Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Grid-Based Projection of Spatial Data into Knowledge Graphs

Amin Anjomshoaa, Hannah Schuster, Axel Polleres

TL;DR

This paper leverages grid cells as the foundational element of SKGs and demonstrates how efficiently the spatial characteristics of real-world entities and their attributes can be encoded within 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.

Grid-Based Projection of Spatial Data into Knowledge Graphs

TL;DR

This paper leverages grid cells as the foundational element of SKGs and demonstrates how efficiently the spatial characteristics of real-world entities and their attributes can be encoded within 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.

Paper Structure

This paper contains 8 sections, 3 equations, 5 figures.

Table of Contents

  1. Introduction
  2. Methodology
  3. Grid Cell Orientation
  4. Routing and Navigation
  5. Grid Knowledge Graph Applications
  6. Discussion
  7. Related Work
  8. Conclusion

Figures (5)

  • Figure 1: Definition of grid cell <id/cell/L1.62.466>.
  • Figure 3: Representing cells as puzzle pieces with links.
  • Figure 4: Sample links generated for a specific cell.
  • Figure 5: Mapping grid cells to simplified cell network for navigation use case.
  • Figure 6: The semantic model of Spatial Knowledge Graph for crisis management use case, highlighting the geo-related properties that facilitate spatial operations.