# A Knowledge Graph Embeddings based Approach for Author Name Disambiguation using Literals

arXiv: https://arxiv.org/abs/2201.09555 · HTML: https://mlsanity.com/paper/2201.09555/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 60 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 20 of 20)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings](https://mlsanity.com/paper/2007.14175/) (2020)
- [Citation Recommendation: Approaches and Datasets](https://mlsanity.com/paper/2002.06961/) (2020)
- [A Survey on Knowledge Graph Embeddings with Literals: Which model links better Literal-ly?](https://mlsanity.com/paper/1910.12507/) (2019)
- [Incorporating Literals into Knowledge Graph Embeddings](https://mlsanity.com/paper/1802.00934/) (2018)
- [(Almost) All of Entity Resolution](https://mlsanity.com/paper/2008.04443/) (2020)
- [OpenCitations, an infrastructure organization for open scholarship](https://mlsanity.com/paper/1906.11964/) (2019)
- [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 on Heterogeneous Information Network with Adversarial Representation Learning](https://mlsanity.com/paper/2002.09803/) (2020)
- [Author Name Disambiguation by Using Deep Neural Network](https://mlsanity.com/paper/1502.08030/) (2015)
- [The OpenCitations Data Model](https://mlsanity.com/paper/2005.11981/) (2020)

## Built on by (most cited first, 4 of 4)

- [Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case](https://mlsanity.com/paper/2308.01105/) (2023)
- [BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting](https://mlsanity.com/paper/2404.08322/) (2024)
- [Recent Developments in Deep Learning-based Author Name Disambiguation](https://mlsanity.com/paper/2503.13448/) (2024)
- [VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2505.11803/) (2025)

## Abstract (arXiv)

Scholarly data is growing continuously containing information about the articles from a plethora of venues including conferences, journals, etc. Many initiatives have been taken to make scholarly data available as Knowledge Graphs (KGs). These efforts to standardize these data and make them accessible have also led to many challenges such as exploration of scholarly articles, ambiguous authors, etc. This study more specifically targets the problem of Author Name Disambiguation (AND) on Scholarly KGs and presents a novel framework, Literally Author Name Disambiguation (LAND), which utilizes Knowledge Graph Embeddings (KGEs) using multimodal literal information generated from these KGs. This framework is based on three components: 1) Multimodal KGEs, 2) A blocking procedure, and finally, 3) Hierarchical Agglomerative Clustering. Extensive experiments have been conducted on two newly created KGs: (i) KG containing information from Scientometrics Journal from 1978 onwards (OC-782K), and (ii) a KG extracted from a well-known benchmark for AND provided by AMiner (AMiner-534K). The results show that our proposed architecture outperforms our baselines of 8-14% in terms of the F1 score and shows competitive performances on a challenging benchmark such as AMiner. The code and the datasets are publicly available through Github: https://github.com/sntcristian/and-kge and Zenodo:https://doi.org/10.5281/zenodo.6309855 respectively.