Reasoning Web. Declarative Artificial Intelligence: 16th International Summer School 2020, Oslo, Norway, June 24–26, 2020, Tutorial Lectures 9783030600662, 9783030600679

Table of contents :
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