Geospatial Topological Relation Extraction from Text with Knowledge Augmentation

Geospatial topological relation extraction (GeoTopoRE) aims to extract topological relations between named geospatial entities (i.e., geo-entities) in text. It is a domain-specific relation extraction (RE) task essential in geospatial knowledge graph construction and spatial reasoning. Unlike general-purpose RE, which primarily depends on semantic and syntactic cues, GeoTopoRE requires integrating geometric knowledge about geo-entities. This is essential for accurately capturing or inferring the complex geospatial relationships among entities. GeoTopoRE is not studied systematically and lacks dedicated datasets for evaluation, posing significant challenges to developing and assessing effective models. This study presents two major contributions: (i) the introduction of a high-quality, human-labeled dataset WikiTopo for the GeoTopoRE task, and (ii) a novel framework GeoWISE designed to adapt existing RE models to the GeoTopoRE task, with integrated semantic and external geospatial domain knowledge. We leverage coarse-to-fine-grained natural language inference (NLI) to align externally sourced knowledge with the semantic text context, enhanced by geospatial expertise. This integrated knowledge is then conveyed to language models as geospatial cues, enabling a nuanced understanding of topological relations. Empirical results demonstrate the efficacy of our framework in few-shot settings, showing significant and consistent improvements in the GeoTopoRE task for diverse state-of-the-art RE models.