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

TL;DR

This paper explores the challenges, classification, and future directions of geographic question answering (GeoQA), highlighting its differences from general QA and reviewing existing approaches and research gaps.

Contribution

It provides a comprehensive analysis of GeoQA challenges, proposes a classification framework, and outlines future research directions in the field.

Findings

Geographic questions pose unique challenges due to spatial complexity.

Existing GeoQA systems are limited in scope and question types.

Future research should focus on addressing spatial reasoning and data integration.

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.