Entity Typing with Triples Using Language Models

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

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  • Rheinische Friedrich-Wilhelms-Universität Bonn
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OriginalspracheEnglisch
Titel des SammelwerksThe Semantic Web
UntertitelESWC 2023 Satellite Events
Herausgeber/-innenCatia Pesquita, Hala Skaf-Molli, Vasilis Efthymiou, Sabrina Kirrane, Axel Ngonga, Diego Collarana, Renato Cerqueira, Mehwish Alam, Cassia Trojahn, Sven Hertling
Herausgeber (Verlag)Springer Science and Business Media Deutschland GmbH
Seiten169-173
Seitenumfang5
ISBN (elektronisch)978-3-031-43458-7
ISBN (Print)9783031434570
PublikationsstatusVeröffentlicht - 21 Okt. 2023
Veranstaltung20th Extended Semantic Web Conference, ESWC 2023 - Hersonissos, Griechenland
Dauer: 28 Mai 20231 Juni 2023

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band13998 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Abstract

Entity Typing is the task of assigning a type to an entity in a knowledge graph. In this paper, we propose ETwT (Entity Typing with Triples), which leverages the triples of an entity, namely its label, description and the property labels used on it. We analyse which language models and classifiers are best suited to this input and compare ETwT’s performance on coarse-grained and fine-grained entity typing. Our evaluation demonstrates that ETwT is able to predict coarse-grained entity types with an F $$:1$$ score of 0.994, outperforming three baselines.

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Entity Typing with Triples Using Language Models. / Riaz, Aniqa; Abdollahi, Sara; Gottschalk, Simon.
The Semantic Web: ESWC 2023 Satellite Events. Hrsg. / Catia Pesquita; Hala Skaf-Molli; Vasilis Efthymiou; Sabrina Kirrane; Axel Ngonga; Diego Collarana; Renato Cerqueira; Mehwish Alam; Cassia Trojahn; Sven Hertling. Springer Science and Business Media Deutschland GmbH, 2023. S. 169-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13998 LNCS).

Publikation: Beitrag in Buch/Bericht/Sammelwerk/KonferenzbandAufsatz in KonferenzbandForschungPeer-Review

Riaz, A, Abdollahi, S & Gottschalk, S 2023, Entity Typing with Triples Using Language Models. in C Pesquita, H Skaf-Molli, V Efthymiou, S Kirrane, A Ngonga, D Collarana, R Cerqueira, M Alam, C Trojahn & S Hertling (Hrsg.), The Semantic Web: ESWC 2023 Satellite Events. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 13998 LNCS, Springer Science and Business Media Deutschland GmbH, S. 169-173, 20th Extended Semantic Web Conference, ESWC 2023, Hersonissos, Griechenland, 28 Mai 2023. https://doi.org/10.1007/978-3-031-43458-7_32
Riaz, A., Abdollahi, S., & Gottschalk, S. (2023). Entity Typing with Triples Using Language Models. In C. Pesquita, H. Skaf-Molli, V. Efthymiou, S. Kirrane, A. Ngonga, D. Collarana, R. Cerqueira, M. Alam, C. Trojahn, & S. Hertling (Hrsg.), The Semantic Web: ESWC 2023 Satellite Events (S. 169-173). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13998 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43458-7_32
Riaz A, Abdollahi S, Gottschalk S. Entity Typing with Triples Using Language Models. in Pesquita C, Skaf-Molli H, Efthymiou V, Kirrane S, Ngonga A, Collarana D, Cerqueira R, Alam M, Trojahn C, Hertling S, Hrsg., The Semantic Web: ESWC 2023 Satellite Events. Springer Science and Business Media Deutschland GmbH. 2023. S. 169-173. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-43458-7_32
Riaz, Aniqa ; Abdollahi, Sara ; Gottschalk, Simon. / Entity Typing with Triples Using Language Models. The Semantic Web: ESWC 2023 Satellite Events. Hrsg. / Catia Pesquita ; Hala Skaf-Molli ; Vasilis Efthymiou ; Sabrina Kirrane ; Axel Ngonga ; Diego Collarana ; Renato Cerqueira ; Mehwish Alam ; Cassia Trojahn ; Sven Hertling. Springer Science and Business Media Deutschland GmbH, 2023. S. 169-173 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
Download
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title = "Entity Typing with Triples Using Language Models",
abstract = "Entity Typing is the task of assigning a type to an entity in a knowledge graph. In this paper, we propose ETwT (Entity Typing with Triples), which leverages the triples of an entity, namely its label, description and the property labels used on it. We analyse which language models and classifiers are best suited to this input and compare ETwT{\textquoteright}s performance on coarse-grained and fine-grained entity typing. Our evaluation demonstrates that ETwT is able to predict coarse-grained entity types with an F $$:1$$ score of 0.994, outperforming three baselines.",
author = "Aniqa Riaz and Sara Abdollahi and Simon Gottschalk",
note = "Funding Information: This work was partially funded the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany (“ATTENTION!”, 01MJ22012D). ; 20th Extended Semantic Web Conference, ESWC 2023 ; Conference date: 28-05-2023 Through 01-06-2023",
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Download

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T1 - Entity Typing with Triples Using Language Models

AU - Riaz, Aniqa

AU - Abdollahi, Sara

AU - Gottschalk, Simon

N1 - Funding Information: This work was partially funded the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany (“ATTENTION!”, 01MJ22012D).

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N2 - Entity Typing is the task of assigning a type to an entity in a knowledge graph. In this paper, we propose ETwT (Entity Typing with Triples), which leverages the triples of an entity, namely its label, description and the property labels used on it. We analyse which language models and classifiers are best suited to this input and compare ETwT’s performance on coarse-grained and fine-grained entity typing. Our evaluation demonstrates that ETwT is able to predict coarse-grained entity types with an F $$:1$$ score of 0.994, outperforming three baselines.

AB - Entity Typing is the task of assigning a type to an entity in a knowledge graph. In this paper, we propose ETwT (Entity Typing with Triples), which leverages the triples of an entity, namely its label, description and the property labels used on it. We analyse which language models and classifiers are best suited to this input and compare ETwT’s performance on coarse-grained and fine-grained entity typing. Our evaluation demonstrates that ETwT is able to predict coarse-grained entity types with an F $$:1$$ score of 0.994, outperforming three baselines.

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SN - 9783031434570

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

A2 - Pesquita, Catia

A2 - Skaf-Molli, Hala

A2 - Efthymiou, Vasilis

A2 - Kirrane, Sabrina

A2 - Ngonga, Axel

A2 - Collarana, Diego

A2 - Cerqueira, Renato

A2 - Alam, Mehwish

A2 - Trojahn, Cassia

A2 - Hertling, Sven

PB - Springer Science and Business Media Deutschland GmbH

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