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AGILE: GIScience Series Open-access proceedings of the Association of Geographic Information Laboratories in Europe
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Articles | Volume 2
AGILE GIScience Ser., 2, 8, 2021
https://doi.org/10.5194/agile-giss-2-8-2021
AGILE GIScience Ser., 2, 8, 2021
https://doi.org/10.5194/agile-giss-2-8-2021

  04 Jun 2021

04 Jun 2021

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

Gengchen Mai1,2, Krzysztof Janowicz1,2, Rui Zhu1,2, Ling Cai1,2, and Ni Lao3 Gengchen Mai et al.
  • 1STKO Lab, Department of Geography, University of California, Santa Barbara, CA, USA
  • 2Center for Spatial Studies, University of California, Santa Barbara, CA, USA
  • 3Palo Alto, CA, USA

Keywords: geographic question answering, geographic question classification, geo-semantics, knowledge graphs

Abstract. As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language. While there has been substantial progress in open-domain question answering, QA systems are still struggling to answer questions which involve geographic entities or concepts and that require spatial operations. In this paper, we discuss the problem of geographic question answering (GeoQA). We first investigate the reasons why geographic questions are difficult to answer by analyzing challenges of geographic questions. We discuss the uniqueness of geographic questions compared to general QA. Then we review existing work on GeoQA and classify them by the types of questions they can address. Based on this survey, we provide a generic classification framework for geographic questions. Finally, we conclude our work by pointing out unique future research directions for GeoQA.

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