A Review in Knowledge Extraction from Knowledge Bases

Fabio Yanez, Andrés Montoyo, Yoan Gutierrez, Rafael Muñoz, Armando Suarez

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.
Anthology ID:
2023.ranlp-1.12
Volume:
Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing
Month:
September
Year:
2023
Address:
Varna, Bulgaria
Editors:
Ruslan Mitkov, Galia Angelova
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd., Shoumen, Bulgaria
Note:
Pages:
109–116
Language:
URL:
https://aclanthology.org/2023.ranlp-1.12
DOI:
Bibkey:
Cite (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.
Cite (Informal):
A Review in Knowledge Extraction from Knowledge Bases (Yanez et al., RANLP 2023)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2023.ranlp-1.12.pdf
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