Explainability in Machine Learning -- Causal Explanations and Fairness in Data -- Statistical Relational Extensions of Answer Set Programming -- Vadalog: Its Extensions and Business Applications -- Cross-Modal Knowledge Discovery, Inference, and Challenges -- Reasoning with Tractable Probabilistic Circuits -- From Statistical Relational to Neural Symbolic Artificial Intelligence -- Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.

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 "Reasoning in Probabilistic Models and Machine Learning" 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: Logic-Based Explainability in Machine Learning; Causal Explanations and Fairness in Data; Statistical Relational Extensions of Answer Set Programming; Vadalog: Its Extensions and Business Applications; Cross-Modal Knowledge Discovery, Inference, and Challenges; Reasoning with Tractable Probabilistic Circuits; From Statistical Relational to Neural Symbolic Artificial Intelligence; Building Intelligent Data Apps in Rel using Reasoning and Probabilistic Modelling.