NASTyLinker: NIL-aware scalable transformer-based entity linker

DOI:	https://doi.org/10.1007/978-3-031-33455-9_11
URL:	https://link.springer.com/chapter/10.1007/978-3-03...
Additional URL:	https://dl.acm.org/doi/abs/10.1007/978-3-031-33455...
Document Type:	Conference or workshop publication
Year of publication:	2023
Book title:	The Semantic Web : 20th International Conference, ESWC 2023, Hersonissos, Crete, Greece, May 28-June 1, 2023, Proceedings
The title of a journal, publication series:	Lecture Notes in Computer Science
Volume:	13870
Page range:	174-191
Conference title:	ESWC 2023, 20th International Conference
Location of the conference venue:	Hersonissos, Crete, Greece
Date of the conference:	28.05.2023-01.06.2023
Publisher:	Dimou, Anastasia ; Dragoni, Mauro ; Faria, Daniel ; Hertling, Sven ; Jiménez-Ruiz, Ernesto ; McCusker, Jamie ; Pesquita, Catia ; Troncy, Raphaël
Place of publication:	Berlin [u.a.]
Publishing house:	Springer
ISBN:	978-3-031-33454-2 , 978-3-031-33455-9
ISSN:	0302-9743 , 1611-3349
Related URLs:	https://thegraphlounge.com/event/eswc-20... https://www.researchgate.net/publication...
Publication language:	English
Institution:	School of Business Informatics and Mathematics > Data Science (Paulheim 2018-)
Subject:	004 Computer science, internet
Abstract:	Entity Linking (EL) is the task of detecting mentions of entities in text and disambiguating them to a reference knowledge base. Most prevalent EL approaches assume that the reference knowledge base is complete. In practice, however, it is necessary to deal with the case of linking to an entity that is not contained in the knowledge base (NIL entity). Recent works have shown that, instead of focusing only on affinities between mentions and entities, considering inter-mention affinities can be used to represent NIL entities by producing clusters of mentions. At the same time, inter-mention affinities can help to substantially improve linking performance for known entities. With NASTyLinker, we introduce an EL approach that is aware of NIL entities and produces corresponding mention clusters while maintaining high linking performance for known entities. The approach clusters mentions and entities based on dense representations from Transformers and resolves conflicts (if more than one entity is assigned to a cluster) by computing transitive mention-entity affinities. We show the effectiveness and scalability of NASTyLinker on NILK, a dataset that is explicitly constructed to evaluate EL with respect to NIL entities. Further, we apply the presented approach to an actual EL task, namely to knowledge graph population by linking entities in Wikipedia listings, and provide an analysis of the outcome.