Reasoning web : causality, explanations and declarative knowledge : 18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial lectures / Leopoldo Bertossi, Guohui Xiao, editors.
Summer School on Reasoning Web (18th : 2022 : Online); Bertossi, Leopoldo, editor.; Xiao, Guohui, editor.
2023
Q334 .S86 2022eb
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Title
Reasoning web : causality, explanations and declarative knowledge : 18th International Summer School 2022, Berlin, Germany, September 27-30, 2022, Tutorial lectures / Leopoldo Bertossi, Guohui Xiao, editors.
Meeting Name
Summer School on Reasoning Web (18th : 2022 : Online)
ISBN
9783031314148 (electronic bk.)
303131414X (electronic bk.)
9783031314131
3031314131
DOI
https://doi.org/10.1007/978-3-031-31414-8
Published
Cham, Switzerland : Springer, 2023.
Language
English
Description
1 online resource (ix, 211 pages) : illustrations (some color).
Item Number
10.1007/978-3-031-31414-8 doi
Call Number
Q334 .S86 2022eb
Dewey Decimal Classification
006.3
Summary
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.
Note
"It was held online due to anticipated mobility restrictions caused by the Covid-19 pandemic."-- Preface.
Inlcudes author index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed May 2, 2023).
Added Author
Bertossi, Leopoldo, editor.
Xiao, Guohui, editor.
Series
Lecture notes in computer science ; 13759. 1611-3349
Available in Other Form
Print version: 9783031314131
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.

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