This comprehensive review provides an in-depth analysis of graph theory, various graph
types, and the role of graph visualization in scientific studies. Graphs serve as powerful tools …

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 …

Data-to-text Generation (D2T) aims to generate textual natural language statements that can
fluently and precisely describe the structured data such as graphs, tables, and meaning …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Dependency trees convey rich structural information that is proven useful for extracting
relations among entities in text. However, how to effectively make use of relevant information …

Document-level relation extraction requires integrating information within and across
multiple sentences of a document and capturing complex interactions between inter …

Despite their competitive performance on knowledge-intensive tasks, large language
models (LLMs) still have limitations in memorizing all world knowledge especially long tail …

Graph-to-text generation aims to generate fluent texts from graph-based data. In this paper,
we investigate two recently proposed pretrained language models (PLMs) and analyze the …

The dominant graph-to-sequence transduction models employ graph neural networks for
graph representation learning, where the structural information is reflected by the receptive …

Multilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in
different languages. The task of cross-lingual entity alignment is to match entities in a source …