A grammar for interpreting geo-analytical questions as concept transformations

Area of Science:

• Geographic Information Systems (GIS)
• Natural Language Processing (NLP)
• Spatial Analysis

Background:

• Geographic Question Answering (GeoQA) systems currently struggle with geo-analytical questions requiring complex GIS procedures.
• Existing systems primarily focus on retrieving factual geographic information, not performing spatial analysis.
• Bridging the gap requires GeoQA systems to interpret questions as executable GIS workflows.

Purpose of the Study:

• To develop a novel approach for geo-analytical question answering (GeoQA).
• To enable GeoQA systems to interpret natural language questions as transformations within GIS workflows.
• To enhance the analytical capabilities of GeoQA beyond simple fact retrieval.

Main Methods:

• Proposed a question parsing approach based on core concepts of spatial information and their functional roles within a context-free grammar framework.
• Developed a method to model spatial information independently of implementation formats.
• Defined functional roles to represent transformations and usage within a GIS workflow.

Main Results:

• Successfully converted geo-analytical questions into expressions of concept transformations representing abstract GIS workflows.
• Developed and tested the approach on a corpus of 309 GIS-related questions and an independent set of 134 test questions.
• Achieved high precision and recall on a gold standard of concept transformations.

Conclusions:

• The proposed parsing approach effectively interprets geo-analytical questions for GIS workflows.
• This method significantly advances the capability of GeoQA systems to perform spatial analysis.
• Enables data analysts to leverage geographic information through advanced analytical queries without extensive GIS expertise.