TL;DR
This paper introduces geospatial knowledge graphs as a powerful framework for representing, managing, and reasoning over geographic information, enhancing interoperability and supporting cross-disciplinary GeoAI applications.
Contribution
It provides an overview of key concepts, standardization, tools, and applications of geospatial knowledge graphs, and discusses future research directions in this emerging field.
Findings
Facilitates FAIR data principles in geospatial data management
Bridges symbolic and subsymbolic GeoAI approaches
Outlines new research directions for geospatial knowledge graphs
Abstract
Geospatial knowledge graphs have emerged as a novel paradigm for representing and reasoning over geospatial information. In this framework, entities such as places, people, events, and observations are depicted as nodes, while their relationships are represented as edges. This graph-based data format lays the foundation for creating a "FAIR" (Findable, Accessible, Interoperable, and Reusable) environment, facilitating the management and analysis of geographic information. This entry first introduces key concepts in knowledge graphs along with their associated standardization and tools. It then delves into the application of knowledge graphs in geography and environmental sciences, emphasizing their role in bridging symbolic and subsymbolic GeoAI to address cross-disciplinary geospatial challenges. At the end, new research directions related to geospatial knowledge graphs are outlined.
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Taxonomy
TopicsSemantic Web and Ontologies · Geographic Information Systems Studies · Data Management and Algorithms