Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …

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 …

Foundation models in language and vision have the ability to run inference on any textual
and visual inputs thanks to the transferable representations such as a vocabulary of tokens …

GraIL and its variants have shown their promising capacities for inductive relation reasoning
on knowledge graphs. However, the uni-directional message-passing mechanism hinders …

Abstract Knowledge Graph (KG) stores human knowledge facts in an intuitive graphical
structure but faces challenges such as incomplete construction or inability to handle new …

Audiovisual data is everywhere in this digital age, which raises higher requirements for the
deep learning models developed on them. To well handle the information of the multi-modal …

Although extensive research has been conducted on 3D point cloud segmentation,
effectively adapting generic models to novel categories remains a formidable challenge …

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

The task of inductive link prediction in knowledge graphs (KGs) generally focuses on test
predictions with solely new nodes but not both new nodes and new relation types. In this …

Since the dynamic characteristics of knowledge graphs, many inductive knowledge graph
representation learning (KGRL) works have been proposed in recent years, focusing on …