DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph
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/.
Show BibTeX
@inproceedings{DBLP:conf/semweb/BanerjeeAUB23,
author = {Debayan Banerjee and
Arefa and
Ricardo Usbeck and
Chris Biemann},
editor = {Irini Fundulaki and
Kouji Kozaki and
Daniel Garijo and
Jos{\'{e}} Manu{\'{e}}l G{\'{o}}mez{-}P{\'{e}}rez},
title = {DBLPLink: An Entity Linker for the {DBLP} Scholarly Knowledge Graph},
booktitle = {Proceedings of the {ISWC} 2023 Posters, Demos and Industry Tracks:
From Novel Ideas to Industrial Practice co-located with 22nd International
Semantic Web Conference {(ISWC} 2023), Athens, Greece, November 6-10,
2023},
series = {{CEUR} Workshop Proceedings},
volume = {3632},
publisher = {CEUR-WS.org},
year = {2023},
url = {https://ceur-ws.org/Vol-3632/ISWC2023\_paper\_428.pdf},
timestamp = {Wed, 07 Feb 2024 16:02:39 +0100},
biburl = {https://dblp.org/rec/conf/semweb/BanerjeeAUB23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}