Real-world knowledge can be represented as a graph consisting of entities and
relationships between the entities. The need for efficient and scalable solutions arises when …

Abstract Background Knowledges graphs (KGs) serve as a convenient framework for
structuring knowledge. A number of computational methods have been developed to …

This panel will focus on industry applications related to knowledge graph and showcase
how knowledge graph transforms the conventional and unconventional industries to the new …

Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have
been designed to reason on knowledge graphs (KGs). An important design component of …

Since the first identified case of COVID-19 in December 2019, a plethora of pharmaceuticals
and therapeutics have been tested for COVID-19 treatment. While medical advancements …

Computing latent representations for graph-structured data is an ubiquitous learning task in
many industrial and academic applications ranging from molecule synthetization to social …

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing
response to the COVID-19 pandemic by the biomedical research community. While rich …

Purpose This study aims to evaluate a method of building a biomedical knowledge graph
(KG). Design/methodology/approach This research first constructs a COVID-19 KG on the …

In recent years, Knowledge Graph (KG) development has attracted significant researches
considering the applications in web search, relation prediction, natural language …

Knowledge graphs (KGs) are a popular way to organise information based on ontologies or
schemas. Despite advances in KGs, representing knowledge remains a non-trivial task …