With the growth of data and intelligent technologies, the healthcare sector opened numerous
technology that enabled services for patients, clinicians, and researchers. One major hurdle …

Embedding static graphs in low-dimensional vector spaces plays a key role in network
analytics and inference, supporting applications like node classification, link prediction, and …

Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on
mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research …

Knowledge Graph (KG) reasoning that predicts missing facts for incomplete KGs has been
widely explored. However, reasoning over Temporal KG (TKG) that predicts facts in the …

Knowledge graphs (KGs) have been widely used in the field of artificial intelligence, such as
in information retrieval, natural language processing, recommendation systems, etc …

Graphs arise naturally in many real-world applications including social networks,
recommender systems, ontologies, biology, and computational finance. Traditionally …

Temporal Knowledge graph (TKG) reasoning aims to predict missing facts based on
historical TKG data. Most of the existing methods are incapable of explicitly modeling the …

Use of conversational agents, like chatbots, avatars, and robots is increasing worldwide. Yet,
their effectiveness in health care is largely unknown. The aim of this advanced review was to …

Transformers have achieved remarkable performance in widespread fields, including
natural language processing, computer vision and graph mining. However, vanilla …

Existing knowledge graph (KG) embedding models have primarily focused on static KGs.
However, real-world KGs do not remain static, but rather evolve and grow in tandem with the …