Reasoning Web. Declarative Artificial Intelligence

The purpose of the Reasoning Web Summer School is to disseminate recent advances on reasoning techniques and related issues that are of particular interest to Semantic Web and Linked Data applications. It is primarily intended for postgraduate students, postdocs, young researchers, and senior researchers wishing to deepen their knowledge. As in the previous years, lectures in the summer school were given by a distinguished group of expert lecturers. The broad theme of this year's summer school was again Declarative Artificial Intelligence and it covered various aspects of ontological reasoning and related issues that are of particular interest to Semantic Web and Linked Data applications. The following eight lectures were presented during the school: Foundations of Graph Path Query Languages; On Combining Ontologies and Rules; Modelling Symbolic Knowledge Using Neural Representations; Mining the Semantic Web with Machine Learning: Main Issues That Need to Be Known; Temporal ASP: From Logical Foundations to Practical Use with telingo; A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs; and Score-Based Explanations in Data Management and Machine Learning.

Useful for students, researchers, and practitioners engaged with Declarative Artificial Intelligence Thoroughly revised tutorials cover various aspects of ontological reasoning and related issues Lecturers are known experts in this field

Autorentext

Mantas simkus, TU Wien, Vienna, AustriaIvan Varzinczak, Centre de Recherche en Informatique de Lens (CRIL) and Université de Artois and CNRS, Lens, France

Inhalt
Foundations of Graph Path Query Languages.- On Combining Ontologies and Rules.- Modelling Symbolic Knowledge using Neural Representations.- Mining the Semantic Web with Machine Learning: main issues that need to be known.- Temporal ASP: from logical foundations to practical use with telingo.- A Review of SHACL: From Data Validation to Schema Reasoning for RDF Graphs.- Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis.