Many embedding approaches of drugs have been proposed for the downstream task of drug-
drug interaction (DDI) prediction in a DDI-derived network where drugs are considered …

Students' head pose estimation is a very difficult task since the training data is insufficient for
many head pose angles. In this study, we consider each head pose image as a Gaussian …

Abstract Knowledge graph embedding has received widespread attention in recent years.
Most existing models represent time-independent facts as low dimensional embeddings …

The rapid development and widespread applications of cognitive computing technologies
have led to a paradigm shift towards cognitive intelligent development in manufacturing …

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 …

Abstract Knowledge Graph (KG) is the network which contains some topic-based entities,
called nodes, and the associated information among the entities. Here, the concept in the …

Abstract Knowledge graphs, which contains billions of facts, have become a useful resource
for many AI-related downstream tasks, such as knowledge inference, personalized search …

Exercise recommendation plays a crucial role in facilitating learning outcomes. This poses
challenges because of the need to consider multiple aspects jointly to achieve practicality …

Abstract Knowledge graph embedding has found widespread application across various
fields due to its inherent structured data. However, some non-graph-based models …

Although the diagnosis and treatment of depression is a medical field, ICTs and AI
technologies are used widely to detect depression earlier in the elderly. These technologies …