# Causation Correlation Analysis of Aviation Accidents: A Knowledge Graph-Based Approach
## 2 Methods  
### Modeling the ACAKG  
Establishing the ACAKG is the first step in conducting aviation accident causation correlation analysis. A knowledge graph is a structured semantic graph composed of vertices (or nodes) and edges, which is used to describe concepts and their relationships. Its basic structure consists of triplets (\"entity-relationship-entity\"), where vertices represent concepts or entities and edges represent semantic relationships between concepts or entities. Based on the type of knowledge, knowledge graphs are typically classified into general knowledge graphs and domain knowledge graphs. The ACAKG belongs to the latter category, characterized by its specialization, dynamism, and strong contextual correlation [27]. Through the construction of the ACAKG, scattered textual descriptions of aviation accident causes can be linked to entities and relationships, forming a complete, structured, and easily extractable knowledge base [23]. This facilitates the management, retrieval, utilization, understanding, and analysis of aviation accident causal information contained within [22], revealing the potential relationships among the internal causes of accidents as well as those between the causal factors of accidents and other factors of aviation accidents. Figure 1 demonstrates the basic process of domain knowledge graph construction and application, which is mainly divided into five parts: data acquisition, ontology construction, information extraction, knowledge graph construction, and application. Among them, the ontology module constructs the main concepts in the field of aviation accident causation, and the information extraction module performs preprocessing and extraction on the text. Knowledge storage involves storing the acquired knowledge in a specific structure and visualizing it. In addition, the knowledge graph can be divided into a schema layer and a data layer. The schema layer, with the ontology as its core, can be expressed as classes and attributes. The data layer refers to the entity relationship network composed of all instance data obtained through information extraction [22]. In the knowledge graph, the ontology module can be typically divided into four types of concepts, namely, entities, categories, relationships, and attributes, which are characterized by conceptualization, formalization, domain specificity, and clarity [28]. Entities are the basic unit of the ontology, categories are collections of entities with similar attributes, relationships refer to the connections between categories or entities, and attributes define categories or entities. Ontology construction methods mainly include manual, automatic, and semi-automatic approaches. In the construction of the ontology for aviation accident causation presented in this paper, a semi-automatic approach is adopted [27]. Step 1: An ontology framework encompassing accident causation, flight phases, accident types, and accident consequences is established based on existing research on aviation accident causation and relevant ICAO standards [29; 30; 31]. Step 2: This framework is refined in response to feedback from experts in aviation safety. Step 3: In the process of semi-automatic triple annotation, when the annotated instances found do not belong to the existing ontology, further improvement and supplementation should be made to the ontology. Figure 2 illustrates the core concepts of the ACAKG ontology. Figure 1: Process of knowledge graph building and application. Figure 2: Concepts in ACAKG ontology. Information extraction mainly involves three steps. Firstly, manual calibration and refinement are conducted to address any data deficiencies, duplications, or inaccuracies in the report data. Secondly, entity extraction is performed. Thirdly, the relationships between entities are determined and extracted. Entity extraction is the process of extracting information from aviation accident texts. Relationship extraction involves organizing and extracting the relationships between entities.