Background Representation learning provides new and powerful graph analytical
approaches and tools for the highly valued data science challenge of mining knowledge …

Complex biological systems are traditionally modelled as graphs of interconnected
biological entities. These graphs, ie biological knowledge graphs, are then processed using …

Adoption of recently developed methods from machine learning has given rise to creation of
drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain …

Linked data and bio-ontologies enabling knowledge representation, standardization, and
dissemination are an integral part of developing biological and biomedical databases. That …

Knowledge graph embeddings (KGEs) have received significant attention in other domains
due to their ability to predict links and create dense representations for graphs' nodes and …

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

Graph is a natural representation encoding both the features of the data samples and
relationships among them. Analysis with graphs is a classic topic in data mining and many …

Graph neural networks (GNN) have emerged as a new state-of-the-art for learning
knowledge graph representations. Although they have shown impressive performance in …

Knowledge representation learning represents entities and relations of knowledge graph in
a continuous low-dimensional semantic space. Recently, various representation learning …

Motivation Biological data and knowledge bases increasingly rely on Semantic Web
technologies and the use of knowledge graphs for data integration, retrieval and federated …