# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
## 1 Introduction
Consequently, mining the correlation among the internal factors of accident causation and between accident causation and other accident factors from accident data facilitates a deeper understanding of the mechanisms of accidents, providing informational support for aviation safety analysis and reducing the probability of accidents. The multi-dimensional network model is a tool for multi-dimensional information representation and analysis that is capable of quantitatively analyzing the inherent patterns in network topology structures. It provides a novel perspective for analyzing complex relationships in accident causation. [PERSON] et al. proposed multi-dimensional topological indices and constructed a knowledge graph by analyzing railway operational accidents in 2021 [16]. Subsequently, [PERSON] et al., further improved the multi-dimensional network topology analysis method for railway accidents by adding the time dimension, conducting a more comprehensive study on the knowledge graph of hazard correlation analysis [17]. As for aviation accidents, there are numerous causal factors that contribute to their occurrence, and the inherent patterns within accident causal factors and other accident factors need to be obtained through the analysis of a large number of accident samples. Therefore, this article employs the multi-dimensional network model to quantitatively analyze the correlations among accident causal factors and between accident causal factors and other accident factors. Additionally, current research on aviation accidents primarily relies on aviation accident reports, which comprehensively reflect information on and the entire process of the accident. Accident reports serve as a direct source of information for a comprehensive and in-depth analysis, providing insights into the whole picture and causes of accidentsand playing a crucial role in enhancing aviation safety management capabilities. However, the traditional approach to mining aviation accident reports still relies heavily on manual methods. When dealing with unstructured textual information, manual analysis is inefficient, is prone to missing information, is difficult to conduct on a large number of samples, and has trouble directly retrieving useful information [18]. Therefore, faced with vast and complex aviation accident reports, determining how to efficiently and effectively mine the information within them has become an urgent need for the intelligent development of aviation safety management processes [19]. With the increasing maturity of artificial intelligence methods, technologies represented by knowledge graphs have brought new opportunities to aviation safety research [20]. Unlike the qualitative analysis method used by traditional accident causation analysis models, knowledge graphs describe various relationships between knowledge entities, expressing various types of nodes and their relationships [21]. This approach enables more efficient and comprehensive mining of their associated relationships [22], expresses complex accident causation information [23], and enhances the relationships among accident data [24]. In the field of aviation safety, the application of knowledge graphs in related research is still in its infancy. For instance, [PERSON] [25] and [PERSON] [26] both proposed different entity extraction methods based on the construction of knowledge graphs. The sections of this study are structured as follows: In Section 2, we introduce the model of the Aviation Accident Causation Correlation Knowledge Graph (ACAKG). In Section 3, by collecting 437 aviation accident investigation reports globally from 2018 to 2022, we construct the ACAKG using these data as a foundation. We analyze and derive trend patterns, verifying the effectiveness of this method and laying a foundation for further research on accident causal factors. Section 4 discusses the findings, and Section 5 summarizes the content of this study.