This volume contains 8 lecture notes of the 16th Reasoning Web Summer School (RW 2020), held in Oslo, Norway, in June 2020.
The Reasoning Web series of annual summer schools has become the prime educational event in the field of reasoning techniques on the Web, attracting both young and established researchers. The broad theme of this year's summer school was “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 have been presented during the school: Introduction to Probabilistic Ontologies, On the Complexity of Learning Description Logic Ontologies, Explanation via Machine Arguing, Stream Reasoning: From Theory to Practice, First-Order Rewritability of Temporal Ontology-Mediated Queries, An Introduction to Answer Set Programming and Some of Its Extensions, Declarative Data Analysis using Limit Datalog Programs, and Knowledge Graphs: Research Directions.
Author(s): Marco Manna, Andreas Pieris
Series: Information Systems and Applications, incl. Internet/Web, and HCI, 12258
Publisher: Springer
Year: 2020
Language: English
Pages: 268
City: Cham
Preface
Organization
Contents
Introduction to Probabilistic Ontologies
1 Introduction
2 Ontologies
3 Uncertainty
3.1 Basics of Probability Theory
3.2 Conditional Probabilities
3.3 Boolean Random Variables and Joint Distributions
3.4 Bayesian Networks
4 Probabilistic Ontologies
5 The Five Golden Rules
5.1 Use Probabilities
5.2 Use the Right Probabilities
5.3 To Count or Not to Count
5.4 Understand the Numbers
5.5 Be Careful with Independence
6 A Specific Language
7 Conclusions
References
On the Complexity of Learning Description Logic Ontologies
1 Introduction
2 Description Logic
3 The Complexity of Learning
3.1 Model of Computation
3.2 Learning Frameworks and Queries
3.3 Learnability and Complexity Classes
4 Learning DL Ontologies
4.1 An Example
4.2 Complexity Results
5 Related Work
6 Conclusion
References
Explanation via Machine Arguing
1 Introduction
2 Background: Argumentation
3 Building Explainable Systems with Argumentative Foundations
3.1 Preprocessing
3.2 Extracting QBAFs
3.3 Explanations
3.4 Exercise
3.5 Solution
4 Extracting Argumentative Explanations from Existing AI Systems
4.1 Preprocessing
4.2 Extracting TFs
4.3 Explanations
4.4 Explanation Customisation
4.5 Feedback
4.6 Exercise
5 Conclusions
References
Stream Reasoning: From Theory to Practice
1 Introduction
2 Preliminaries
2.1 Continuous Queries
2.2 Resource Description Framework
2.3 SPARQL
3 Streaming Linked Data Life-Cycle
4 Processing
4.1 RSP-QL Primer
4.2 Putting RSP-QL into Practice
5 Exercise - Linear Pizza Oven
6 Conclusion
References
Temporal Ontology-Mediated Queries and First-Order Rewritability: A Short Course
1 Introduction
2 One-Dimensional Temporal OBDA
3 Ontology-Mediated Queries with LTL-Ontologies
4 OBDA with Temporalised DL-Lite
5 Ontology-Mediated Queries with MTL Ontologies
6 Future Research
References
An Introduction to Answer Set Programming and Some of Its Extensions
1 Introduction
2 History of ASP from a Logic Programming Perspective
3 ASP Language
3.1 Core ASP
3.2 Semantic Characterizations
3.3 Language Extensions
4 Semantics of Aggregates and Generalized Atoms
5 Knowledge Representation and Reasoning in ASP
6 Implementations and Applications
6.1 System algorithms
6.2 Applications
References
Declarative Data Analysis Using Limit Datalog Programs
1 Introduction
2 Syntax and Semantics of Positive DatalogZ
2.1 Syntax
2.2 Semantics
3 Positive Limit-Linear DatalogZ
3.1 Syntax and Semantics of Limit Programs
3.2 Undecidability and Limit-Linear Programs
3.3 Application Examples
4 Complexity of Positive LL-Programs
4.1 Pseudointerpretations
4.2 Upper Bounds for Positive Programs
4.3 Complexity Lower Bounds for LL-Programs
5 Fragments with Tractable Reasoning
5.1 Stable Positive LL-Programs
5.2 Type-Consistent Programs
6 Related Work
7 Conclusion
References
Knowledge Graphs: Research Directions
1 Introduction
2 Data Model
3 Queries
4 Ontologies
5 Rules
6 Context
7 Embeddings
8 Graph Neural Networks
9 Conclusions
References
Author Index
