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/.
@article{arxiv.2309.07545,
title = {DBLPLink: An Entity Linker for the DBLP Scholarly Knowledge Graph},
author = {Debayan Banerjee and Arefa and Ricardo Usbeck and Chris Biemann},
journal= {arXiv preprint arXiv:2309.07545},
year = {2023}
}
Comments
Accepted at International Semantic Web Conference (ISWC) 2023 Posters & Demo Track