87 citations · 202 across the 7 of their papers we have counts for
9 papers
Towards General-Purpose Representation Learning of Polygonal Geometries
Gengchen Mai, Chiyu Jiang, Weiwei Sun +6
Neural network representation learning for spatial data is a common need for geographic artificial intelligence (GeoAI) problems. In recent years, many advancements have been made…
Sphere2Vec: Multi-Scale Representation Learning over a Spherical Surface for Geospatial Predictions
Gengchen Mai, Yao Xuan, Wenyun Zuo +2
Generating learning-friendly representations for points in a 2D space is a fundamental and long-standing problem in machine learning. Recently, multi-scale encoding schemes (such a…
Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions
Gengchen Mai, Krzysztof Janowicz, Rui Zhu +2
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…
SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting
Gengchen Mai, Krzysztof Janowicz, Ling Cai +5
Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answ…
Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online
Gengchen Mai, Krzysztof Janowicz, Sathya Prasad +5
Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to pr…
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
Gengchen Mai, Krzysztof Janowicz, Bo Yan +3
Unsupervised text encoding models have recently fueled substantial progress in NLP. The key idea is to use neural networks to convert words in texts to vector space representations…