# Deep Author Name Disambiguation using DBLP Data

arXiv: https://arxiv.org/abs/2303.10067 · HTML: https://mlsanity.com/paper/2303.10067/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 42 references that are in the map.
- First posted on arXiv 17 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [LINE: Large-scale Information Network Embedding](https://mlsanity.com/paper/1503.03578/) (2015)
- [PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks](https://mlsanity.com/paper/1508.00200/) (2015)
- [DeepER -- Deep Entity Resolution](https://mlsanity.com/paper/1710.00597/) (2017)
- [A Joint Model for Word Embedding and Word Morphology](https://mlsanity.com/paper/1606.02601/) (2016)
- [Name Disambiguation in Anonymized Graphs using Network Embedding](https://mlsanity.com/paper/1702.02287/) (2017)
- [Ethnicity sensitive author disambiguation using semi-supervised learning](https://mlsanity.com/paper/1508.07744/) (2015)
- [Author Name Disambiguation by Using Deep Neural Network](https://mlsanity.com/paper/1502.08030/) (2015)
- [Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams](https://mlsanity.com/paper/1607.05746/) (2016)
- [Whois? Deep Author Name Disambiguation using Bibliographic Data](https://mlsanity.com/paper/2207.04772/) (2022)

## Abstract (arXiv)

In the academic world, the number of scientists grows every year and so does the number of authors sharing the same names. Consequently, it challenging to assign newly published papers to their respective authors. Therefore, Author Name Ambiguity (ANA) is considered a critical open problem in digital libraries. This paper proposes an Author Name Disambiguation (AND) approach that links author names to their real-world entities by leveraging their co-authors and domain of research. To this end, we use data collected from the DBLP repository that contains more than 5 million bibliographic records authored by around 2.6 million co-authors. Our approach first groups authors who share the same last names and same first name initials. The author within each group is identified by capturing the relation with his/her co-authors and area of research, represented by the titles of the validated publications of the corresponding author. To this end, we train a neural network model that learns from the representations of the co-authors and titles. We validated the effectiveness of our approach by conducting extensive experiments on a large dataset.