Reasoning Web. Explainable Artificial Intelligence [electronic resource] : 15th International Summer School 2019, Bolzano, Italy, September 20–24, 2019, Tutorial Lectures /

Classical Algorithms for Reasoning and Explanation in Description Logics -- Explanation-Friendly Query Answering Under Uncertainty -- Provenance in Databases: Principles and Applications -- Knowledge Representation and Rule Mining in Entity-Centric Knowledge Bases -- Explaining Data with Formal Concept Analysis -- Logic-based Learning of Answer Set Programs -- Constraint Learning: An Appetizer -- A Modest Markov Automata Tutorial -- Explainable AI Planning (XAIP): Overview and the Case of Contrastive.

The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shownuseful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.