Standard
TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering. / Sakhovskiy, Andrey; Salnikov, Mikhail; Nikishina, Irina et al.
Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing: Graph-Based Methods for Natural Language Processing, 62nd Annual Meeting of the Association of Computational Linguistics. ed. / Dmitry Ustalov; Yanjun Gao; Alexander Pachenko; Elena Tutubalina; Irina Nikishina; Arti Ramesh; Andrey Sakhovskiy; Ricardo Usbeck; Gerald Penn; Marco Valentino. Kerrville: Association for Computational Linguistics (ACL), 2024. p. 116-125.
Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review
Harvard
Sakhovskiy, A, Salnikov, M, Nikishina, I
, Usmanova, A, Kraft, A, Möller, C
, Banerjee, D, Huang, J, Jiang, L
, Abdullah, R, Yan, X, Ustalov, D, Tutubalina, E
, Usbeck, R & Panchenko, A 2024,
TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering. in D Ustalov, Y Gao, A Pachenko, E Tutubalina, I Nikishina, A Ramesh, A Sakhovskiy, R Usbeck, G Penn & M Valentino (eds),
Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing: Graph-Based Methods for Natural Language Processing, 62nd Annual Meeting of the Association of Computational Linguistics. Association for Computational Linguistics (ACL), Kerrville, pp. 116-125, TextGraphs-17, Bangkok, Thailand,
15.08.24. <
https://aclanthology.org/2024.textgraphs-1.9>
APA
Sakhovskiy, A., Salnikov, M., Nikishina, I.
, Usmanova, A., Kraft, A., Möller, C.
, Banerjee, D., Huang, J., Jiang, L.
, Abdullah, R., Yan, X., Ustalov, D., Tutubalina, E.
, Usbeck, R., & Panchenko, A. (2024).
TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering. In D. Ustalov, Y. Gao, A. Pachenko, E. Tutubalina, I. Nikishina, A. Ramesh, A. Sakhovskiy, R. Usbeck, G. Penn, & M. Valentino (Eds.),
Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing: Graph-Based Methods for Natural Language Processing, 62nd Annual Meeting of the Association of Computational Linguistics (pp. 116-125). Association for Computational Linguistics (ACL).
https://aclanthology.org/2024.textgraphs-1.9Vancouver
Sakhovskiy A, Salnikov M, Nikishina I
, Usmanova A, Kraft A, Möller C et al.
TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering. In Ustalov D, Gao Y, Pachenko A, Tutubalina E, Nikishina I, Ramesh A, Sakhovskiy A, Usbeck R, Penn G, Valentino M, editors, Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing: Graph-Based Methods for Natural Language Processing, 62nd Annual Meeting of the Association of Computational Linguistics. Kerrville: Association for Computational Linguistics (ACL). 2024. p. 116-125
Bibtex
@inbook{8b2f000ca41a47ed9231e8ac28940620,
title = "TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering",
abstract = "This paper describes the results of the Knowledge Graph Question Answering (KGQA) shared task that was co-located with the TextGraphs 2024 workshop. In this task, given a textual question and a list of entities with the corresponding KG subgraphs, the participating system should choose the entity that correctly answers the question. Our competition attracted thirty teams, four of which outperformed our strong ChatGPT-based zero-shot baseline. In this paper, we overview the participating systems and analyze their performance according to a large-scale automatic evaluation. To the best of our knowledge, this is the first competition aimed at the KGQA problem using the interaction between large language models (LLMs) and knowledge graphs.",
keywords = "Business informatics",
author = "Andrey Sakhovskiy and Mikhail Salnikov and Irina Nikishina and Aida Usmanova and Angelie Kraft and Cedric M{\"o}ller and Debayan Banerjee and Junbo Huang and Longquan Jiang and Rana Abdullah and Xi Yan and Dmitry Ustalov and Elena Tutubalina and Ricardo Usbeck and Alexander Panchenko",
note = "Publisher Copyright: {\textcopyright} 2024 Association for Computational Linguistics.; TextGraphs-17 : Graph-based Methods for Natural Language Processing ; Conference date: 15-08-2024 Through 15-08-2024",
year = "2024",
month = aug,
day = "1",
language = "English",
pages = "116--125",
editor = "Dmitry Ustalov and Yanjun Gao and Alexander Pachenko and Elena Tutubalina and Irina Nikishina and Arti Ramesh and Andrey Sakhovskiy and Ricardo Usbeck and Gerald Penn and Marco Valentino",
booktitle = "Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing",
publisher = "Association for Computational Linguistics (ACL)",
address = "United States",
url = "https://sites.google.com/view/textgraphs2024",
}
RIS
TY - CHAP
T1 - TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering
AU - Sakhovskiy, Andrey
AU - Salnikov, Mikhail
AU - Nikishina, Irina
AU - Usmanova, Aida
AU - Kraft, Angelie
AU - Möller, Cedric
AU - Banerjee, Debayan
AU - Huang, Junbo
AU - Jiang, Longquan
AU - Abdullah, Rana
AU - Yan, Xi
AU - Ustalov, Dmitry
AU - Tutubalina, Elena
AU - Usbeck, Ricardo
AU - Panchenko, Alexander
N1 - Conference code: 17
PY - 2024/8/1
Y1 - 2024/8/1
N2 - This paper describes the results of the Knowledge Graph Question Answering (KGQA) shared task that was co-located with the TextGraphs 2024 workshop. In this task, given a textual question and a list of entities with the corresponding KG subgraphs, the participating system should choose the entity that correctly answers the question. Our competition attracted thirty teams, four of which outperformed our strong ChatGPT-based zero-shot baseline. In this paper, we overview the participating systems and analyze their performance according to a large-scale automatic evaluation. To the best of our knowledge, this is the first competition aimed at the KGQA problem using the interaction between large language models (LLMs) and knowledge graphs.
AB - This paper describes the results of the Knowledge Graph Question Answering (KGQA) shared task that was co-located with the TextGraphs 2024 workshop. In this task, given a textual question and a list of entities with the corresponding KG subgraphs, the participating system should choose the entity that correctly answers the question. Our competition attracted thirty teams, four of which outperformed our strong ChatGPT-based zero-shot baseline. In this paper, we overview the participating systems and analyze their performance according to a large-scale automatic evaluation. To the best of our knowledge, this is the first competition aimed at the KGQA problem using the interaction between large language models (LLMs) and knowledge graphs.
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85204909366&partnerID=8YFLogxK
M3 - Article in conference proceedings
SP - 116
EP - 125
BT - Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing
A2 - Ustalov, Dmitry
A2 - Gao, Yanjun
A2 - Pachenko, Alexander
A2 - Tutubalina, Elena
A2 - Nikishina, Irina
A2 - Ramesh, Arti
A2 - Sakhovskiy, Andrey
A2 - Usbeck, Ricardo
A2 - Penn, Gerald
A2 - Valentino, Marco
PB - Association for Computational Linguistics (ACL)
CY - Kerrville
T2 - TextGraphs-17
Y2 - 15 August 2024 through 15 August 2024
ER -
