Evaluating end-to-end entity linking on domain-specific knowledge bases:
  Learning about ancient technologies from museum collections
Sebastian Cadavid-Sanchez Khalil Kacem Rafael Aparecido Martins Frade Johannes Boehm Thomas Chaney Danial Lashkari Daniel Simig
To study social, economic, and historical questions, researchers in the
social sciences and humanities have started to use increasingly large
unstructured textual datasets. While recent advances in NLP provide many tools
to efficiently process such data, most existing approaches rely on generic
solutions whose performance and suitability for domain-specific tasks is not
well understood. This work presents an attempt to bridge this domain gap by
exploring the use of modern Entity Linking approaches for the enrichment of
museum collection data. We collect a dataset comprising of more than 1700 texts
annotated with 7,510 mention-entity pairs, evaluate some off-the-shelf
solutions in detail using this dataset and finally fine-tune a recent
end-to-end EL model on this data. We show that our fine-tuned model
significantly outperforms other approaches currently available in this domain
and present a proof-of-concept use case of this model. We release our dataset
and our best model.