ABSTRACT A common need for artificial intelligence models in the broader geoscience is to
encode various types of spatial data, such as points, polylines, polygons, graphs, or rasters …

Information extraction methods proved to be effective at triple extraction from structured or
unstructured data. The organization of such triples in the form of (head entity, relation, tail …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

Heterogeneous graphs, which consist of multiple types of nodes and edges, are highly
suitable for characterizing real-world complex systems. In recent years, due to their strong …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Knowledge Graphs are increasingly becoming popular for a variety of downstream tasks like
Question Answering and Information Retrieval. However, the Knowledge Graphs are often …

Recently, a considerable literature has grown up around the theme of Graph Convolutional
Network (GCN). How to effectively leverage the rich structural information in complex …

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of
downstream tasks such as summarization, link prediction, information retrieval, and question …

Neural network representation learning for spatial data (eg, points, polylines, polygons, and
networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …