View in EDS

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

Saved in:
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]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 191070889
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Geo-Object-Reader: a template filling method to jointly extract complex spatial information about geological objects.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Chu%2C+Deping%22">Chu, Deping</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wan%2C+Bo%22">Wan, Bo</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> wanbo@cug.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Fang%2C+Fang%22">Fang, Fang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Shunping%22">Zhou, Shunping</searchLink><relatesTo>2</relatesTo> (AUTHOR)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Feb2026, Vol. 40 Issue 2, p506-534. 29p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Geospatial+data%22">Geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Spatio-temporal+variation%22">Spatio-temporal variation</searchLink><br /><searchLink fieldCode="DE" term="%22Earth+sciences%22">Earth sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Geological+formations%22">Geological formations</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+data+structures%22">Spatial data structures</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=191070889
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1080/13658816.2025.2528954
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 506
    Subjects:
      – SubjectFull: Geospatial data
        Type: general
      – SubjectFull: Spatio-temporal variation
        Type: general
      – SubjectFull: Earth sciences
        Type: general
      – SubjectFull: Graph neural networks
        Type: general
      – SubjectFull: Geological formations
        Type: general
      – SubjectFull: Spatial data structures
        Type: general
    Titles:
      – TitleFull: Geo-Object-Reader: a template filling method to jointly extract complex spatial information about geological objects.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Chu, Deping
      – PersonEntity:
          Name:
            NameFull: Wan, Bo
      – PersonEntity:
          Name:
            NameFull: Fang, Fang
      – PersonEntity:
          Name:
            NameFull: Zhou, Shunping
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 02
              Text: Feb2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 13658816
          Numbering:
            – Type: volume
              Value: 40
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: International Journal of Geographical Information Science
              Type: main
ResultId 1