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Geo-Object-Reader: a template filling method to jointly extract complex spatial information about geological objects.

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Bibliographic Details
Title: Geo-Object-Reader: a template filling method to jointly extract complex spatial information about geological objects.
Authors: Chu, Deping1 (AUTHOR), Wan, Bo2,3 (AUTHOR) wanbo@cug.edu.cn, Fang, Fang2 (AUTHOR), Zhou, Shunping2 (AUTHOR)
Source: International Journal of Geographical Information Science. Feb2026, Vol. 40 Issue 2, p506-534. 29p.
Subjects: Geospatial data, Spatio-temporal variation, Earth sciences, Graph neural networks, Geological formations, Spatial data structures
Abstract: Extracting spatial information from text subserves data-driven geospatial semantic research. Traditional methods consider words, phrases, or triples to extract spatial entities but often overlook specific spatiotemporal conditions, leading to fragmented representations and potentially inaccurate spatial perceptions. In this study, we present Geo-Object-Reader, a template-based method for the joint spatial information extraction (SIE) of spatial objects and spatiotemporal attributes. The joint extraction highlights an integrated representation of spatial object attributes, relations and their associated spatiotemporal conditions. This study develops three SIE templates tailored to the spatiotemporal characteristics of geospatial objects: spatial attribute template, non-spatial attribute template and 3D spatial relation template. These templates integrate specific spatiotemporal fields to ensure that the extracted attributes and relations are accurate and contextually relevant. A subsequent graph neural network approach captures the contextual information associated with these template fields to apprehend the complex interactions within geoscience texts. The final stage involves the use of a directed acyclic graph (DAG)-based filling strategy to enhance the efficiency of template filling. A dataset constructed based on Chinese geological reports was used to demonstrate that the proposed method provides holistic perspectives, bridging semantic gaps and forming more reliable knowledge chains compared to triples. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Extracting spatial information from text subserves data-driven geospatial semantic research. Traditional methods consider words, phrases, or triples to extract spatial entities but often overlook specific spatiotemporal conditions, leading to fragmented representations and potentially inaccurate spatial perceptions. In this study, we present Geo-Object-Reader, a template-based method for the joint spatial information extraction (SIE) of spatial objects and spatiotemporal attributes. The joint extraction highlights an integrated representation of spatial object attributes, relations and their associated spatiotemporal conditions. This study develops three SIE templates tailored to the spatiotemporal characteristics of geospatial objects: spatial attribute template, non-spatial attribute template and 3D spatial relation template. These templates integrate specific spatiotemporal fields to ensure that the extracted attributes and relations are accurate and contextually relevant. A subsequent graph neural network approach captures the contextual information associated with these template fields to apprehend the complex interactions within geoscience texts. The final stage involves the use of a directed acyclic graph (DAG)-based filling strategy to enhance the efficiency of template filling. A dataset constructed based on Chinese geological reports was used to demonstrate that the proposed method provides holistic perspectives, bridging semantic gaps and forming more reliable knowledge chains compared to triples. [ABSTRACT FROM AUTHOR]
ISSN:13658816
DOI:10.1080/13658816.2025.2528954