Abstract Knowledge Graph (KG) provides high-quality structured knowledge for various
downstream knowledge-aware tasks (such as recommendation and intelligent question …

The Knowledge graph, a multi-relational graph that represents rich factual information
among entities of diverse classifications, has gradually become one of the critical tools for …

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 …

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 …

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 …

Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing
research interest in recent years. Most existing methods focus on reasoning at past …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Abstract Aspect-based Sentiment Analysis (ABSA) is a subclass of sentiment analysis, which
aims to identify the sentiment polarity such as positive, negative, or neutral for specific …

Inductive link prediction---where entities during training and inference stages can be
different---has been shown to be promising for completing continuously evolving knowledge …

Static knowledge graphs (KGs), despite their wide usage in relational reasoning and
downstream tasks, fall short of realistic modeling of knowledge and facts that are only …