# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
## 1 Introduction  
As a high-speed, high-frequency, and efficient mode of transportation, aviation flight plays an irreplaceable role in human progress and social life. However, the increase in aviation activities has led to more accidents. According to the ICAO's yearly accident statistics of global scheduled commercial operations (with a certified maximum take-off weight of over 5700 kg), between 2013 and 2022, the number of global aviation accidents increased from 90 in 2013 to 114 in 2019, though accidents declined after 2020 as a result of the great pandemic, which reduced aviation activity [1; 2]. In addition, despite the infrequency of aviation accidents, their occurrence can inflict substantial damage on fatalities and property, thereby having a profound negative societal impact and posing a considerable impediment to the continued progression of the aviation industry. In view of the number of aviation accidents and the harmful consequences of these accidents, it is necessary to ensure the safety of aviation flights, and improving aviation safety has been increasingly emphasized [3]. Therefore, scholars have conducted extensive research on aviation accidents. Accident causation analysis is a traditional research hotspot, and a large number of scholars have constructed index systems, applied different causation models, and improved and combined the models to analyze, evaluate, and prevent accidents. Based on three causation models, namely, HFACS, ATSB, and IATA, [PERSON] et al. mined the root causes of accidents and proposed a human-machine-environment-procedure classification scheme [4]. [PERSON] et al. integrated the HFACS framework and Fuzzy Cognitive Mapping for in-flight startle causality analysis [5]. [PERSON] et al. constructed an aircraft safety assessment index under uncertain conditions by analyzing the uncertainties of aviation maneuvers [6]. Human causes are among the main causes of accidents and the focus of analysis [7; 8; 9; 10]. However, traditional methods usually conduct a safety analysis in a qualitative way through causal models and indicator systems in which it is difficult to quantitatively analyze the intrinsic connections among accident causal factors. Other common dimensions of accident research are analyzing the historical data of accidents and accident signs quantitatively; obtaining the inherent patterns of accident occurrence from the dimensions of data statistics and mining, causality analysis, etc. ; and analyzing the trends in accident occurrence and prediction. [PERSON] et al. investigated a random sample of more than 200 commercial air transportation accidents and incidents in total over 16 years and found that the most significant human factors contributing to aviation accidents and incidents are situational awareness and non-adherence to procedures [11]. [PERSON] et al. fitted the civil aviation accident trends from 1994 to 2020 using the Causal-ARIMA model to improve the accuracy and interpretability of aviation accident trend analysis [12]. [PERSON] assessed the quantitative relationship between symptoms and latent causal factors by using multivariate logistic regression to model the relationship among latent causal factors, symptom causal factors, and accident severity [13]. [PERSON] et al. developed a hybrid model to quantify the risk associated with the consequences of each hazardous cause, thus predicting the risk level for the severity of an unusual aviation event [14]. In summary, aviation accident research focuses on accident causation analysis and the data-driven mining of accident patterns and trends. However, as a systemic issue, aviation accident causation factors are mutually induced and coupled with spatio-temporal randomness and diversity. Therefore, a holistic approach is required for comprehensive interpretation [15].