A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process

Wang, Zhenhua; Zhang, Beike; Gao, Dong

doi:10.1016/j.compind.2022.103647

Cited by 27 publications

(5 citation statements)

References 63 publications

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“…It contains more internal factors including culture and brand, which not only maintains the continuity of product design style but also has innovation. In product innovation design, an important means is to adopt the gene design method for user experience to realize the serialization of products facing different needs or products with greater deformation ability [2].…”

Section: Introductionmentioning

confidence: 99%

Application of User Experience Gene Extraction Model Based on Industrial Design

2022

Security and Communication Networks

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Product design DNA is a new concept produced by applying the idea of genetic engineering to the field of industrial design, involving multiple knowledge fields, aiming to give products a unique shape and style image to build a brand. This paper systematically summarizes the current situation and progress of product design DNA research at home and abroad, focusing on the expression structure, application research progress, and research on key technologies. The law of DNA generation and derivation; explore the user’s cognitive mechanism for product design DNA; realize the connection between product design DNA reasoning and production. The designed user experience gene extraction based on industrial design provides new ideas for product design and has strong guiding significance.

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Section: Introductionmentioning

confidence: 99%

Application of User Experience Gene Extraction Model Based on Industrial Design

2022

Security and Communication Networks

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“…Four representative models are selected for evaluation: TextRNN, Transformer (Cunha et al, 2023), Bert with size of base (Pérez Pozo et al, 2022;Wang et al, 2022;Wang et al, 2024) and LLAMA2 with size of 7B (Touvron et al, 2023). These models, widely acclaimed and adopted, collectively embody distinct stages in the progression of deep learning, presenting a rich diversity.…”

Section: Experiments Settingmentioning

confidence: 99%

Will sentiment analysis need subculture? A new data augmentation approach

Wang,

He,

et al. 2024

Asso for Info Science & Tech

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Nowadays, the omnipresence of the Internet has fostered a subculture that congregates around the contemporary milieu. The subculture artfully articulates the intricacies of human feelings by ardently pursuing the allure of novelty, a fact that cannot be disregarded in the sentiment analysis. This paper aims to enrich data through the lens of subculture, to address the insufficient training data faced by sentiment analysis. To this end, a new approach of subculture‐based data augmentation (SCDA) is proposed, which engenders enhanced texts for each training text by leveraging the creation of specific subcultural expression generators. The extensive experiments attest to the effectiveness and potential of SCDA. The results also shed light on the phenomenon that disparate subcultural expressions elicit varying degrees of sentiment stimulation. Moreover, an intriguing conjecture arises, suggesting the linear reversibility of certain subcultural expressions.

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“…Consequently, the issue of knowledge graph completion, which involves determining the validity of triples within the knowledge graph, has garnered significant attention. Various studies have been undertaken to explore knowledge graph completion, focusing on methods for modeling the connectivity patterns between entities in the IKG and developing scoring functions to assess the validity of triples [9][10][11]. However, these approaches primarily rely on the graph structure and relational information within the existing knowledge graph, limiting their predictive capabilities for triples containing less common entities.…”

Section: Knowledge Graph Completionmentioning

confidence: 99%

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

Huang,

Yang

2024

Electronics

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Industrial knowledge graphs (IKGs) have received widespread attention from researchers in recent years; they are intuitive to humans and can be understood and processed by machines. However, how to update the entity triples in the graph based on the continuous production data to cover as much knowledge as possible, while applying a KG to meet the needs of different industrial tasks, are two difficulties. This paper proposes a two-stage model construction strategy to benefit both knowledge graph completion and industrial tasks. Firstly, this paper summarizes the specific forms of multi-source data in industry and provides processing methods for each type of data. The core is to vectorize the data and align it conceptually, thereby achieving the fusion modeling of multi-source data. Secondly, this paper defines two interrelated subtasks to construct a pretrained language–knowledge graph model based on multi-task learning. At the same time, considering the dynamic characteristics of the production process, a dynamic expert network structure is adopted for different tasks combined with the pretrained model. In the knowledge completion task, the proposed model achieved an accuracy of 91.25%, while in the self-healing control task of a blast furnace, the proposed model reduced the incorrect actions rate to 0 and completed self-healing control for low stockline fault in 278 min. The proposed framework has achieved satisfactory results in experiments, which verifies the effectiveness of introducing knowledge into industry.

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A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process

Cited by 27 publications

References 63 publications

Application of User Experience Gene Extraction Model Based on Industrial Design

Application of User Experience Gene Extraction Model Based on Industrial Design

Will sentiment analysis need subculture? A new data augmentation approach

Pretrained Language–Knowledge Graph Model Benefits Both Knowledge Graph Completion and Industrial Tasks: Taking the Blast Furnace Ironmaking Process as an Example

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