A grammar for interpreting geo-analytical questions as concept transformations. Issue 2 (1st February 2023)

• Record Type: Journal Article
• Authors: Xu, Haiqi; Nyamsuren, Enkhbold; Scheider, Simon; Top, Eric
• Is Part Of: International journal of geographical information science. Volume 37:Issue 2(2023)
• Page Start: 276
• Page End: 306
• Publication Date: 2023-02-01
• Subjects: Geographic question answering -- geo-analytical questions -- grammatical parser -- natural language processing -- core concepts of spatial information; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2022.2077947
• Languages: English
• ISSNs: 1365-8816

Abstract

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.