DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph

Debayan Banerjee, Arefa, Ricardo Usbeck, Chris Biemann
9/14/2023
cs.CL

No confident code implementation found for this paper.

Abstract

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/.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

A Practical Entity Linking System for Tables in Scientific Literature

86% match2023

Multilingual End to End Entity Linking

84% match1 repo502023

BELB: a Biomedical Entity Linking Benchmark

84% match1 repo152023

CADEL: A Corpus of Administrative Web Documents for Japanese Entity Linking

83% match2026

Instructed Language Models with Retrievers Are Powerful Entity Linkers

82% match1 repo62023

Exploring In-Context Learning Capabilities of Foundation Models for Generating Knowledge Graphs from Text

82% match2023

Cite this paper

@article{banerjee2023dblplink,
  title  = {DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph},
  author = {Debayan Banerjee and Arefa and Ricardo Usbeck and Chris Biemann},
  year   = {2023},
  eprint = {2309.07545},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.07545v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2309.07545.svg)](https://codeofpaper.com/paper/2309.07545)

Discussion