The term life sciences refers to the disciplines that study living organisms and life processes,
and include chemistry, biology, medicine, and a range of other related disciplines. Research …

While Knowledge Graphs (KGs) have long been used as valuable sources of structured
knowledge, in recent years, KG embeddings have become a popular way of deriving …

This paper establishes a rigorous connection between circuit representations and tensor
factorizations, two seemingly distinct yet fundamentally related areas. By connecting these …

Neurosymbolic artificial intelligence (AI) is an increasingly active area of research that
combines symbolic reasoning methods with deep learning to leverage their complementary …

Most algorithms for representation learning and link prediction on relational data are
designed for static data. However, the data to which they are applied typically evolves over …

Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture
the multi-relational, heterogeneous, and dynamic natures of biomedical systems. KG …

Abstract Foundation Models (FMs) hold transformative potential to accelerate scientific
discovery, yet reaching their full capacity in complex, highly multimodal domains such as …

Abstract Background Knowledge graphs (KGs) are an important tool for representing
complex relationships between entities in the biomedical domain. Several methods have …

Abstract Knowledge graph embeddings, or KGEs, are models that learn vector
representations of knowledge graphs. These representations have been used for tasks such …

Knowledge graph embeddings, or KGEs, are models that learn vector representations of
knowledge graphs. These representations have been used for tasks such as predicting …