Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 8, Issue 2


Published
on

November 29, 2023


Pages


DOI

Article

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

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Authors

Rong Ai Affiliation:
Institute of Cultural Industries, Economics and Management College, Handan University, Handan, Hebei, 056000, China.
, Jianwei Song Affiliation:
Institute of Cultural Industries, Economics and Management College, Handan University, Handan, Hebei, 056000, China.
and Xiaowei Xie Affiliation:
State Grid Handan Power Supply Company, Handan, Hebei, 056000, China.


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.


Keywords

DGCN relational extraction, Muhead-CU-FL-BE entity extraction, Neo4j, knowledge graph, industry convergence, 01A13


Citation

Ai, R., Song, J., & Xie, X. (2023). Visualization and analysis of knowledge graph for the integration of traditional culture and rural tourism industry. Applied Mathematics and Nonlinear Sciences, 8(2). https://doi.org/10.2478/amns.2023.2.01308
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