Information extraction methods proved to be effective at triple extraction from structured or
unstructured data. The organization of such triples in the form of (head entity, relation, tail …

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 …

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

Skeleton-based action recognition depends on skeleton sequences to detect categories of
human actions. In skeleton-based action recognition, the recognition of action scenes with …

Nowadays, a large number of Knowledge Graph Completion (KGC) methods have been
proposed by using embedding based manners, to overcome the incompleteness problem …

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

Abstract Knowledge graphs (KGs) are regarded as important resources for a variety of
artificial intelligence (AI) and auxiliary decision tasks but suffer from incompleteness. To …

The advent of Large Language Models (LLMs) has significantly transformed the AI
landscape, enhancing machine learning and AI capabilities. Factuality issue is a critical …

Abstract Knowledge Graphs (KGs) constitute an indispensable corpus of structured
knowledge, underpinning a plethora of analytical applications, notably in the realms of …

Abstract Knowledge graph (KG) has gradually become the cornerstone of many Artificial
Intelligence (AI) tasks, as one of the most effective ways to represent world knowledge, while …