Do Judge an Entity by Its Name! Entity Typing Using Language Models

Russa Biswas; Radina Sofronova; Mehwish Alam; Nicolas Heist; Heiko Paulheim; Harald Sack

doi:10.1007/978-3-030-80418-3_12

Do Judge an Entity by Its Name! Entity Typing Using Language Models

Russa Biswas, Radina Sofronova, Mehwish Alam, Nicolas Heist, Heiko Paulheim, Harald Sack

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.

Original language	English
Title of host publication	The Semantic Web
Subtitle of host publication	ESWC 2021 Satellite Events, Revised Selected Papers
Editors	Ruben Verborgh, Anastasia Dimou, Aidan Hogan, Claudia d’Amato, Ilaria Tiddi, Arne Bröring, Simon Maier, Femke Ongenae, Riccardo Tommasini, Mehwish Alam
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	65-70
Number of pages	6
ISBN (Print)	9783030804176
DOIs	https://doi.org/10.1007/978-3-030-80418-3_12
Publication status	Published - 1 Jan 2021
Externally published	Yes
Event	18th Extended Semantic Web Conference, ESWC 2021 - Virtual, Online Duration: 6 Jun 2021 → 10 Jun 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12739 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th Extended Semantic Web Conference, ESWC 2021
City	Virtual, Online
Period	6/06/21 → 10/06/21

Keywords

Deep neural networks
Entity type prediction
Knowledge graph completion

Access to Document

10.1007/978-3-030-80418-3_12

Cite this

Biswas, R., Sofronova, R., Alam, M., Heist, N., Paulheim, H., & Sack, H. (2021). Do Judge an Entity by Its Name! Entity Typing Using Language Models. In R. Verborgh, A. Dimou, A. Hogan, C. d’Amato, I. Tiddi, A. Bröring, S. Maier, F. Ongenae, R. Tommasini, & M. Alam (Eds.), The Semantic Web: ESWC 2021 Satellite Events, Revised Selected Papers (pp. 65-70). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12739 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-80418-3_12

Biswas, Russa ; Sofronova, Radina ; Alam, Mehwish et al. / Do Judge an Entity by Its Name! Entity Typing Using Language Models. The Semantic Web: ESWC 2021 Satellite Events, Revised Selected Papers. editor / Ruben Verborgh ; Anastasia Dimou ; Aidan Hogan ; Claudia d’Amato ; Ilaria Tiddi ; Arne Bröring ; Simon Maier ; Femke Ongenae ; Riccardo Tommasini ; Mehwish Alam. Springer Science and Business Media Deutschland GmbH, 2021. pp. 65-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Do Judge an Entity by Its Name! Entity Typing Using Language Models",

abstract = "The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.",

keywords = "Deep neural networks, Entity type prediction, Knowledge graph completion",

author = "Russa Biswas and Radina Sofronova and Mehwish Alam and Nicolas Heist and Heiko Paulheim and Harald Sack",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 18th Extended Semantic Web Conference, ESWC 2021 ; Conference date: 06-06-2021 Through 10-06-2021",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

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editor = "Ruben Verborgh and Anastasia Dimou and Aidan Hogan and Claudia d{\textquoteright}Amato and Ilaria Tiddi and Arne Br{\"o}ring and Simon Maier and Femke Ongenae and Riccardo Tommasini and Mehwish Alam",

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Biswas, R, Sofronova, R, Alam, M, Heist, N, Paulheim, H & Sack, H 2021, Do Judge an Entity by Its Name! Entity Typing Using Language Models. in R Verborgh, A Dimou, A Hogan, C d’Amato, I Tiddi, A Bröring, S Maier, F Ongenae, R Tommasini & M Alam (eds), The Semantic Web: ESWC 2021 Satellite Events, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12739 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 65-70, 18th Extended Semantic Web Conference, ESWC 2021, Virtual, Online, 6/06/21. https://doi.org/10.1007/978-3-030-80418-3_12

Do Judge an Entity by Its Name! Entity Typing Using Language Models. / Biswas, Russa; Sofronova, Radina; Alam, Mehwish et al.
The Semantic Web: ESWC 2021 Satellite Events, Revised Selected Papers. ed. / Ruben Verborgh; Anastasia Dimou; Aidan Hogan; Claudia d’Amato; Ilaria Tiddi; Arne Bröring; Simon Maier; Femke Ongenae; Riccardo Tommasini; Mehwish Alam. Springer Science and Business Media Deutschland GmbH, 2021. p. 65-70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12739 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Do Judge an Entity by Its Name! Entity Typing Using Language Models

AU - Biswas, Russa

AU - Sofronova, Radina

AU - Alam, Mehwish

AU - Heist, Nicolas

AU - Paulheim, Heiko

AU - Sack, Harald

PY - 2021/1/1

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N2 - The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.

AB - The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.

KW - Deep neural networks

KW - Entity type prediction

KW - Knowledge graph completion

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - The Semantic Web

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A2 - Dimou, Anastasia

A2 - Hogan, Aidan

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A2 - Tiddi, Ilaria

A2 - Bröring, Arne

A2 - Maier, Simon

A2 - Ongenae, Femke

A2 - Tommasini, Riccardo

A2 - Alam, Mehwish

PB - Springer Science and Business Media Deutschland GmbH

T2 - 18th Extended Semantic Web Conference, ESWC 2021

Y2 - 6 June 2021 through 10 June 2021

ER -

Biswas R, Sofronova R, Alam M, Heist N, Paulheim H, Sack H. Do Judge an Entity by Its Name! Entity Typing Using Language Models. In Verborgh R, Dimou A, Hogan A, d’Amato C, Tiddi I, Bröring A, Maier S, Ongenae F, Tommasini R, Alam M, editors, The Semantic Web: ESWC 2021 Satellite Events, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2021. p. 65-70. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-80418-3_12

Do Judge an Entity by Its Name! Entity Typing Using Language Models

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