Many complex real-world systems exhibit inherently intertwined temporal and spatial characteristics. Spatio-temporal knowledge graphs (STKGs) have therefore emerged as a powerful representation paradigm, as they integrate entities, relationships, time and space within a unified graph structure. They are increasingly applied across diverse domains, including environmental systems and urban, transportation, social and human mobility networks. However, modeling STKGs remains challenging: their foundations span classical graph theory as well as temporal and spatial graph models, which have evolved independently across different research communities and follow heterogeneous modeling assumptions and terminologies. As a result, existing approaches often lack conceptual alignment, generalizability and reusability. This survey provides a systematic review of spatio-temporal knowledge graph models, tracing their origins in static, temporal and spatial graph modeling. We analyze existing approaches along key modeling dimensions, including edge semantics, temporal and spatial annotation strategies, temporal and spatial semantics and relate these choices to their respective application domains. Our analysis reveals that unified modeling frameworks are largely absent and that most current models are tailored to specific use cases rather than designed for reuse or long-term knowledge preservation. Based on these findings, we derive modeling guidelines and identify open challenges to guide future research.

翻译：许多复杂的现实世界系统展现出内在交织的时间与空间特性。时空知识图谱（STKGs）因此成为一种强大的表示范式，它将实体、关系、时间与空间整合于统一的图结构中，并日益广泛应用于环境系统、城市、交通、社交及人类移动网络等多个领域。然而，时空知识图谱的建模仍面临挑战：其理论基础涵盖经典图论以及时空图模型，这些模型在不同研究社区中独立发展，遵循异构的建模假设与术语体系，导致现有方法常缺乏概念对齐性、泛化性与可复用性。本综述系统梳理了时空知识图谱模型，追溯其在静态、时间与空间图建模中的起源，并沿关键建模维度（包括边语义、时空标注策略、时空语义）分析现有方法，同时将这些设计选择与各自应用领域相关联。分析表明，当前普遍缺乏统一的建模框架，多数模型针对特定用例定制，而非为复用或长期知识保存而设计。基于这些发现，我们提出建模指导原则并指明开放挑战，以引导未来研究方向。