Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
- 1STKO Lab, Department of Geography, University of California, Santa Barbara, CA, USA
- 2Center for Spatial Studies, University of California, Santa Barbara, CA, USA
- 3Palo Alto, CA, USA
Keywords: geographic question answering, geographic question classification, geo-semantics, knowledge graphs
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.