Automatic knowledge graph construction aims at manufacturing structured human
knowledge. To this end, much effort has historically been spent extracting informative fact …

The task of entity alignment between knowledge graphs (KGs) aims to identify every pair of
entities from two different KGs that represent the same entity. Many machine learning-based …

While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …

In this paper, we investigate a realistic but underexplored problem, called few-shot temporal
knowledge graph reasoning, that aims to predict future facts for newly emerging entities …

Graph neural networks (GNNs) are emerging for machine learning research on graph-
structured data. GNNs achieve state-of-the-art performance on many tasks, but they face …

Learning multi-agent system dynamics have been extensively studied for various real-world
applications, such as molecular dynamics in biology, multi-body system prediction in …

Knowledge graph question answering aims to identify answers of the query according to the
facts in the knowledge graph. In the vast majority of the existing works, the input queries are …

Graph Neural Networks (GNNs) have demonstrated great success in Knowledge Graph
Completion (KGC) by modeling how entities and relations interact in recent years. However …

Extensive knowledge graphs (KGs) have been constructed to facilitate knowledge-driven
tasks across various scenarios. However, existing work usually develops separate …

The human-oriented applications aim to exploit behaviors of people, which impose
challenges on user modeling of integrating social network (SN) with knowledge graph (KG) …