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 …

Extrapolation reasoning on temporal knowledge graphs (TKGs) aims to forecast future facts
based on past counterparts. There are two main challenges:(1) incorporating the complex …

Abstract Knowledge graph (KG) reasoning utilizes two primary techniques, ie, rule-based
and KG-embedding based. The former provides precise inferences, but inferring via …

Logical rules are essential for uncovering the logical connections between relations, which
could improve the reasoning performance and provide interpretable results on knowledge …

Logical reasoning consistently plays a fundamental and significant role in the domains of
knowledge engineering and artificial intelligence. Recently, Large Language Models (LLMs) …

Math formulas (eg," distance= speed X time'') serve as one of the fundamental
commonsense knowledge in human cognition, where humans naturally acquire and …

Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have
been designed to reason on knowledge graphs (KGs). An important design component of …

Heterogeneous Information Networks (HINs) are information networks with multiple types of
nodes and edges. The concept of meta-path, ie, a sequence of entity types and relation …

The use of knowledge graphs has gained significant traction in a wide variety of
applications, ranging from recommender systems and question answering to fact checking …

Learning logical rules is critical to improving reasoning in KGs. This is due to their ability to
provide logical and interpretable explanations when used for predictions, as well as their …