Towards Entity Spaces

Marieke van Erp, Paul Groth

Abstract
Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis. For example, they allow us to find documents relevant to a specific entity irrespective of the underlying syntactic expression within a document. However, the entities that are commonly represented in knowledge bases are often a simplification of what is truly being referred to in text. For example, in a knowledge base, we may have an entity for Germany as a country but not for the more fuzzy concept of Germany that covers notions of German Population, German Drivers, and the German Government. Inspired by recent advances in contextual word embeddings, we introduce the concept of entity spaces - specific representations of a set of associated entities with near-identity. Thus, these entity spaces provide a handle to an amorphous grouping of entities. We developed a proof-of-concept for English showing how, through the introduction of entity spaces in the form of disambiguation pages, the recall of entity linking can be improved.
Anthology ID:
2020.lrec-1.261
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2129–2137
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.261
DOI:
Bibkey:
Cite (ACL):
Marieke van Erp and Paul Groth. 2020. Towards Entity Spaces. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 2129–2137, Marseille, France. European Language Resources Association.
Cite (Informal):
Towards Entity Spaces (van Erp & Groth, LREC 2020)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.261.pdf
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