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 …

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 …

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

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

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 …

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 …

Recent advancements in large language models (LLMs) have significantly improved various
natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next …

随着图神经网络的发展, 基于图神经网络的知识图谱嵌入方法日益受到研究人员的关注.
相比传统的方法, 它可以更好地处理实体的多样性和复杂性, 并捕捉实体的多重特征和复杂关系 …

Knowledge graphs (KGs) serve as useful resources for various applications including
machine learning, data mining, and artificial intelligence. Knowledge Graph Completion …

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