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 …

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

Node classification in knowledge graphs aids in the discovery of new drugs, the
identification of risky users in social networks, and the completion of missing type …

Class expression learning in description logics has long been regarded as an iterative
search problem in an infinite conceptual space. Each iteration of the search process invokes …

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

As machine learning and deep learning become increasingly integrated into our daily lives,
understanding how these technologies make decisions is crucial. To ensure transparency …

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

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 …

Rule mining on knowledge graphs allows for explainable link prediction. Contrarily,
embedding-based methods for link prediction are well known for their generalization …

Abstract Knowledge bases are now first-class citizens of the Web. Circa 50% of the 3.2
billion websites in the 2022 crawl of Web Data Commons contains knowledge base …