Deep learning (DL) has been widely used in various fields. However, its black-box nature
limits people's understanding and trust in its decision-making process. Therefore, it becomes …

Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence
(AI) field. The transformer model is a type of DNN that was originally used for the natural …

Graph neural networks (GNNs), which effectively use topological structures in the
knowledge graphs (KG) to embed entities and relations in low-dimensional spaces, have …

Edges in many real-world social/information networks are associated with rich text
information (eg, user-user communications or user-product reviews). However, mainstream …

Recent studies have demonstrated the significant role that circRNA plays in the progression
of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner …

A vast amount of textual and structural information is required for knowledge graph
construction and its downstream tasks. However, most of the current knowledge graphs are …

Large Language Models (LLMs) have received considerable interest in wide applications
lately. During pre-training via massive datasets, such a model implicitly memorizes the …

Factorization of a matrix into a product of two rectangular factors, is a classic tool in various
machine learning applications. Tensor factorizations generalize this concept to more than …

Recently, fully-inductive link prediction in knowledge graphs (KGs) has aimed to predict
missing links between unseen–unseen entities, independently completing evolving KGs …

In knowledge graphs (KGs), logic rules offer interpretable explanations for predictions and
are essential for reasoning on downstream tasks, such as question answering. However, a …