The rapid generation of large amounts of information about the coronavirus SARS-CoV-2
and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview …

Clustering data effectively remains a significant challenge in machine learning, particularly
when the optimal number of clusters is unknown. Traditional deep clustering methods often …

Most real-world knowledge graphs, including Wikidata, DBpedia, and Yago are incomplete.
Answering queries on such incomplete graphs is an important, but challenging problem …

Ontologies are a standard tool for creating semantic schemata in many knowledge intensive
domains of human interest. They are becoming increasingly important also in the areas that …

Abstract Concept learning approaches based on refinement operators explore partially
ordered solution spaces to compute concepts, which are used as binary classification …

Many applications require explainable node classification in knowledge graphs. Towards
this end, a popular “white-box” approach is class expression learning: Given sets of positive …

Node classification is an important task in many fields, eg, predicting entity types in
knowledge graphs, classifying papers in citation graphs, or classifying nodes in social …

In reverse engineering of database queries, we aim to construct a query from a given set of
answers and non-answers; it can then be used to explore the data further or as an …

Graph Neural Networks (GNNs) are effective for node classification in graph-structured data,
but they lack explainability, especially at the global level. Current research mainly utilizes …

We consider the problem of class expression learning using cardinality-minimal sets of
examples. Recent class expression learning approaches employ deep neural networks and …