Construction of a COVID-19 Pandemic Situation Knowledge Graph Considering Spatial Relationships: A Case Study of Guangzhou, China

Yang, Xiaorui; Li, Weihong; Chen, Yebin; Guo, Ying

doi:10.3390/ijgi11110561

Cited by 3 publications

(2 citation statements)

References 54 publications

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“…A knowledge graph (KG) is a structured semantic knowledge base used to describe concepts and their interrelationships in the physical world in symbolic form. Its basic constituent unit is the triplet of "entity-relationship-entity", as well as the entity and its related attribute-value pairs [26]. Entities relate to each other through relations to form a network knowledge structure.…”

Section: Preliminary Establishment Of Knowledge Graphmentioning

confidence: 99%

Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder

Fang,

Chen,

Liang

et al. 2023

WEVJ

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Research on manufacturing components for electric vehicles plays a vital role in their development. Furthermore, significant advancements in additive manufacturing processes have revolutionized the production of various parts. By establishing a system that enables the recovery, processing, and reuse of metal powders essential for additive manufacturing, we can achieve sustainable production of electric vehicles. This approach holds immense importance in terms of reducing manufacturing costs, expanding the market, and safeguarding the environment. In this study, we developed an additive manufacturing system for recycled metal powders, encompassing powder variety, properties, processing, manufacturing, component properties, and applications. This system was used to create a knowledge graph providing a convenient resource for researchers to understand the entire procedure from recycling to application. To improve the graph’s accuracy, we employed ChatGPT and BERT training. We also demonstrated the knowledge graph’s utility by processing recycled 316 L stainless steel powders and assessing their quality through image processing. This experiment serves as a practical example of recycling and analyzing powders using the established knowledge graph.

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Section: Preliminary Establishment Of Knowledge Graphmentioning

confidence: 99%

Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder

Fang,

Chen,

Liang

et al. 2023

WEVJ

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show abstract

“…In this paper, we first use the Harbin Institute of Technology (HIT) Language Technology Platform (LTP) tool to preprocess the input sentences [37], and then obtain the word labeling information and syntactic dependency information of the preprocessed sentence sequences. Syntactic dependency analysis can identify the syntactic structure of a sentence or the dependencies between words in a sentence.…”

Section: Graph Embedding Representation Improvementmentioning

confidence: 99%

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

Liu,

Zhang,

Zheng

et al. 2023

Electronics

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Presently, road and traffic control construction on most university campuses cannot keep up with the growth of the universities. Campus roads are not very wide, crossings do not have lights, and there are no full-time traffic management personnel. Teachers and students are prone to forming a peak flow of people when going to and from classes. This has led to a constant stream of traffic accidents. It is critical to conduct a comprehensive analysis of this issue by utilizing voluminous data pertaining to school traffic incidents in order to safeguard the lives of faculty and students. In the case of domestic universities, fewer studies have studied knowledge graph construction methods for traffic safety incidents. In event knowledge graph construction, the reasonable release and recycling of computational resources are inefficient, and existing entity–relationship joint extraction methods are unable to deal with ternary overlapping and entity boundary ambiguity problems in relationship extraction. In response to the above problems, this paper proposes a knowledge graph construction method for university on-campus traffic safety events with improved dynamic resource scheduling algorithms and multi-layer semantic graph convolutional neural networks. The experiment’s results show that the proposed dynamic computational resource scheduling method increases GPU and CPU use by 25% and 9%. On the public dataset, the proposed data extraction model’s F1 scores for event triples increase by 1.3% on the NYT dataset and by 0.4% on the WebNLG dataset. This method can help the relevant university personnel in dealing with unexpected traffic incidents and reduce the impact on public opinion.

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Near Relationship Enhanced Multisourced Data Fusion Method for Voice-Interactive Indoor Positioning

Wang,

Fan,

Wang

et al. 2024

IEEE Internet Things J.

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Construction of a COVID-19 Pandemic Situation Knowledge Graph Considering Spatial Relationships: A Case Study of Guangzhou, China

Cited by 3 publications

References 54 publications

Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder

Knowledge Graph Learning for Vehicle Additive Manufacturing of Recycled Metal Powder

Construction of an Event Knowledge Graph Based on a Dynamic Resource Scheduling Optimization Algorithm and Semantic Graph Convolutional Neural Networks

Near Relationship Enhanced Multisourced Data Fusion Method for Voice-Interactive Indoor Positioning

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