  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/.

Computation and Language