## 1 Introduction
Essentially, a knowledge graph is a semantic network primarily used to describe objects in the objective world and their relationships, possessing the capability for formalized representation of entities and their interrelations. Characterized by large scale, multi-semantics, and high quality, knowledge graphs have been extensively applied in fields such as semantic search and recommendation systems. Compared to traditional information service knowledge bases with deficiencies in semantic association, knowledge graphs demonstrate significant advantages in processing and presenting multi-source heterogeneous data. This feature provides a novel solution for the acquisition, utilization, and visualization of geological hazard information. The introduction of knowledge graphs facilitates the rapid collection, integration, and correlation of geological disaster-related data [9], making it an important tool for revealing the factors influencing loess slope stability and their interrelationships. In recent years, numerous knowledge graphs, such as Freebase [10], Dbpedia [11], Wikidata [12], and YAGO2 [13], have been developed and successfully applied across various domains, including finance, healthcare, and e-commerce [14]. However, to date, no studies have been identified regarding the construction of knowledge graphs specifically for analyzing the slope stability related to geological hazards in loess region environments. Slope stability evaluation methodologies encompass qualitative and quantitative approaches, each with distinct advantages and limitations. Qualitative methods, including engineering geological analogy and graphical analysis, enable rapid preliminary assessments by examining influencing factors, yet their reliance on expert judgment introduces subjectivity and restricts reliability. Traditional quantitative techniques, such as the Swedish arc method and [PERSON]'s simplified approach, apply geotechnical mechanics principles through limit equilibrium analysis but often oversimplify complex real-world conditions [15]. Numerical simulations like finite element analysis and discrete element modeling transform stability problems into computational frameworks, though their accuracy remains heavily dependent on model selection and parameter calibration. Given the nonlinear, multifactorial nature of slope systems, conventional linear evaluation models prove inadequate, necessitating advanced nonlinear assessment systems [16]. The integration of artificial intelligence and machine learning addresses these challenges through neural networks that establish nonlinear relationships between stability coefficients and influencing factors [17; 18; 19; 20; 21]. [PERSON] [22] developed a PLSR-BP model for evaluating loess slope stability by integrating partial least squares regression (PLSR) with a backpropagation (BP) neural network optimized by particle swarm optimization (PSO). PLSR was used to extract key influencing factors and eliminate multicollinearity, while the optimized BP network captured the nonlinear relationship with improved convergence and efficiency. Experimental results demonstrate that the model offers high prediction accuracy and is effective for the stability assessment of loess slopes. [PERSON] [23] et al. reviewed the application of five machine learning models (MLP, SVM, k-NN, DT, and RF) in predicting slope safety factors. Using geotechnical parameters and data from 70 slopes in the South Pars region, the performance of each model was evaluated. The results showed that the MLP model performed the best, with a precision of 0.938, an accuracy of 0.90, and low error, indicating that machine learning methods have significant potential for application in slope stability assessment. [PERSON] et al. [24] proposed an ensemble machine learning approach combining bagging and boosting techniques with decision tree and random forest base classifiers for efficient slope stability prediction.