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

<p>In order to construct a knowledge graph of Hainan marine intangible cultural heritage, this paper designs the graph database by setting the annotation system and semantic structure of the intangible heritage metadata. For the named entity recognition of the knowledge graph, a model combining long and short-term memory neural networks and Bi-Lstm-CRF with conditional random field is established. The recognized entities are processed based on the Bert pre-training model, and the semantic relations are extracted by combining the cluster search method. Compared with other models, the accuracy rate of the Bs-Spert model for the extraction of semantic relations of the Hainan marine NRM dataset is improved by 0.64%, 0.85% and 0.98% compared with GCNRE, Match Blank and MTDS, respectively. Meanwhile, other metrics of the Bs-Spert model also performed well, with a recall rate of 82.26% and an F1-value of 74.01%. By constructing a knowledge map based on the graph database, we can analyze the current situation of Hainan’s marine NRM more comprehensively and thus propose corresponding countermeasures.</p>
</abstract>

In order to construct a knowledge graph of Hainan marine intangible cultural heritage, this paper designs the graph database by setting the annotation system and semantic structure of the intangible heritage metadata. For the named entity recognition of the knowledge graph, a model combining long and short-term memory neural networks and Bi-Lstm-CRF with conditional random field is established. The recognized entities are processed based on the Bert pre-training model, and the semantic relations are extracted by combining the cluster search method. Compared with other models, the accuracy rate of the Bs-Spert model for the extraction of semantic relations of the Hainan marine NRM dataset is improved by 0.64%, 0.85% and 0.98% compared with GCNRE, Match Blank and MTDS, respectively. Meanwhile, other metrics of the Bs-Spert model also performed well, with a recall rate of 82.26% and an F1-value of 74.01%. By constructing a knowledge map based on the graph database, we can analyze the current situation of Hainan’s marine NRM more comprehensively and thus propose corresponding countermeasures.

The current situation and measures for the protection of marine intangible cultural heritage in Hainan in the context of big data

Applied Mathematics and Nonlinear Sciences

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

In order to construct a knowledge graph of Hainan marine intangible cultural heritage, this paper designs the graph database by setting the annotation system...

The current situation and measures for the protection of marine intangible cultural heritage in Hainan in the context of big data

Data publikacji: 11 paź 2023

Zakres stron: -

Otrzymano: 29 lis 2022

Przyjęty: 21 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00602

Słowa kluczowe
Graph database, Knowledge graph, Model of Bi-Lstm-CRF, Bs-Spert model, Intangible cultural heritage

© 2023 Qiongfei Deng, published by Sciendo

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

The current situation and measures for the protection of marine intangible cultural heritage in Hainan in the context of big data

Data publikacji: 11 paź 2023

Zakres stron: -

Otrzymano: 29 lis 2022

Przyjęty: 21 kwi 2023

DOI: https://doi.org/10.2478/amns.2023.2.00602

Słowa kluczoweGraph database, Knowledge graph, Model of Bi-Lstm-CRF, Bs-Spert model, Intangible cultural heritage

© 2023 Qiongfei Deng, published by Sciendo

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

Słowa kluczowe
Graph database, Knowledge graph, Model of Bi-Lstm-CRF, Bs-Spert model, Intangible cultural heritage