Article

Combining Word and Entity Embeddings for Entity Linking

Authors:

Jose G. Moreno,

Romaric Besançon,

Romain Beaumont,

Anne-Laure Ligozat,

Xavier Tannier,

Brigitte GrauAuthors Info & Claims

The Semantic Web: 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 – June 1, 2017, Proceedings, Part I

Pages 337 - 352

https://doi.org/10.1007/978-3-319-58068-5_21

Published: 28 May 2017 Publication History

Abstract

The correct identification of the link between an entity mention in a text and a known entity in a large knowledge base is important in information retrieval or information extraction. The general approach for this task is to generate, for a given mention, a set of candidate entities from the base and, in a second step, determine which is the best one. This paper proposes a novel method for the second step which is based on the joint learning of embeddings for the words in the text and the entities in the knowledge base. By learning these embeddings in the same space we arrive at a more conceptually grounded model that can be used for candidate selection based on the surrounding context. The relative improvement of this approach is experimentally validated on a recent benchmark corpus from the TAC-EDL 2015 evaluation campaign.

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Cited By

Zhang BZhao TMa CChen YWang Y(2024)AugSBertChat: User Feedback-Enhanced QA with Sentence-RoBERTaAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5669-8_17(202-213)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5669-8_17
Ristoski PLin ZZhou QGentile AGonçalves R(2021)KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity LinkingProceedings of the 11th Knowledge Capture Conference10.1145/3460210.3493549(49-56)Online publication date: 2-Dec-2021
https://dl.acm.org/doi/10.1145/3460210.3493549
Almasian SSpitz AGertz M(2019)Word Embeddings for Entity-Annotated TextsAdvances in Information Retrieval10.1007/978-3-030-15712-8_20(307-322)Online publication date: 14-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-15712-8_20
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Published In

cover image Guide Proceedings

The Semantic Web: 14th International Conference, ESWC 2017, Portorož, Slovenia, May 28 – June 1, 2017, Proceedings, Part I

May 2017

687 pages

ISBN:978-3-319-58067-8

DOI:10.1007/978-3-319-58068-5

Editors:
Eva Blomqvist
Linköping University, Linköping, Sweden
,
Diana Maynard
University of Sheffield, Sheffield, United Kingdom
,
Aldo Gangemi
Paris Nord University, Paris, France
,
Rinke Hoekstra
Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
,
Pascal Hitzler
Wright State University, Dayton, Ohio, USA
,
Olaf Hartig
Linköping University, Linköping, Sweden

© Springer International Publishing AG 2017.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 28 May 2017

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Cited By

Zhang BZhao TMa CChen YWang Y(2024)AugSBertChat: User Feedback-Enhanced QA with Sentence-RoBERTaAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5669-8_17(202-213)Online publication date: 5-Aug-2024
https://dl.acm.org/doi/10.1007/978-981-97-5669-8_17
Ristoski PLin ZZhou QGentile AGonçalves R(2021)KG-ZESHEL: Knowledge Graph-Enhanced Zero-Shot Entity LinkingProceedings of the 11th Knowledge Capture Conference10.1145/3460210.3493549(49-56)Online publication date: 2-Dec-2021
https://dl.acm.org/doi/10.1145/3460210.3493549
Almasian SSpitz AGertz M(2019)Word Embeddings for Entity-Annotated TextsAdvances in Information Retrieval10.1007/978-3-030-15712-8_20(307-322)Online publication date: 14-Apr-2019
https://dl.acm.org/doi/10.1007/978-3-030-15712-8_20
Nguyen GTamine LSoulier LSouf N(2018)A Tri-Partite Neural Document Language Model for Semantic Information RetrievalThe Semantic Web10.1007/978-3-319-93417-4_29(445-461)Online publication date: 3-Jun-2018
https://dl.acm.org/doi/10.1007/978-3-319-93417-4_29

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