A Generative AI-Enhanced Case-Based Reasoning Method for Risk Assessment: Ontology Modeling and Similarity Calculation Framework

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptim...

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Veröffentlicht in:Mathematics (Basel) Jg. 13; H. 17; S. 2735
Hauptverfasser: Sun, Jiayi, Fei, Liguo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.09.2025
Schlagworte:
Accuracy
Adaptability
Adaptation
Case studies
case-based reasoning
Cognition & reasoning
Content analysis
Decision making
Electric power
Emergency response
Generative artificial intelligence
Genetic algorithms
Hydroelectric power
Information resources
Knowledge
Knowledge representation
Literature reviews
Matching
Mathematical analysis
Ontology
Optimization
Problem solving
Reasoning
Research methodology
Revisions
Risk assessment
Risk management
Robustness (mathematics)
Scientific validity
Similarity
similarity calculation framework
Typhoons
China
ISSN:2227-7390, 2227-7390
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Zusammenfassung:Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math13172735