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From spatial representation to processes, relational networks, and thematic roles in geographic information retrieval

Author:

Benjamin AdamsAuthors Info & Claims

GIR'18: Proceedings of the 12th Workshop on Geographic Information Retrieval

Article No.: 1, Pages 1 - 2

https://doi.org/10.1145/3281354.3281355

Published: 06 November 2018 Publication History

Abstract

Geographic information retrieval (GIR) has largely been synonymous with spatial information retrieval. However, geographic information in text is not always explicitly, nor even implicitly, spatial, and when people are seeking geographic information, it is not exlusively for the purpose of spatial analysis or understanding. Here we argue that GIR research is artificially limited by a focus on static spatial representation and we could expand GIR's domain of interest to include organization of information that is non-spatial. Specifically, we highlight three areas worth further study: 1) information about geographic processes and change in text, 2) geographic information where relational context is more important than spatial context, and 3) geographic entities referenced in text that take on thematic (non-spatial) roles.

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Cited By

Wang BFei TKang YLi MDu QHan MDong N(2020)Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context windowPLOS ONE10.1371/journal.pone.023634715:7(e0236347)Online publication date: 23-Jul-2020
https://doi.org/10.1371/journal.pone.0236347
Hu YAdams B(2020)Harvesting Big Geospatial Data from Natural Language TextsHandbook of Big Geospatial Data10.1007/978-3-030-55462-0_19(487-507)Online publication date: 17-Dec-2020
https://doi.org/10.1007/978-3-030-55462-0_19

Index Terms

From spatial representation to processes, relational networks, and thematic roles in geographic information retrieval
1. Information systems
  1. Information retrieval
    1. Document representation
      1. Content analysis and feature selection
      2. Document structure
  2. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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Published In

cover image ACM Conferences

GIR'18: Proceedings of the 12th Workshop on Geographic Information Retrieval

November 2018

37 pages

ISBN:9781450360340

DOI:10.1145/3281354

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 November 2018

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SIGSPATIAL '18

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SIGSPATIAL

SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 6, 2018

WA, Seattle, USA

Acceptance Rates

GIR'18 Paper Acceptance Rate 8 of 12 submissions, 67%;

Overall Acceptance Rate 46 of 61 submissions, 75%

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Cited By

Wang BFei TKang YLi MDu QHan MDong N(2020)Understanding the spatial dimension of natural language by measuring the spatial semantic similarity of words through a scalable geospatial context windowPLOS ONE10.1371/journal.pone.023634715:7(e0236347)Online publication date: 23-Jul-2020
https://doi.org/10.1371/journal.pone.0236347
Hu YAdams B(2020)Harvesting Big Geospatial Data from Natural Language TextsHandbook of Big Geospatial Data10.1007/978-3-030-55462-0_19(487-507)Online publication date: 17-Dec-2020
https://doi.org/10.1007/978-3-030-55462-0_19

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