Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Wang, Zhuohao; Wang, Dong; Li, Qing

doi:10.1049/cje.2021.05.007

Cited by 23 publications

(8 citation statements)

References 17 publications

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“…TF-IDF algorithm is a classic text feature weighting method, which measures the importance of a word in a document [23,24]. Equation ( 3) is the formula of TF-IDF algorithm, where TFðw, dÞ is the word frequency of word w in text d, N is the total number of documents in corpus, and DFðwÞ is the number of documents containing word w in the training corpus.…”

Section: Methodsmentioning

confidence: 99%

Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

Wang

Tang

2022

Advances in Multimedia

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During the Ming Dynasty, China and Korea exchanged frequently and recorded a large amount of written information, which is of great value for understanding the culture of that time. The large amount of data makes it difficult to conduct quantitative analysis by researchers, which makes the analysis limited. This paper carries out a research on the cognition and analysis method of Chinese and Korean envoys to foreign cultures in Ming Dynasty based on big data analysis technology. Based on the literature research, this paper determines the ontology model establishment method to efficiently detect the written records of Chinese and Korean envoys in Ming Dynasty. The established ontology model and the improved clustering analysis method can improve the efficiency of data detection, reduce the error of data detection, and provide data basis for the research of this paper. According to the technology of big data analysis, this paper analyzes the focus and status class of Chinese and Korean envoys in Ming Dynasty and analyzes cognition of the Chinese and Korean envoys for the foreign culture. The results show that the envoys of the Chinese and Korean pay different attention to the foreign culture due to their different cognition of the foreign culture, compared with Ming Dynasty envoys, Korean envoys paid 15.3 percent less attention to geography, 19.7 percent more to history, 11.7 percent more to people, and 16 percent less to customs. This reflects the two envoys’ different perceptions of the foreign culture. And the status class of the envoys exacerbates this difference. In the early Ming Dynasty, the creative diversity of Ming envoys was far lower than that of Korean envoys. As time went by, the creative diversity of Ming envoys increased. The results provide support for further understanding of Chinese and Korean culture and their relationship in Ming Dynasty.

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Section: Methodsmentioning

confidence: 99%

Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

Wang

Tang

2022

Advances in Multimedia

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“…The fundamental idea behind TF-IDF is that if a word appears frequently in a document but less frequently in other documents, it carries more significance in distinguishing the document and expressing its core content. Hen2vecce, such words are assigned higher weights [ 34 ]. The TF-IDF algorithm calculates a weight for each word in a document based on the term frequency (TF) and inverse document frequency (IDF).…”

Section: Methodsmentioning

confidence: 99%

Knowledge graph and CBR-based approach for automated analysis of bridge operational accidents: Case representation and retrieval

Xu,

Wei,

Cai

et al. 2023

PLoS ONE

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Bridge operational accident analysis is a critical process in bridge operational risk management. It provides valuable knowledge support for responding to newly occurring accidents. However, there are three issues: (1) research specifically focused on the past bridge operational accidents is relatively scarce; (2) there is a lack of mature research findings regarding the bridge operational accidents knowledge representation; and (3) in similar case retrieval, while case-based reasoning (CBR) is a valuable approach, there are still some challenges and limitations associated with its usage. To tackle these problems, this research proposed an automated analysis approach for bridge operational accidents based on a knowledge graph and CBR. The approach includes case representation and case retrieval, leveraging advancements in computer science and artificial intelligence. In the proposed approach, the case representation involves the adoption of a knowledge graph to construct multi-dimensional networks. The knowledge graph captures the relationships between various factors and entities, allowing for a comprehensive representation of accidents domain knowledge. In the case retrieval, a multi-circle layer retrieval strategy was innovatively proposed to enhance retrieval efficiency. Three target cases were randomly selected to verify the validity of the proposed methodology. The combination of a knowledge graph and CBR can indeed provide useful tools for the automated analysis of bridge operational accidents. Additionally, the proposed methodology can serve as a reference for intelligent risk management in other types of infrastructures.

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“…LSTM recurrent calculations are then performed, and the basic LSTM function is shown in Equation 4. Next, softmax calculations are performed based on the input gates of characters and words to obtain weighted coefficients, followed by weighted summation, laying the foundation for subsequent training [24].…”

Section: ) Entity Extractionmentioning

confidence: 99%

Construction Method and Practical Application of Oil and Gas Field Surface Engineering Case Database Based on Knowledge Graph

Xia,

Dai,

Zhang

et al. 2024

Processes

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To address the challenge of quickly and efficiently accessing relevant management experience for a wide range of ground engineering construction projects, supporting project management with information technology is crucial. This includes the establishment of a case database and an application platform for intelligent search and recommendations. The article leverages Optical Character Recognition (OCR) technology, knowledge graph technology, and Natural Language Processing (NLP) technology. It explores the mechanisms for classifying construction cases, methods for constructing a case database, structuring case data, intelligently retrieving and matching cases, and intelligent recommendation methods. This research forms a complete, feasible, and scalable method for deconstructing, storing, intelligently retrieving, and recommending construction cases, providing a theoretical basis for the establishment of a construction case database. It aims to meet the needs of digital project management and intelligent decision-making support in the oil and gas sector, thereby enhancing the efficiency and accuracy of project construction. This work offers a theoretical foundation for the development of an intelligent management platform for ground engineering projects in the oil and gas industry, supporting the sector’s digital transformation and intelligent development.

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Keyword Extraction from Scientific Research Projects Based on SRP‐TF‐IDF

Cited by 23 publications

References 17 publications

Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

Cultural Cognition and Analytical Methods of Chinese and Korean Envoys in Ming Dynasty Based on Big Data Analysis Technology

Knowledge graph and CBR-based approach for automated analysis of bridge operational accidents: Case representation and retrieval

Construction Method and Practical Application of Oil and Gas Field Surface Engineering Case Database Based on Knowledge Graph

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