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

<p>In this paper, we first construct a cognitive neural network and use the knowledge representation learning technique for knowledge complementation and relation extraction to semantically match head-tail entity pairs with relations and accurately model the semantic relationships between entities and relations. Then, the vectors of each entity are mapped onto the hyperplane of a given relation by the cognitive mapping translation model, and vector translation operations are performed on the hyperplane. Finally, this paper’s translation model is compared with other models to compare the semantic representation translation, task processing function, and model complexity of each model so as to verify the superiority of this paper’s model for minority culture outreach translation. The results show that this paper’s method increases 3.4 and 1.2 BLEU values on average in the four translation tasks compared to the Imagination model and VAG model, respectively. When the number of iterations is 40, the MRR value of this paper’s model is 0.345, which is at least 0.1-0.5 more than the other models, thus indicating that this paper’s model has a better performance in outreach translation.</p>
</abstract>

In this paper, we first construct a cognitive neural network and use the knowledge representation learning technique for knowledge complementation and relation extraction to semantically match head-tail entity pairs with relations and accurately model the semantic relationships between entities and relations. Then, the vectors of each entity are mapped onto the hyperplane of a given relation by the cognitive mapping translation model, and vector translation operations are performed on the hyperplane. Finally, this paper’s translation model is compared with other models to compare the semantic representation translation, task processing function, and model complexity of each model so as to verify the superiority of this paper’s model for minority culture outreach translation. The results show that this paper’s method increases 3.4 and 1.2 BLEU values on average in the four translation tasks compared to the Imagination model and VAG model, respectively. When the number of iterations is 40, the MRR value of this paper’s model is 0.345, which is at least 0.1-0.5 more than the other models, thus indicating that this paper’s model has a better performance in outreach translation.

A Study of Minority Culture Outreach Translation Based on Cognitive Mapping Construction

Applied Mathematics and Nonlinear Sciences

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

In this paper, we first construct a cognitive neural network and use the knowledge representation learning technique for knowledge complementation and relation...

A Study of Minority Culture Outreach Translation Based on Cognitive Mapping Construction

Pubblicato online: 16 dic 2023

Pagine: -

Ricevuto: 14 feb 2023

Accettato: 26 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01511

Parole chiave
Cognitive mapping, Cognitive neural network, Knowledge representation learning, Semantic matching, Outreach translation

© 2023 Ling Jin, published by Sciendo

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

A Study of Minority Culture Outreach Translation Based on Cognitive Mapping Construction

Pubblicato online: 16 dic 2023

Pagine: -

Ricevuto: 14 feb 2023

Accettato: 26 giu 2023

DOI: https://doi.org/10.2478/amns.2023.2.01511

Parole chiaveCognitive mapping, Cognitive neural network, Knowledge representation learning, Semantic matching, Outreach translation

© 2023 Ling Jin, published by Sciendo

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

Parole chiave
Cognitive mapping, Cognitive neural network, Knowledge representation learning, Semantic matching, Outreach translation