Claudia d'Amato

TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.

TL;DR: In this paper, the authors provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.

TL;DR: A FOIL-like algorithm is presented that can be applied to general DL languages, discussing related theoretical aspects of learning with the inherent incompleteness underlying the semantics of this representation.

TL;DR: It is argued that machine learning research has to offer a wide variety of methods applicable to different expressivity levels ofSemantic Web knowledge bases: ranging from weakly expressive but widely available knowledge bases in RDF to highly expressive first-order knowledge bases, this paper surveys statistical approaches to mining the Semantic Web.

TL;DR: This work proposes a semantic similarity measure for complex Description Logics concept descriptions that elicits the underlying ontology semantics and theorizes a set of criteria that a measure has to satisfy in order to be compliant with a semantic expected behavior.