# Effect of forename string on author name disambiguation

arXiv: https://arxiv.org/abs/2102.03250 · HTML: https://mlsanity.com/paper/2102.03250/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 47 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Ethnicity sensitive author disambiguation using semi-supervised learning](https://mlsanity.com/paper/1508.07744/) (2015)
- [Distortive Effects of Initial-Based Name Disambiguation on Measurements of Large-Scale Coauthorship Networks](https://mlsanity.com/paper/1502.06306/) (2015)
- [Evaluating author name disambiguation for digital libraries: A case of DBLP](https://mlsanity.com/paper/1806.10540/) (2018)
- [The impact of imbalanced training data on machine learning for author name disambiguation](https://mlsanity.com/paper/1808.00525/) (2018)
- [Modelling transition phenomena of scientific coauthorship networks](https://mlsanity.com/paper/1604.08891/) (2016)
- [Evaluating Co-Authorship Networks in Author Name Disambiguation for Common Names](https://mlsanity.com/paper/1606.03857/) (2016)
- [Scaling Author Name Disambiguation with CNF Blocking](https://mlsanity.com/paper/1709.09657/) (2017)
- [Homonym Detection in Curated Bibliographies: Learning from dblp's Experience (full version)](https://mlsanity.com/paper/1806.06017/) (2018)

## Built on by (most cited first, 1 of 1)

- [Recent Developments in Deep Learning-based Author Name Disambiguation](https://mlsanity.com/paper/2503.13448/) (2024)

## Abstract (arXiv)

In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performances of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled datasets under varying ratios and lengths of full forenames, reflecting real-world scenarios in which an author is represented by forename variants (synonym) and some authors share the same forenames (homonym). Results show that increasing the ratios of full forenames improves substantially the performances of both heuristic and machine-learning-based disambiguation. Performance gains by algorithmic disambiguation are pronounced when many forenames are initialized or homonym is prevalent. As the ratios of full forenames increase, however, they become marginal compared to the performances by string matching. Using a small portion of forename strings does not reduce much the performances of both heuristic and algorithmic disambiguation compared to using full-length strings. These findings provide practical suggestions such as restoring initialized forenames into a full-string format via record linkage for improved disambiguation performances.