🤖 AI Summary
Current spatiotemporal knowledge graph (STKG) models suffer from fundamental limitations—including conceptual fragmentation, terminological inconsistency, poor reusability, and inadequate support for long-term knowledge preservation—arising from their disparate foundations in static, temporal, and spatial graph paradigms. To address these issues, this work introduces the first multidimensional analytical framework encompassing edge semantics, spatiotemporal annotation, and semantic modeling. Through a systematic literature review and cross-dimensional comparative analysis, we clarify the theoretical evolution of STKGs. We further propose a general-purpose modeling guideline explicitly designed for long-term knowledge preservation, establish standardized design principles, and distill six key open challenges. Our contributions provide both theoretical foundations and practical pathways for transitioning STKGs from application-specific solutions toward universal, sustainable knowledge infrastructure.
📝 Abstract
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.
