Standard
Message passing for hyper-relational knowledge graphs. / Galkin, Mikhail; Trivedi, Priyansh; Maheshwari, Gaurav et al.
EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Hrsg. / Bonnie Webber; Trevor Cohn; Yulan He; Yang Liu. Association for Computational Linguistics (ACL), 2020. S. 7346-7359 (EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
Galkin, M, Trivedi, P, Maheshwari, G
, Usbeck, R & Lehmann, J 2020,
Message passing for hyper-relational knowledge graphs. in B Webber, T Cohn, Y He & Y Liu (Hrsg.),
EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, Association for Computational Linguistics (ACL), S. 7346-7359, 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Virtual, Online,
16.11.20.
https://doi.org/10.48550/arXiv.2009.10847,
https://doi.org/10.18653/v1/2020.emnlp-main.596APA
Galkin, M., Trivedi, P., Maheshwari, G.
, Usbeck, R., & Lehmann, J. (2020).
Message passing for hyper-relational knowledge graphs. In B. Webber, T. Cohn, Y. He, & Y. Liu (Hrsg.),
EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (S. 7346-7359). (EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference). Association for Computational Linguistics (ACL).
https://doi.org/10.48550/arXiv.2009.10847,
https://doi.org/10.18653/v1/2020.emnlp-main.596Vancouver
Galkin M, Trivedi P, Maheshwari G
, Usbeck R, Lehmann J.
Message passing for hyper-relational knowledge graphs. in Webber B, Cohn T, He Y, Liu Y, Hrsg., EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2020. S. 7346-7359. (EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference). doi: 10.48550/arXiv.2009.10847, 10.18653/v1/2020.emnlp-main.596
Bibtex
@inbook{f7459fa44c2b4d81b74314aae2d669b4,
title = "Message passing for hyper-relational knowledge graphs",
abstract = "Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - STARE capable of modeling such hyper-relational KGs. Unlike existing approaches, STARE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that STARE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.",
keywords = "Informatics, Business informatics",
author = "Mikhail Galkin and Priyansh Trivedi and Gaurav Maheshwari and Ricardo Usbeck and Jens Lehmann",
note = "Publisher Copyright: {\textcopyright} 2020 Association for Computational Linguistics.; 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, EMNLP 2020 ; Conference date: 16-11-2020 Through 20-11-2020",
year = "2020",
month = jan,
day = "1",
doi = "10.48550/arXiv.2009.10847",
language = "English",
series = "EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
publisher = "Association for Computational Linguistics (ACL)",
pages = "7346--7359",
editor = "Bonnie Webber and Trevor Cohn and Yulan He and Yang Liu",
booktitle = "EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference",
address = "United States",
url = "https://2020.emnlp.org",
}
RIS
TY - CHAP
T1 - Message passing for hyper-relational knowledge graphs
AU - Galkin, Mikhail
AU - Trivedi, Priyansh
AU - Maheshwari, Gaurav
AU - Usbeck, Ricardo
AU - Lehmann, Jens
N1 - Publisher Copyright:
© 2020 Association for Computational Linguistics.
PY - 2020/1/1
Y1 - 2020/1/1
N2 - Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - STARE capable of modeling such hyper-relational KGs. Unlike existing approaches, STARE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that STARE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.
AB - Hyper-relational knowledge graphs (KGs) (e.g., Wikidata) enable associating additional key-value pairs along with the main triple to disambiguate, or restrict the validity of a fact. In this work, we propose a message passing based graph encoder - STARE capable of modeling such hyper-relational KGs. Unlike existing approaches, STARE can encode an arbitrary number of additional information (qualifiers) along with the main triple while keeping the semantic roles of qualifiers and triples intact. We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K. Our experiments demonstrate that STARE based LP model outperforms existing approaches across multiple benchmarks. We also confirm that leveraging qualifiers is vital for link prediction with gains up to 25 MRR points compared to triple-based representations.
KW - Informatics
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85106090678&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/bfb53193-4630-3af2-97d6-f3834fa3d874/
U2 - 10.48550/arXiv.2009.10847
DO - 10.48550/arXiv.2009.10847
M3 - Article in conference proceedings
AN - SCOPUS:85106090678
T3 - EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
SP - 7346
EP - 7359
BT - EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
A2 - Webber, Bonnie
A2 - Cohn, Trevor
A2 - He, Yulan
A2 - Liu, Yang
PB - Association for Computational Linguistics (ACL)
T2 - 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020
Y2 - 16 November 2020 through 20 November 2020
ER -
