Reasoning Web. Causality, Explanations and Declarative Knowledge
18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial Lectures
Published on 27. April 2023
IX, 211 pages
978-3-031-31414-8 (ISBN)
Description
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.
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.
Reviews / Votes
"The book is highly recommended for a variety of readers. Undergraduate and graduate students should find it useful, especially the initial chapters, while the later chapters are best suited for a specialized audience. As an additional benefit, a YouTube playlist is available, which includes video lectures on the topics covered by the book and more." (Corrado Mencar, Computing Reviews, October 15, 2024)
More details
Series
Edition
1st ed. 2023
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Illustrations
IX, 211 p. 22 illus., 15 illus. in color.
File size
5,29 MB
ISBN-13
978-3-031-31414-8 (9783031314148)
DOI
10.1007/978-3-031-31414-8
Schweitzer Classification
Other editions
Additional editions
Leopoldo Bertossi | Guohui Xiao
Reasoning Web. Causality, Explanations and Declarative Knowledge
18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial Lectures
Book
04/2023
Springer
€62.05
Shipment within 15-20 days
Persons
Leopoldo Bertossi,
Skema Business School, Montreal, Canada
Skema Business School, Montreal, Canada
Guohui Xiao
University of Bergen, Bergen, Norway
Content
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.
