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

<p>In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural tourism industry. Firstly, a bottom-up knowledge graph construction method is designed based on the DGCN relational extraction model, Muhead-CU-FL-BE entity extraction model and Neo4j high-performance graph database. Then, based on the domain and characteristics of traditional culture and rural tourism industry integration, the knowledge graph of rural traditional culture and tourism industry integration is constructed from three aspects: line results, ontology model and description of this paper. Finally, the performance of the knowledge graph constructed in this paper is tested on the relevant dataset, and the visualization analysis of industrial integration is carried out through the constructed knowledge graph. The results show that the overall performance of the relationship extraction algorithm in this paper is around 0.7, the entity extraction algorithm has the best performance, and the overall performance is around 0.8, and the ratio of the public ancestor nodes of 5 times linking in the 5th and 6th layers is greater than 0.65. The centrality of culture and tourism industry, cultural and tourism fusion, high-quality development, culture and tourism, and industry fusion are 0.78, 0.60, 0.58, and 0.80. The centrality of the keywords is 0.78, 0.60, 0.58, and 0.80, respectively, 1.00, and the strength of the salient values of each keyword is concentrated around 1~3.</p>
</abstract>

In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural tourism industry. Firstly, a bottom-up knowledge graph construction method is designed based on the DGCN relational extraction model, Muhead-CU-FL-BE entity extraction model and Neo4j high-performance graph database. Then, based on the domain and characteristics of traditional culture and rural tourism industry integration, the knowledge graph of rural traditional culture and tourism industry integration is constructed from three aspects: line results, ontology model and description of this paper. Finally, the performance of the knowledge graph constructed in this paper is tested on the relevant dataset, and the visualization analysis of industrial integration is carried out through the constructed knowledge graph. The results show that the overall performance of the relationship extraction algorithm in this paper is around 0.7, the entity extraction algorithm has the best performance, and the overall performance is around 0.8, and the ratio of the public ancestor nodes of 5 times linking in the 5th and 6th layers is greater than 0.65. The centrality of culture and tourism industry, cultural and tourism fusion, high-quality development, culture and tourism, and industry fusion are 0.78, 0.60, 0.58, and 0.80. The centrality of the keywords is 0.78, 0.60, 0.58, and 0.80, respectively, 1.00, and the strength of the salient values of each keyword is concentrated around 1~3.

Visualization and Analysis of Knowledge Graph for the Integration of Traditional Culture and Rural Tourism Industry

Applied Mathematics and Nonlinear Sciences

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

In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural...

Visualization and Analysis of Knowledge Graph for the Integration of Traditional Culture and Rural Tourism Industry

Publicado en línea: 29 nov 2023

Páginas: -

Recibido: 25 ene 2023

Aceptado: 09 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01308

Palabras clave
DGCN relational extraction, Muhead-CU-FL-BE entity extraction, Neo4j, knowledge graph, industry convergence

© 2023 Rong Ai et al., published by Sciendo

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

Visualization and Analysis of Knowledge Graph for the Integration of Traditional Culture and Rural Tourism Industry

Publicado en línea: 29 nov 2023

Páginas: -

Recibido: 25 ene 2023

Aceptado: 09 jun 2023

DOI: https://doi.org/10.2478/amns.2023.2.01308

Palabras claveDGCN relational extraction, Muhead-CU-FL-BE entity extraction, Neo4j, knowledge graph, industry convergence

© 2023 Rong Ai et al., published by Sciendo

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

Palabras clave
DGCN relational extraction, Muhead-CU-FL-BE entity extraction, Neo4j, knowledge graph, industry convergence