can author identities be resolved across large publication collections?

Names alone are unreliable identifiers: different people can share a name, and one person's name can appear in several forms. This work investigates a scalable approach to author-name disambiguation using approximate network structures.

What the study found

• The work addresses the computational cost of author disambiguation at large scale.
• It describes an approach that uses network structure to help distinguish publication authors.

How the study works

The conference contribution studies approximate network structures for grouping publication records that may refer to the same author.

Scope and limitations

• Disambiguation is an estimation task; name ambiguity and incomplete metadata can leave uncertainty.
• Use the paper's evaluation when deciding whether the approach fits a different publication collection.

Abstract

Properly identifying the author of a scientific article is an important task for giving credit, tracking progress, and identifying ideas’ lineages. Usually, publications and citations do not provide unique identifiers to authors but only the raw string character representation of their name and affiliation. The fundamental problem is that an author might change the string representations due to changing in name spelling (e.g., removing accents), journal limitations (e.g., only allow first letter of first name), or simply two people having the same name. Several researchers have proposed methods to solve this problem, but most methods do not scale well and are not open to the community. In this work, we develop a scalable method that we make publicly available to disambiguate large-scale publications

Abstract from the original work, reproduced under its Creative Commons license. The overview above summarizes the study.

Cite this work

Tong Zeng, Daniel E Acuna (2020). Large-scale author name disambiguation using approximate network structures. International Conference on Computational Social Science. https://doi.org/10.5281/zenodo.4403705

@article{zengacuna2020,
  title = {Large-scale author name disambiguation using approximate network structures},
  author = {Zeng, Tong and Acuna, Daniel E},
  year = {2020},
  publication_date = {2020-07-17},
  journal = {International Conference on Computational Social Science},
  doi = {10.5281/zenodo.4403705},
  url = {https://scienceofscience.org/assets/pdf/ic2s2-author_name_disambiguation_zeng_and_acuna.pdf}
}