Connectivity of Cities

This study describes the development of a lightweight hybrid framework for context-aware platial information that integrates NLP techniques with Graph Neural Networks (GNNs) to extract and analyze geosemantic knowledge from textual and spatial data. The main purpose is to create a platial knowledge graph that represents cities not simply as locations on maps, but as multidimensional entities with spatial, cultural, historical, and social characteristics. More specifically, we construct a diverse platial knowledge graph where cities are represented as nodes connected to other cities based on semantic features derived from both linguistic content and spatial context.