## 3 Construction Methodology of the Traffic Accident Knowledge Graph
### Construction of the Traffic Accident Knowledge Graph  
A knowledge graph fundamentally adopts a graph structure and typically represents real-world entities and their interrelations in the form of triples (head entity, relation, tail entity). In this context, a traffic accident can be intuitively modeled as a set of such triples--for example, \"drunk driving causes vehicle collision\" is represented as (Drunk Driving, causes, Vehicle Collision), where \"Drunk Driving\" and \"Vehicle Collision\" serve as the head and tail entities, respectively, and \"causes\" denotes the relation. This formalization transforms voluminous and redundant accident information into a standardized, structured knowledge network, enabling a comprehensive depiction of road traffic accident data within a single graph and laying the foundation for subsequent knowledge retrieval and deep semantic mining. 1. Data Sources  
The dataset used in this study is an open-source collection from Kaggle that compiles approximately 120 accident-related factors leading to traffic collisions in California--encompassing accident time, location, cause, severity, weather and lighting conditions, road characteristics, site attributes, regional demographics, victim profiles, and participant details. This rich, multidimensional, and fine-grained description framework enables in-depth analysis of interdependencies among diverse data elements. Specifically, the California SWITRS (Statewide Integrated Traffic Records System) data--provided by the California Highway Patrol (CHP)--covers every reported collision from 1 January 2001 to mid-December 2020. The dataset contains roughly 9,460,000 records, totaling 5.78 GB. [PERSON] transformed the original SWITRS CSV files into a consolidated SQLite database by merging four snapshots (2016, 2017, 2018, and 2020) and retaining, via the process_date field in the collisions table, only the most recent entry for each collision, thereby preserving update continuity and accuracy. The database comprises four primary tables:  
Caseids: Stores the unique case_id and reporting year for referential integrity and traceability. Collisions: Contains 74 fields detailing collision time, geographic coordinates, environmental conditions, vehicle attributes, and severity ratings. Parties: Includes 31 fields covering involved parties' demographic characteristics, vehicle information, and fault assignment. Victims: Consists of 11 fields describing injury severity, seating position, and safety equipment usage for each casualty. To facilitate downstream analysis, the SWITRS-to-SQLite conversion script normalized missing values and converted all date/time fields into SQLite-compatible formats. Hosted under an open license on Kaggle, this relational dataset can be imported directly into analysis platforms such as Pandas or GIS for further processing and visualization. Its comprehensive schema and extensive field set provide a robust foundation for constructing our California Traffic Accident Knowledge Graph (TAKG) and performing embedding-based representation learning. By integrating and mining these data within a knowledge graph framework, our study can deeply explore complex interactions among factors, thereby delivering actionable insights for traffic safety management and policy planning. Prior to analysis, we ensured data integrity and reliability by manually cleaning the dataset--removing duplicate records, standardizing field formats, and verifying consistency across all entries. 2. Knowledge Graph Construction  
As illustrated in Figure 2, this study employs a top-down approach [28] to construct the Traffic Accident Knowledge Graph. This methodology first defines a domain ontology and data schema--establishing class hierarchies and constraint rules--and then populatesthe graph by extracting and instantiating entities, attributes, and relationships from the identified data sources.