Abstract Knowledge graphs (KGs) model entities or concepts and their relations in a
structural manner. The incompleteness has turned out to be the main challenge that hinders …

Recently, there has been a surge in recommendations based on heterogeneous information
networks (HINs), attributed to their ability to integrate complex and rich semantics. Despite …

Collaborative filtering (CF) is one of the common approaches for recommendation. Recently,
graph-based methods, which use the holistic bipartite graph structure to learn user …

Collaborative filtering (CF) is a widely used technique in recommender systems. While many
CF methods primarily focus on collaborative signals derived from user–item interactions …

Multi-hop reasoning has gained significant attention in the area of knowledge graph
completion, intending to predict missing facts through existing knowledge. However, due to …

The knowledge graph, a networked structure designed to organize the vast and
heterogeneous knowledge existing in the real world, has gained widespread adoption as a …

Knowledge graph embeddings (KGEs) were originally developed to infer true but missing
facts in incomplete knowledge repositories. In this paper, we link knowledge graph …

Few-shot learning (FSL) aims to learn to new concepts based on very limited data. One of
the main challenges in FSL is the use of pretrained embeddings whose dimension is too …