A controlled natural language and interface for formulating geo-analytical questions with Blockly

While question-answering (QA) has gained attention in recent years, an often overlooked condition for receiving a valid answer is the quality of the question. The ability to formulate sufficiently clear and specific questions is crucial, in particular, for indirect, geo-analytical question-answering systems that need to generate valid geoprocessing workflows. We present a new method for formulating geo-analytical questions and specifying their semantics by integrating a controlled natural language for expressing geo-analytic intentions with a semantic model of geo-analytical concept transformations. This method is implemented in a customized Google Blockly interface, where users can construct questions by selecting and connecting blocks representing different conceptual components of geo-analytical questions. We conducted a user study to compare the Blockly-based interface with a free-form interface. Results show that the Blockly-based interface effectively constrains question formulation, leading to more standardized, complete, and interpretable questions regardless of users’ GIS expertise levels. However, the Blockly-based interface requires more learning effort and receives a medium-to-low usability rating. Our study shows the potential for template-based geo-analytical QA to foster a particular level of quality as input to QA systems. Future research should combine this controlled language with generative models to enable higher quality, adaptability, and scalability of question answering.