Geographic Question Answering with Spatially-Explicit Machine Learning Models

As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions in natural language. With the advancement of deep learning technology, we have witnessed substantial progress in open-domain question answering. However, QA systems are still struggling to answer questions that involve geographic entities or concepts and that require spatial operations. In order to tackle these challenges, this dissertation specifically focuses on the problem of Geographic Question Answering (GeoQA) and develops a series of spatially-explicit machine learning models to handle different GeoQA tasks. First, in Chapter 1, we discuss the challenges of answering geographic questions and the uniqueness of GeoQA. A classification of geographic questions has been presented to facilitate the development of GeoQA. Next, in Chapter 2 a spatially-explicit query relaxation model is presented to demonstrate the usefulness of geographic information and spatial thinking in the geographic question answering and query relaxation process. To develop a more generalizable approach for GeoQA and other geospatial tasks, in Chapter 3, we present a general-purpose multi-scale representation learning model for geographic locations which can be utilized in multiple downstream tasks. It has been later on utilized to build a location-aware knowledge graph embedding model for a knowledge graph-based GeoQA model in Chapter 4. Only relying on points as the spatial representations for geographic entities is not sufficient to answer many geographic questions that involve spatial relations such as topological relations and cardinal direction relations. So in Chapter 5, we present a polygon encoder that can be used to answer multiple types of spatial relation questions. In the end, we draw a conclusion by listing several challenges of GeoQA which have not been solved in this dissertation and point out some future research directions. We hope this dissertation can reveal the importance of GeoQA and demonstrate the usefulness of spatially-explicit machine learning models on geospatial problems. We also hope GeoQA will become a unique research domain and serve as an important part of Geographic Artificial Intelligent (GeoAI) research