@inproceedings{yanez-etal-2023-review,
title = "A Review in Knowledge Extraction from Knowledge Bases",
author = "Yanez, Fabio and
Montoyo, Andr{\'e}s and
Gutierrez, Yoan and
Mu{\~n}oz, Rafael and
Suarez, Armando",
editor = "Mitkov, Ruslan and
Angelova, Galia",
booktitle = "Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing",
month = sep,
year = "2023",
address = "Varna, Bulgaria",
publisher = "INCOMA Ltd., Shoumen, Bulgaria",
url = "https://aclanthology.org/2023.ranlp-1.12",
pages = "109--116",
abstract = "Generative language models achieve the state of the art in many tasks within natural language processing (NLP). Although these models correctly capture syntactic information, they fail to interpret knowledge (semantics). Moreover, the lack of interpretability of these models promotes the use of other technologies as a replacement or complement to generative language models. This is the case with research focused on incorporating knowledge by resorting to knowledge bases mainly in the form of graphs. The generation of large knowledge graphs is carried out with unsupervised or semi-supervised techniques, which promotes the validation of this knowledge with the same type of techniques due to the size of the generated databases. In this review, we will explain the different techniques used to test and infer knowledge from graph structures with machine learning algorithms. The motivation of validating and inferring knowledge is to use correct knowledge in subsequent tasks with improved embeddings.",
}
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<abstract>Generative language models achieve the state of the art in many tasks within natural language processing (NLP). Although these models correctly capture syntactic information, they fail to interpret knowledge (semantics). Moreover, the lack of interpretability of these models promotes the use of other technologies as a replacement or complement to generative language models. This is the case with research focused on incorporating knowledge by resorting to knowledge bases mainly in the form of graphs. The generation of large knowledge graphs is carried out with unsupervised or semi-supervised techniques, which promotes the validation of this knowledge with the same type of techniques due to the size of the generated databases. In this review, we will explain the different techniques used to test and infer knowledge from graph structures with machine learning algorithms. The motivation of validating and inferring knowledge is to use correct knowledge in subsequent tasks with improved embeddings.</abstract>
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%0 Conference Proceedings
%T A Review in Knowledge Extraction from Knowledge Bases
%A Yanez, Fabio
%A Montoyo, Andrés
%A Gutierrez, Yoan
%A Muñoz, Rafael
%A Suarez, Armando
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
%D 2023
%8 September
%I INCOMA Ltd., Shoumen, Bulgaria
%C Varna, Bulgaria
%F yanez-etal-2023-review
%X Generative language models achieve the state of the art in many tasks within natural language processing (NLP). Although these models correctly capture syntactic information, they fail to interpret knowledge (semantics). Moreover, the lack of interpretability of these models promotes the use of other technologies as a replacement or complement to generative language models. This is the case with research focused on incorporating knowledge by resorting to knowledge bases mainly in the form of graphs. The generation of large knowledge graphs is carried out with unsupervised or semi-supervised techniques, which promotes the validation of this knowledge with the same type of techniques due to the size of the generated databases. In this review, we will explain the different techniques used to test and infer knowledge from graph structures with machine learning algorithms. The motivation of validating and inferring knowledge is to use correct knowledge in subsequent tasks with improved embeddings.
%U https://aclanthology.org/2023.ranlp-1.12
%P 109-116
Markdown (Informal)
[A Review in Knowledge Extraction from Knowledge Bases](https://aclanthology.org/2023.ranlp-1.12) (Yanez et al., RANLP 2023)
ACL
- Fabio Yanez, Andrés Montoyo, Yoan Gutierrez, Rafael Muñoz, and Armando Suarez. 2023. A Review in Knowledge Extraction from Knowledge Bases. In Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing, pages 109–116, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.