Grid-Based Projection of Spatial Data into Knowledge Graphs

TL;DR

This paper presents a grid-based method for encoding spatial data and street networks into knowledge graphs, improving efficiency and supporting routing tasks within RDF frameworks.

Contribution

It introduces a novel grid cell approach for representing spatial features and street networks in knowledge graphs, diverging from traditional segment-based methods.

Findings

Efficient encoding of spatial features using grid cells.

A new tessellation-based street network representation.

Supports routing and navigation tasks in RDF-based knowledge graphs.

Abstract

The Spatial Knowledge Graphs (SKG) are experiencing growing adoption as a means to model real-world entities, proving especially invaluable in domains like crisis management and urban planning. Considering that RDF specifications offer limited support for effectively managing spatial information, it's common practice to include text-based serializations of geometrical features, such as polygons and lines, as string literals in knowledge graphs. Consequently, Spatial Knowledge Graphs (SKGs) often rely on geo-enabled RDF Stores capable of parsing, interpreting, and indexing such serializations. In this paper, we leverage grid cells as the foundational element of SKGs and demonstrate how efficiently the spatial characteristics of real-world entities and their attributes can be encoded within knowledge graphs. Furthermore, we introduce a novel methodology for representing street networks in knowledge graphs, diverging from the conventional practice of individually capturing each street segment. Instead, our approach is based on tessellating the street network using grid cells and creating a simplified representation that could be utilized for various routing and navigation tasks, solely relying on RDF specifications.