Intelligent generation method of emergency plan for hydraulic engineering based on knowledge graph – take the South-to-North Water Diversion Project as an example

Liu, Xuemei; Lu, Hankang; Li, Hairui

doi:10.1080/27678490.2022.2153629

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The research of these applications aims to make full use of the rich information of the hydraulic knowledge graph to improve the efficiency of management and the accuracy of decision making in the field of hydraulics. Liu X et al [5] used the text data of a water conservancy project inspection to construct the knowledge graph of the water conservancy project emergency plan, and generated the emergency plan intelligently through knowledge retrieval and reasoning, which broke the problems of low digitization, poor knowledge relevance, and insufficient intelligent decision making in the traditional water conservancy project emergency plan. Ye et al [6] proposed a knowledge-graph-based urban flood resilience service framework to provide accurate and timely flood resilience information as well as efficient flood management solutions by enabling the sharing and intelligent application of knowledge in the flood resilience domain.…”

Section: Introductionmentioning

confidence: 99%

Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering

et al. 2023

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In response to the shortcomings of existing knowledge graph embedding strategies, such as weak feature interaction and latent knowledge representation, a unique hydraulic knowledge graph embedding method is suggested. The proposed method incorporates spatial position features into the entity-relation embedding process, thereby enhancing the representation capability of latent knowledge. Furthermore, it utilizes a multi-layer convolutional neural network to fuse features at different levels, effectively capturing more abundant semantic information. Additionally, the method employs multi-scale dilated convolution kernels to capture rich explicit interaction features across different scales of space. In this study, the effectiveness of the proposed model was validated on the link prediction task. Experimental results demonstrated that, compared to the ConvE model, the proposed model achieved a significant improvement of 14.8% in terms of mean reciprocal rank (MRR) on public datasets. Additionally, the suggested model outperformed the ConvR model on the hydraulic dataset, leading to a 10.1% increase in MRR. The results indicate that the proposed approach exhibits good applicability and performance in the task of hydraulic knowledge graph complementation. This suggests that the method has the potential to offer significant assistance for knowledge discovery and application research in the field of hydraulics.

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Section: Introductionmentioning

confidence: 99%

Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering

et al. 2023

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Knowledge Graph-Based Method for Intelligent Generation of Emergency Plans for Water Conservancy Projects

Wang,

Liu,

Liu

et al. 2023

IEEE Access

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A Bibliometric Review of Large Language Models Research from 2017 to 2023

Fan,

Li,

et al. 2024

ACM Trans. Intell. Syst. Technol.

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Large language models (LLMs), such as OpenAI’s Generative Pre-trained Transformer (GPT), are a class of language models that have demonstrated outstanding performance across a range of natural language processing (NLP) tasks. LLMs have become a highly sought-after research area because of their ability to generate human-like language and their potential to revolutionize science and technology. In this study, we conduct bibliometric and discourse analyses of scholarly literature on LLMs. Synthesizing over 5,000 publications, this paper serves as a roadmap for researchers, practitioners, and policymakers to navigate the current landscape of LLMs research. We present the research trends from 2017 to early 2023, identifying patterns in research paradigms and collaborations. We start with analyzing the core algorithm developments and NLP tasks that are fundamental in LLMs research. We then investigate the applications of LLMs in various fields and domains, including medicine, engineering, social science, and humanities. Our review also reveals the dynamic, fast-paced evolution of LLMs research. Overall, this paper offers valuable insights into the current state, impact, and potential of LLMs research and its applications.

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Intelligent generation method of emergency plan for hydraulic engineering based on knowledge graph – take the South-to-North Water Diversion Project as an example

Cited by 5 publications

References 26 publications

Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering

Research on a Knowledge Graph Embedding Method Based on Improved Convolutional Neural Networks for Hydraulic Engineering

Knowledge Graph-Based Method for Intelligent Generation of Emergency Plans for Water Conservancy Projects

A Bibliometric Review of Large Language Models Research from 2017 to 2023

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