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 Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

Deploying large language models (LLMs) to real scenarios for domain-specific question
answering (QA) is a key thrust for LLM applications, which poses numerous challenges …

Multi-modal knowledge graph completion (MMKGC) aims to automatically discover the
unobserved factual knowledge from a given multi-modal knowledge graph by collaboratively …

As a crucial extension of entity alignment (EA), multi-modal entity alignment (MMEA) aims to
identify identical entities across disparate knowledge graphs (KGs) by exploiting associated …

Large language models (LLMs) have significantly advanced performance across a spectrum
of natural language processing (NLP) tasks. Yet, their application to knowledge graphs …

Fusing data from different sources to improve decision making in smart cities has received
increasing attention. Collected data through sensors usually exist in a multi-modal form …

Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and
improving information retrieval and question-answering systems. Existing methods often …

Recent embedding-based methods have achieved great successes in exploiting entity
alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we …

Abstract Knowledge Graphs (KGs) are pivotal in various NLP applications but often grapple
with incompleteness, especially due to the long-tail problem where infrequent, unpopular …