Relational machine learning studies methods for the statistical analysis of relational, or
graph-structured, data. In this paper, we provide a review of how such statistical models can …

Abstract Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts. They
are widely used in entity recognition, structured search, question answering, and other …

We present Dedalo, a framework which is able to exploit Linked Data to generate
explanations for clusters. In general, any result of a Knowledge Discovery process, including …

Advances in information extraction have enabled the automatic construction of large
knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. Learning rules from …

Advances in information extraction have enabled the automatic construction of large
knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are …

Latent factor models are increasingly popular for modeling multi-relational knowledge
graphs. By their vectorial nature, it is not only hard to interpret why this class of models works …

Recent advances in information extraction have led to the so-called knowledge graphs
(KGs), ie, huge collections of relational factual knowledge. Since KGs are automatically …

Abstract Onto-Relational Learning is an extension of Relational Learning aimed at
accounting for ontologies in a clear, well-founded and elegant manner. The system-QuIn …

Among the inferences studied in Description Logics (DLs), induction has been paid
increasing attention over the last decade. Indeed, useful non-standard reasoning tasks can …

Rules complement and extend ontologies on the Semantic Web. We refer to these rules as
onto-relational since they combine DL-based ontology languages and Knowledge …