Link prediction is a very fundamental task on graphs. Inspired by traditional path-based
methods, in this paper we propose a general and flexible representation learning framework …

Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest.
Here, graph representation learning has become the dominant paradigm for link prediction …

Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental
task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge …

There has recently been increasing interest in learning representations of temporal
knowledge graphs (KGs), which record the dynamic relationships between entities over …

Graph walking based on reinforcement learning (RL) has shown great success in navigating
an agent to automatically complete various reasoning tasks over an incomplete knowledge …

Visual question answering is concerned with answering free-form questions about an
image. Since it requires a deep linguistic understanding of the question and the ability to …

Biomedical knowledge graphs permit an integrative computational approach to reasoning
about biological systems. The nature of biological data leads to a graph structure that differs …

In this paper, an explainable artificial intelligence (AI) technique is employed to analyze the
match style and gameplay of the national basketball association (NBA). A descriptive …

In recent years, reasoning over knowledge graphs (KGs) has been widely adapted to
empower retrieval systems, recommender systems, and question answering systems …

Link prediction in knowledge hypergraphs has been recognized as a critical issue in various
downstream tasks for knowledge-enabled applications, from question answering to …