In many application domains data from different sources are increasingly available to
thoroughly monitor and describe a system or device. Especially within the industrial …

Deep learning models contributed to reaching unprecedented results in prediction and
classification tasks of Artificial Intelligence (AI) systems. However, alongside this notable …

The emergence of various disruptive technologies such as big data, Internet of Things, and
artificial intelligence have instigated our society to generate enormous volumes of data. The …

Question answering over knowledge graphs (KGQA) has evolved from simple single-fact
questions to complex questions that require graph traversal and aggregation. We propose a …

Background Understanding the impact of gene interactions on disease phenotypes is
increasingly recognised as a crucial aspect of genetic disease research. This trend is …

Deep learning techniques are increasingly being applied to solve various machine learning
tasks that use Knowledge Graphs as input data. However, these techniques typically learn a …

The growing availability of multirelational data gives rise to an opportunity for novel
characterization of complex real-world relations, supporting the proliferation of diverse …

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

Background Accurately recognizing rare diseases based on symptom description is an
important task in patient triage, early risk stratification, and target therapies. However, due to …

Clinical, biomedical, and translational science has reached an inflection point in the breadth
and diversity of available data and the potential impact of such data to improve human …