# DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

arXiv: https://arxiv.org/abs/2309.07545 · HTML: https://mlsanity.com/paper/2309.07545/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 12 references that are in the map.
- First posted on arXiv 14 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [Neural Entity Linking: A Survey of Models Based on Deep Learning](https://mlsanity.com/paper/2006.00575/) (2020)
- [DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2303.13351/) (2023)

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

- [NLQxform: A Language Model-based Question to SPARQL Transformer](https://mlsanity.com/paper/2311.07588/) (2023)
- [SPARQL Generation with Entity Pre-trained GPT for KG Question Answering](https://mlsanity.com/paper/2402.00969/) (2024)
- [BERTologyNavigator: Advanced Question Answering with BERT-based Semantics](https://mlsanity.com/paper/2401.09553/) (2024)
- [DBLPLink 2.0 -- An Entity Linker for the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2507.22811/) (2025)

## Abstract (arXiv)

In this work, we present a web application named DBLPLink, which performs entity linking over the DBLP scholarly knowledge graph. DBLPLink uses text-to-text pre-trained language models, such as T5, to produce entity label spans from an input text question. Entity candidates are fetched from a database based on the labels, and an entity re-ranker sorts them based on entity embeddings, such as TransE, DistMult and ComplEx. The results are displayed so that users may compare and contrast the results between T5-small, T5-base and the different KG embeddings used. The demo can be accessed at https://ltdemos.informatik.uni-hamburg.de/dblplink/.