A Survey on Spatio-Temporal Knowledge Graph Models

TL;DR

This survey reviews the development of spatio-temporal knowledge graph models, analyzing their foundations, modeling choices, and application domains, highlighting the lack of unified frameworks and proposing future research directions.

Contribution

It systematically analyzes existing STKG models, compares modeling strategies, and provides guidelines and open challenges for future research in the field.

Findings

Most models are tailored to specific use cases.

Unified modeling frameworks are largely absent.

Many approaches lack generalizability and reusability.

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.