## 1 Introduction
[8] designed a machine learning system for classifying the severity of powered two-wheeler accidents, achieving approximately 90% accuracy and recall by leveraging features available at the moment of collision. Although these advanced methods can efficiently process traffic accident datasets, they still confront challenges such as limited data volume, imbalanced class distributions, and interference from noisy inputs, which impede the capture of complex spatiotemporal patterns and multifactor interactions. High-dimensional and heterogeneous data fusion often suffers from the \"curse of dimensionality\" and feature redundancy, leading to degraded predictive performance. Moreover, these approaches frequently lack transparency, hindering their practical deployment for causal analysis and countermeasure planning; their sensitivity to hyperparameters and substantial computational demands further complicate real-world implementation. Knowledge graphs are, at their core, semantic networks that interconnect real-world entities and the relationships among them, wherein nodes represent entities and edges denote various semantic relations [9]. In scholarly literature, numerous definitions of knowledge graphs have been proposed, tailored to specific application scenarios and technical contexts [10]. Broadly speaking, a knowledge graph is a large-scale factual database with a graph structure, comprising vast numbers of triples formed by entities (nodes) and their relations (edges), and semantically modeling real-world phenomena. Traditional statistical and machine learning approaches exhibit limitations in uncoveringthe multi-level causal relationships underlying traffic accidents, whereas knowledge graph technology, by integrating diverse information on accidents, environment, vehicles, and driver behavior, holds promise for mining deep semantic associations within the data. Indeed, traffic accident data not only encapsulate rich spatiotemporal information but also involve multiple influencing factors that typically interact in complex nonlinear ways. Moreover, the heterogeneity of data sources, along with inconsistencies in format and quality, further complicates data fusion and preprocessing. The temporal dynamics and uneven geographic distribution of accident data present severe challenges for pattern recognition and causal inference. Knowledge graph technologies have already proven indispensable in Internet-scale applications such as search engines and intelligent question answering, and they are beginning to see preliminary adoption in domains including finance [11], power systems [12], and healthcare [13]. Although conventional statistical analyses and machine learning techniques have achieved notable results in processing traffic accident data, their ability to capture the nonlinear interactions inherent in high-dimensional heterogeneous data and the spatiotemporal dynamics of accidents remains insufficient [14], making them ill-suited for fine-grained risk assessment and causal analysis. At the same time, existing knowledge graph embedding models often exhibit limited representational capacity or parameter redundancy when handling complex one-to-many, many-to-one, and many-to-many mapping relationships [15], which hampers their broader adoption in the traffic safety domain. On the basis of the foregoing theoretical gaps, this study addresses the following research questions: (1) How can one construct a traffic accident knowledge graph encompassing multi-level, multi-source heterogeneous elements--integrating accident events, environmental factors, participants, and victims--so as to fully represent the multidimensional nature of traffic incidents? (2) What limitations do prevailing translation-based embedding methods (TransE, TransH, TransR, and TransD) exhibit when modeling one-to-many, many-to-one, and many-to-many relations within a traffic accident knowledge graph?