Integrating knowledge graph, complex network and Bayesian network for data-driven risk assessment Article Swipe

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Yiping Bai , Yuxuan Xing , Jiansong Wu ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.3303/cet2290006

Bayesian network is an effective method for quantitative risk assessment, but most existing studies are either heavily data-dependent or excessively expert-dependent. In this paper, knowledge graph, complex network theory and Bayesian network are integrated into a KCB model for data-driven risk assessment, especially small data situations. By applying knowledge graph with natural language processing, a causation graph could be extracted and illustrated from accident reports. Some indexes from complex network theory are introduced to identify critical nodes to simplify the huge graph. Based on the simplified network, a Bayesian network is established to quantitatively demonstrate accidents from causes to consequences. Moreover, sensitivity analysis and scenario analysis are conducted to support the decision-making of safety management. In all, the expert involvement of Bayesian network can be reduced by applying the KCB model. Besides, the KCB model can be further applied to many other areas to reach uncertainty modelling.

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Bayesian network Computer science Graph Data science Artificial intelligence Data mining Machine learning Theoretical computer science

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Type: article
Language: en
Landing Page: https://doaj.org/article/20bd9adb80354fe69f01b9297757e4c1
OA Status: green
Cited By: 4
Related Works: 10
OpenAlex ID: https://openalex.org/W4389288745

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OpenAlex ID: https://openalex.org/W4389288745

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DOI: https://doi.org/10.3303/cet2290006

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Title: Integrating knowledge graph, complex network and Bayesian network for data-driven risk assessment

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Type: article

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Language: en

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Publication year: 2022

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Publication date: 2022-05-01

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Authors: Yiping Bai, Yuxuan Xing, Jiansong Wu

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Landing page: https://doaj.org/article/20bd9adb80354fe69f01b9297757e4c1

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Open access: Yes

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OA status: green

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OA URL: https://doaj.org/article/20bd9adb80354fe69f01b9297757e4c1

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Concepts: Bayesian network, Computer science, Graph, Data science, Artificial intelligence, Data mining, Machine learning, Theoretical computer science

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Cited by: 4

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Citations by year (recent): 2024: 2, 2023: 2

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Related works (count): 10

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