A new approach in developing an urban rail transit emergency knowledge graph based on operation fault logs

Fan, Bosong; Shao, Chunfu; Liu, Yutong; Li, Juan

doi:10.1080/19439962.2022.2147613

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…For instance, literature [ 12 ] presents a city fire knowledge extraction method based on semantic rules. In literature [ 13 ], a knowledge extraction method based on semantic information and syntactic features is proposed for the field of urban traffic emergency response. In the literature [ 14 ], a network security named entity recognition model has been developed based on regular expressions and dictionary matching.…”

Section: Introductionmentioning

confidence: 99%

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

Wang,

Liu,

Liu

et al. 2023

PLoS ONE

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Using information technology to extract emergency decision-making knowledge from emergency plan documents is an essential means to enhance the efficiency and capacity of emergency management. To address the problems of numerous terminologies and complex relationships faced by emergency knowledge extraction of water diversion project, a multi-feature graph convolutional network (PTM-MFGCN) based on pre-trained model is proposed. Initially, through the utilization of random masking of domain-specific terminologies during pre-training, the model’s comprehension of the meaning and application of such terminologies within specific fields is enhanced, thereby augmenting the network’s proficiency in extracting professional terminologies. Furthermore, by introducing a multi-feature adjacency matrix to capture a broader range of neighboring node information, thereby enhancing the network’s ability to handle complex relationships. Lastly, we utilize the PTM-MFGCN to achieve the extraction of emergency entity relationships in water diversion project, thus constructing a knowledge graph for water diversion emergency management. The experimental results demonstrate that PTM-MFGCN exhibits improvements of 2.84% in accuracy, 4.87% in recall, and 5.18% in F1 score, compared to the baseline model. Relevant studies can effectively enhance the efficiency and capability of emergency management, mitigating the impact of unforeseen events on engineering safety.

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Section: Introductionmentioning

confidence: 99%

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

Wang,

Liu,

Liu

et al. 2023

PLoS ONE

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Improving search and rescue planning and resource allocation through case-based and concept-based retrieval

Nasar,

Torres,

Gundersen

et al. 2024

J Intell Inf Syst

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The need for effective and efficient search and rescue operations is more important than ever as the frequency and severity of disasters increase due to the escalating effects of climate change. Recognizing the value of personal knowledge and past experiences of experts, in this paper, we present findings of an investigation of how past knowledge and experts’ experiences can be effectively integrated with current search and rescue practices to improve rescue planning and resource allocation. A special focus is on investigating and demonstrating the potential associated with integrating knowledge graphs and case-based reasoning as a viable approach for search and rescue decision support. As part of our investigation, we have implemented a demonstrator system using a Norwegian search and rescue dataset and case-based and concept-based similarity retrieval. The main contribution of the paper is insight into how case-based and concept-based retrieval services can be designed to improve the effectiveness of search and rescue planning. To evaluate the validity of ranked cases in terms of how they align with the existing knowledge and insights of search and rescue experts, we use evaluation measures such as precision and recall. In our evaluation, we observed that attributes, such as the rescue operation type, have high precision, while the precision associated with the objects involved is relatively low. Central findings from our evaluation process are that knowledge-based creation, as well as case- and concept-based similarity retrieval services, can be beneficial in optimizing search and rescue planning time and allocating appropriate resources according to search and rescue incident descriptions.

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A novel hybrid framework for prioritization of failure modes during forging die-design

Sharma,

Raj,

Hussain

2024

J Braz. Soc. Mech. Sci. Eng.

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A new approach in developing an urban rail transit emergency knowledge graph based on operation fault logs

Cited by 4 publications

References 23 publications

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network

Improving search and rescue planning and resource allocation through case-based and concept-based retrieval

A novel hybrid framework for prioritization of failure modes during forging die-design

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