@inproceedings{mirzapour-etal-2022-introducing,
title = "Introducing {R}ezo{JDM}16k: a {F}rench {K}nowledge{G}raph {D}ata{S}et for Link Prediction",
author = "Mirzapour, Mehdi and
Ragheb, Waleed and
Saeedizade, Mohammad Javad and
Cousot, Kevin and
Jacquenet, Helene and
Carbon, Lawrence and
Lafourcade, Mathieu",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2022.lrec-1.553",
pages = "5163--5169",
abstract = "Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM. With 16k nodes, 832k triplets, and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering, and recommendation systems. Moreover, we provide strong knowledge graph embedding baselines that are used in link prediction tasks for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.",
}
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%0 Conference Proceedings
%T Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction
%A Mirzapour, Mehdi
%A Ragheb, Waleed
%A Saeedizade, Mohammad Javad
%A Cousot, Kevin
%A Jacquenet, Helene
%A Carbon, Lawrence
%A Lafourcade, Mathieu
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Thirteenth Language Resources and Evaluation Conference
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F mirzapour-etal-2022-introducing
%X Knowledge graphs applications, in industry and academia, motivate substantial research directions towards large-scale information extraction from various types of resources. Nowadays, most of the available knowledge graphs are either in English or multilingual. In this paper, we introduce RezoJDM16k, a French knowledge graph dataset based on RezoJDM. With 16k nodes, 832k triplets, and 53 relation types, RezoJDM16k can be employed in many NLP downstream tasks for the French language such as machine translation, question-answering, and recommendation systems. Moreover, we provide strong knowledge graph embedding baselines that are used in link prediction tasks for future benchmarking. Compared to the state-of-the-art English knowledge graph datasets used in link prediction, RezoJDM16k shows a similar promising predictive behavior.
%U https://aclanthology.org/2022.lrec-1.553
%P 5163-5169
Markdown (Informal)
[Introducing RezoJDM16k: a French KnowledgeGraph DataSet for Link Prediction](https://aclanthology.org/2022.lrec-1.553) (Mirzapour et al., LREC 2022)
ACL