<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With China’s economic and social development, material living standards have improved, leading to a dramatic increase in the demand for beef and higher requirements for its quality. In this context, molecular breeding technology for beef cattle has gradually become a research hotspot. This paper designs the entire process of knowledge mapping for molecular breeding technology of beef cattle in China, focusing on the molecular breeding knowledge extraction model based on BERT. It also integrates models and algorithms used in knowledge alignment, complementation, and storage to construct the knowledge graph. After testing the performance of the knowledge extraction algorithm, the development history of molecular breeding technology for beef cattle in China was analyzed. The results show that from 2013 to 2023, research related to molecular breeding technology for beef cattle in China exhibited a slow, then rapid growth trend. The terms “molecular breeding” and “beef cattle” appeared most frequently, with 218 and 165 occurrences, respectively, and had the highest centrality. Keywords with high occurrences include “comparative genes” (11.3068), “functional genes” (9.2376), “genome-wide association” (8.3197), and “genome-wide selection” (6.9012), all closely related to the development of molecular breeding technology for beef cattle in China. The prediction of future trends in molecular breeding technology for beef cattle in China is significantly influenced by this study.</p>
</abstract>

With China’s economic and social development, material living standards have improved, leading to a dramatic increase in the demand for beef and higher requirements for its quality. In this context, molecular breeding technology for beef cattle has gradually become a research hotspot. This paper designs the entire process of knowledge mapping for molecular breeding technology of beef cattle in China, focusing on the molecular breeding knowledge extraction model based on BERT. It also integrates models and algorithms used in knowledge alignment, complementation, and storage to construct the knowledge graph. After testing the performance of the knowledge extraction algorithm, the development history of molecular breeding technology for beef cattle in China was analyzed. The results show that from 2013 to 2023, research related to molecular breeding technology for beef cattle in China exhibited a slow, then rapid growth trend. The terms “molecular breeding” and “beef cattle” appeared most frequently, with 218 and 165 occurrences, respectively, and had the highest centrality. Keywords with high occurrences include “comparative genes” (11.3068), “functional genes” (9.2376), “genome-wide association” (8.3197), and “genome-wide selection” (6.9012), all closely related to the development of molecular breeding technology for beef cattle in China. The prediction of future trends in molecular breeding technology for beef cattle in China is significantly influenced by this study.

Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With China’s economic and social development, material living standards have improved, leading to a dramatic increase in the demand for beef and higher...

Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 13 apr 2024

Accettato: 27 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1884

Parole chiave
Beef cattle molecular breeding, Knowledge map, Knowledge extraction, Knowledge alignment, Knowledge complementation, Knowledge storage

© 2024 Hongbo Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Knowledge mapping analysis of the development of molecular breeding technology for beef cattle in China

Pubblicato online: 05 ago 2024

Pagine: -

Ricevuto: 13 apr 2024

Accettato: 27 giu 2024

DOI: https://doi.org/10.2478/amns-2024-1884

Parole chiaveBeef cattle molecular breeding, Knowledge map, Knowledge extraction, Knowledge alignment, Knowledge complementation, Knowledge storage

© 2024 Hongbo Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Beef cattle molecular breeding, Knowledge map, Knowledge extraction, Knowledge alignment, Knowledge complementation, Knowledge storage