Geometric Feature Enhanced Knowledge Graph Embedding and Spatial Reasoning
This work addresses the need for customized GeoAI solutions in geospatial applications such as data retrieval and spatial reasoning, though it is incremental as it enhances general-purpose KGE with domain-specific features.
The study tackled the problem of geographic unawareness in existing knowledge graph embedding (KGE) techniques for Geospatial Knowledge Graphs (GeoKGs) by integrating geometric features like topology and direction, resulting in improved prediction accuracy for geoentities and spatial relations in link prediction tasks.
Geospatial Knowledge Graphs (GeoKGs) model geoentities (e.g., places and natural features) and spatial relationships in an interconnected manner, providing strong knowledge support for geographic applications, including data retrieval, question-answering, and spatial reasoning. However, existing methods for mining and reasoning from GeoKGs, such as popular knowledge graph embedding (KGE) techniques, lack geographic awareness. This study aims to enhance general-purpose KGE by developing new strategies and integrating geometric features of spatial relations, including topology, direction, and distance, to infuse the embedding process with geographic intuition. The new model is tested on downstream link prediction tasks, and the results show that the inclusion of geometric features, particularly topology and direction, improves prediction accuracy for both geoentities and spatial relations. Our research offers new perspectives for integrating spatial concepts and principles into the GeoKG mining process, providing customized GeoAI solutions for geospatial challenges.