@inproceedings{sakhovskiy-etal-2024-textgraphs,
title = "{T}ext{G}raphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering",
author = {Sakhovskiy, Andrey and
Salnikov, Mikhail and
Nikishina, Irina and
Usmanova, Aida and
Kraft, Angelie and
M{\"o}ller, Cedric and
Banerjee, Debayan and
Huang, Junbo and
Jiang, Longquan and
Abdullah, Rana and
Yan, Xi and
Ustalov, Dmitry and
Tutubalina, Elena and
Usbeck, Ricardo and
Panchenko, Alexander},
editor = "Ustalov, Dmitry and
Gao, Yanjun and
Pachenko, Alexander and
Tutubalina, Elena and
Nikishina, Irina and
Ramesh, Arti and
Sakhovskiy, Andrey and
Usbeck, Ricardo and
Penn, Gerald and
Valentino, Marco",
booktitle = "Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.textgraphs-1.9",
pages = "116--125",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering
%A Sakhovskiy, Andrey
%A Salnikov, Mikhail
%A Nikishina, Irina
%A Usmanova, Aida
%A Kraft, Angelie
%A Möller, Cedric
%A Banerjee, Debayan
%A Huang, Junbo
%A Jiang, Longquan
%A Abdullah, Rana
%A Yan, Xi
%A Ustalov, Dmitry
%A Tutubalina, Elena
%A Usbeck, Ricardo
%A Panchenko, Alexander
%Y Ustalov, Dmitry
%Y Gao, Yanjun
%Y Pachenko, Alexander
%Y Tutubalina, Elena
%Y Nikishina, Irina
%Y Ramesh, Arti
%Y Sakhovskiy, Andrey
%Y Usbeck, Ricardo
%Y Penn, Gerald
%Y Valentino, Marco
%S Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F sakhovskiy-etal-2024-textgraphs
%X 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.
%U https://aclanthology.org/2024.textgraphs-1.9
%P 116-125
Markdown (Informal)
[TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering](https://aclanthology.org/2024.textgraphs-1.9) (Sakhovskiy et al., TextGraphs-WS 2024)
ACL
- Andrey Sakhovskiy, Mikhail Salnikov, Irina Nikishina, Aida Usmanova, Angelie Kraft, Cedric Möller, Debayan Banerjee, Junbo Huang, Longquan Jiang, Rana Abdullah, Xi Yan, Dmitry Ustalov, Elena Tutubalina, Ricardo Usbeck, and Alexander Panchenko. 2024. TextGraphs 2024 Shared Task on Text-Graph Representations for Knowledge Graph Question Answering. In Proceedings of TextGraphs-17: Graph-based Methods for Natural Language Processing, pages 116–125, Bangkok, Thailand. Association for Computational Linguistics.