https://orcid.org/0000-0002-2267-4810
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Abstract
Geographic Question Answering (GeoQA) systems can automatically answer questions phrased in natural language. Potentially this may enable data analysts to make use of geographic information without requiring any GIS skills. However, going beyond the retrieval of existing geographic facts on particular places remains a challenge. Current systems usually cannot handle geo-analytical questions that require GIS analysis procedures to arrive at answers. To enable geo-analytical QA, GeoQA systems need to interpret questions in terms of a transformation that can be implemented in a GIS workflow. To this end, we propose a novel approach to question parsing that interprets questions in terms of core concepts of spatial information and their functional roles in context-free grammar. The core concepts help model spatial information in questions independently from implementation formats, and their functional roles indicate how concepts are transformed and used in a workflow. Using our parser, geo-analytical questions can be converted into expressions of concept transformations corresponding to abstract GIS workflows. We developed our approach on a corpus of 309 GIS-related questions and tested it on an independent source of 134 test questions including workflows. The evaluation results show high precision and recall on a gold standard of concept transformations.
Acknowledgments
We are thankful to ESRI as well as the QGIS community who provide high-quality online teaching resources that made this study possible.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data and codes availability statement
The data and codes that support this study are available in ‘figshare.com’ with DOI https://doi.org/10.6084/m9.figshare.17009003.v7.
Notes
3 For example, based on using transformer models in natural language processing (NLP) (Devlin et al. Citation2018).
11 Measurement levels here do not help translate natural language questions into concept transformations but could help generate GIS workflows in the future. Hence, we annotated questions using core concepts together with measurement levels.
Additional information
Funding
Notes on contributors
Haiqi Xu
Haiqi Xu is a Ph.D. student at the Department of Human Geography and Spatial Planning Urban Geography at Utrecht University. Her research interests include GeoAI, geographic question answering, and natural language processing. She contributed to the idea, methodology, evaluation, manuscript writing, and revision of this paper.
Enkhbold Nyamsuren
Enkhbold Nyamsuren is a postdoctoral researcher at the Department of Human Geography and Spatial Planning Urban Geography at Utrecht University. He holds research expertise in computer science and cognitive modeling. He contributed to the methodology, evaluation, and writing of this paper.
Simon Scheider
Simon Scheider is an assistant professor at the Department of Human Geography and Spatial Planning Urban Geography at Utrecht University. His research interest lies at the interface between conceptual modeling, geographic data analysis, and knowledge extraction. He and his group are currently working on a geo-analytical question answering system. He contributed to the idea, writing, and revision of this paper.
Eric Top
Eric Top is a Ph.D. student at the Department of Human Geography and Spatial Planning Urban Geography at Utrecht University. His research interests include knowledge representation, data semantics, and concept formalization in the context of geography and GIS. In particular, his research concerns developing a theory of quantities in geographic information. He contributed to the writing of this paper.