Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

The incorporation of data analytics in the healthcare industry has made significant progress,
driven by the demand for efficient and effective big data analytics solutions. Knowledge …

A knowledge graph (KG), also known as a knowledge base, is a particular kind of network
structure in which the node indicates entity and the edge represent relation. However, with …

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have
been in use in a variety of intelligent medical applications. Thus, understanding the research …

Drug–drug interactions (DDI) are a critical aspect of drug research that can have adverse
effects on patients and can lead to serious consequences. Predicting these events …

Machine learning has been heavily researched and widely used in many disciplines.
However, achieving high accuracy requires a large amount of data that is sometimes …

Despite the importance and abundance of temporal knowledge graphs, most of the current
research has been focused on reasoning on static graphs. In this paper, we study the …

Biomedical knowledge graphs (KGs), which can help with the understanding of complex
biological systems and pathologies, have begun to play a critical role in medical practice …

Knowledge graphs in manufacturing and production aim to make production lines more
efficient and flexible with higher quality output. This makes knowledge graphs attractive for …

Interference between pharmacological substances can cause serious medical injuries.
Correctly predicting so-called drug-drug interactions (DDI) does not only reduce these cases …