Human knowledge provides a formal understanding of the world. Knowledge graphs that
represent structural relations between entities have become an increasingly popular …

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

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 …

One of the most critical tasks for improving data quality and increasing the reliability of data
analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to …

Semantic embedding of knowledge graphs has been widely studied and used for prediction
and statistical analysis tasks across various domains such as Natural Language Processing …

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 …

Abstract Knowledge Graphs (KGs), as a structured human knowledge, manage data in an
ease-of-store, recognizable, and understandable way for machines and provide a rich …

This work studies the use of visual semantic representations to align entities in
heterogeneous knowledge graphs (KGs). Images are natural components of many existing …

Multi-modal entity alignment aims to identify equivalent entities between two different multi-
modal knowledge graphs, which consist of structural triples and images associated with …

Capturing associations for knowledge graphs (KGs) through entity alignment, entity type
inference and other related tasks benefits NLP applications with comprehensive knowledge …