Recently, hyperbolic embedding has successfully demonstrated its superiority over
Euclidean analogues in representing hierarchical data. As the scale-free network that …

The metaverse, as a paradigm continuously evolving in the next generation of the Internet,
aims to integrate various network applications. However, existing applications on the …

Multi-hop knowledge graph question answering (KGQA) targets at pinpointing the answer
entities to a natural language question by reasoning across multiple triples in knowledge …

Graph embedding is an important technique for improving the quality of link prediction
models on knowledge graphs. Although embedding based on neural networks can capture …

Incompleteness is a prominent issue pervasive in real-world knowledge graphs, and link
prediction techniques, which utilize known facts to forecast missing or unknown links, have …

Large Language Models (LLMs) have demonstrated surprising performance across various
natural language processing tasks. Recently, medical LLMs enhanced with domain-specific …

Knowledge graphs (KGs) play a vital role in enhancing search results and recommendation
systems. With the rapid increase in the size of KGs, they are becoming inaccurate and …

Nowadays, reasoning over knowledge graphs (KGs) has been widely adapted to empower
retrieval systems, recommender systems, and question answering systems, generating a …

Abstract Knowledge graph completion can solve the common problems of missing and
incomplete knowledge in the process of building knowledge graphs by predicting the …

Abstract Knowledge graph (KG) is a powerful tool in many areas, but it is impossible to take
in all knowledge during construction for the complexity of relations among natural entities. In …