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<p>This paper is dedicated to designing and constructing a knowledge ontology framework for sports intangible cultural heritage (ICH) resources, aiming to support their preservation and inheritance by integrating and mining sports ICH resources. The study includes the collection of multiple data of sports ICH from various data sources, and constructing ICH knowledge ontology using CIDOC CRM metadata reference model and seven-step method. To enrich the content of the ontology, the TextRank algorithm is used to extract textual critical information and design a domain-specific NER model for sports NRL. In addition, the study adopts the VSM vector space model for text representation and uses an improved hierarchical classification model for text categorization to improve classification accuracy. The study also explores the similarity calculation of concepts in the sports NRM ontology and proposes a semantic similarity calculation formula based on the ontology concepts. Respondents’ willingness to pay was investigated through the conditional value method (CVM) to assess the value of sports NRM tourism resources. Finally, the influencing factors of respondents’ willingness to pay were analyzed using statistical analysis and Logistic regression model, and it was found that they were mainly influenced by the degree of understanding of sports non-heritage resources and the level of education. The results of this study not only provide theoretical and methodological support for the effective integration and excavation of sports non-heritage resources and a new perspective for their protection, inheritance and sustainable development.</p>
</abstract>

This paper is dedicated to designing and constructing a knowledge ontology framework for sports intangible cultural heritage (ICH) resources, aiming to support their preservation and inheritance by integrating and mining sports ICH resources. The study includes the collection of multiple data of sports ICH from various data sources, and constructing ICH knowledge ontology using CIDOC CRM metadata reference model and seven-step method. To enrich the content of the ontology, the TextRank algorithm is used to extract textual critical information and design a domain-specific NER model for sports NRL. In addition, the study adopts the VSM vector space model for text representation and uses an improved hierarchical classification model for text categorization to improve classification accuracy. The study also explores the similarity calculation of concepts in the sports NRM ontology and proposes a semantic similarity calculation formula based on the ontology concepts. Respondents’ willingness to pay was investigated through the conditional value method (CVM) to assess the value of sports NRM tourism resources. Finally, the influencing factors of respondents’ willingness to pay were analyzed using statistical analysis and Logistic regression model, and it was found that they were mainly influenced by the degree of understanding of sports non-heritage resources and the level of education. The results of this study not only provide theoretical and methodological support for the effective integration and excavation of sports non-heritage resources and a new perspective for their protection, inheritance and sustainable development.

Classification and Value Assessment of Sports Intangible Cultural Heritage Resources Combined with Digital Technology

Applied Mathematics and Nonlinear Sciences

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

This paper is dedicated to designing and constructing a knowledge ontology framework for sports intangible cultural heritage (ICH) resources, aiming to support...

Classification and Value Assessment of Sports Intangible Cultural Heritage Resources Combined with Digital Technology

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 12 sty 2024

Przyjęty: 22 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0548

Słowa kluczowe
Vector space model, Logistic regression model, Hierarchical classification model, Semantic similarity, Non-heritage resources

© 2023 Yongjiang Zhang et al., published by Sciendo

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

Classification and Value Assessment of Sports Intangible Cultural Heritage Resources Combined with Digital Technology

Data publikacji: 26 lut 2024

Zakres stron: -

Otrzymano: 12 sty 2024

Przyjęty: 22 sty 2024

DOI: https://doi.org/10.2478/amns-2024-0548

Słowa kluczoweVector space model, Logistic regression model, Hierarchical classification model, Semantic similarity, Non-heritage resources

© 2023 Yongjiang Zhang et al., published by Sciendo

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

Słowa kluczowe
Vector space model, Logistic regression model, Hierarchical classification model, Semantic similarity, Non-heritage resources