Research of Chinese intangible cultural heritage knowledge graph construction and attribute value extraction with graph attention network

Fan, Tao; Wang, Hao

doi:10.1016/j.ipm.2021.102753

Cited by 37 publications

(11 citation statements)

References 21 publications

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“…The knowledge graph for power transformer maintenance is designed for scenarios such as routine maintenance, fault troubleshooting, knowledge retrieval, fault information inquiry, and decision support for power transformers. It mainly consists of the ontology layer, entity layer, relationships, and attributes [17].…”

Section: Construction Methods For Power Transformer Maintenance Knowl...mentioning

confidence: 99%

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

Yang,

Meng,

Zhang

2024

IET Electric Power Appl

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Currently, the digital transformation of the power grid is underway, and the intelligent health management technology for power transformers is rapidly advancing. However, there are issues in the operation and maintenance process, such as weak information correlation and low decision‐making efficiency. Knowledge graphs have been applied in other industrial fields, such as spacecraft maintenance, to significantly improve knowledge query efficiency. However, there is a lack of literature on knowledge graph construction in the field of power transformer operation and maintenance. Additionally, there is limited publicly available data and difficulties in effectively mining operation and maintenance knowledge in this field. A method for constructing a knowledge graph for power transformer operation and maintenance based on ALBERT is proposed. Firstly, publicly available literature in the field of power transformers is collected, and a sample enhancement method using regular matching is used to enrich the semi‐structured corpora, such as power system accident investigation reports, to construct a training dataset for power transformer operation and maintenance. Then, the ALBERT‐BiLSTM‐CRF deep learning algorithm is applied to extract power transformer operation and maintenance entities from the relevant literature and accident investigation reports, and this method is compared with traditional deep learning algorithms to demonstrate its superiority. Subsequently, the ALBERT‐BiLSTM‐Attention deep learning algorithm, which incorporates ALBERT and attention mechanism, is utilised to extract relationships between power transformer operation and maintenance entities. Compared to other deep learning algorithms, this algorithm demonstrates better performance in the domain‐specific texts of power transformer operation and maintenance. Finally, the Neo4j graph database is used to visualise and present the knowledge graph, enabling decision support based on the power transformer operation and maintenance knowledge graph.

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Section: Construction Methods For Power Transformer Maintenance Knowl...mentioning

confidence: 99%

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

Yang,

Meng,

Zhang

2024

IET Electric Power Appl

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“…Domain-generic ontologies like the CIDOC Conceptual Reference Model (CIDOC CRM), 2 the Historical Context Ontology (HiCO) 3 and the Europeana Data Model (EDM), 4 have been used for information integration, exchange, sharing, and reuse in the CH domain. Project-applied ontologies, such as ArCo ontology (ArCo) 5 and CrossCult ontology (CrossCult), 6 have been developed for specific projects. Top-level ontologies like Friend of a Friend (FOAF), 7 GeoNames, 8 Visual Representation Ontology (VRO), 9 Time ontology (Time), 10 and the Ontology for Media 1 https:// pro.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, domain knowledge graphs (DKGs) have gained prominence in preserving and developing intangible cultural heritage (ICH) [1,2]. DKGs refine, extract, correlate, and integrate data related to ICH projects, helping audiences interpret and appreciate the essence of ICH [3][4][5]. Currently, the primary focus of ICH project DKGs is to effectively organize information resources and link multi-source heterogeneous data [6].…”

Section: Introductionmentioning

confidence: 99%

A study on the construction of knowledge graph of Yunjin video resources under productive conservation

Yuan

et al. 2023

Herit Sci

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Nanjing Yunjin, a highly representative Chinese silk weaving handicraft, was included in the Representative List of Intangible Cultural Heritage of Humanity in 2009. However, due to modern silk weaving technology advancements, aesthetic style evolution, and inadequate public recognition of Yunjin culture, the art faces a decline in market recognition and practitioners, posing a risk to its preservation. Addressing this issue necessitates product innovation, efficient knowledge storage, management, and utilization, and enhancing public cultural identity for Yunjin. Following the government’s “productive conservation” concept for intangible cultural heritage (ICH) projects in the handicraft category, this study uses Yunjin video resources as the primary data source. It constructs a domain knowledge graph (DKG) using an ontological approach to effectively and systematically preserve Yunjin knowledge. Furthermore, the study leverages Neo4j network topology to reveal intricate and diverse relationships within Yunjin knowledge, uncovering rich cultural connotations. Lastly, Cypher is employed for semantic queries, graph visualization, and domain expert evaluation. Evaluation results indicate that the constructed Yunjin DKG meets quality standards, supporting the development of products that align with market aesthetics while preserving Yunjin’s intrinsic cultural values. This approach fosters a complementary relationship between economic benefits and ICH. Additionally, the Yunjin DKG application presents a technical path for knowledge interconnection, integration, and discovery within ICH projects in the handicraft category.

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“…The first was the image recognition investigation of ICH by DL technology based on this [ 9 ]. Aiming at the image recognition of ICH, scholars proposed many DL-based methods, such as CNN-based image classification methods, Recurrent Neural Network (RNN)-based sequence classification methods, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

Application interface design of Chongqing intangible cultural heritage based on deep learning

Liu,

Cheng,

2023

Heliyon

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Research of Chinese intangible cultural heritage knowledge graph construction and attribute value extraction with graph attention network

Cited by 37 publications

References 21 publications

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs

A study on the construction of knowledge graph of Yunjin video resources under productive conservation

Application interface design of Chongqing intangible cultural heritage based on deep learning

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