Foundations of Artificial Intelligence IV
4 weeks à 3 hours
Record of Achievement
For free


The course "Foundations of Artificial Intelligence IV" introduces into the representation of conceptual knowledge using ontologies. The course explains the theoretical foundations of modern ontological languages such as OWL that are based on description logics developed within AI.

The course discusses different types of knowledge and motivates the need to represent conceptual knowledge in an AI system. It touches upon the history of knowledge representation in AI and its roots in philosophical logic. Description logics as well as ABox and TBox reasoning are introduced. The description logic ALC is discussed in detail as a foundational language for many different ontological representations. The course proceeds with an overview of web ontologies, the semantic web, and the OWL standard. A discussion of challenges in knowledge representation relating to non-monotonic reasoning and dealing with exceptions concludes the course. 

The course is based on the textbook by Stuart Russell and Peter Norvig: Introduction to Artificial Intelligence - A Modern Approach, 3rd edition, 2012, or 4th edition 2020. The 3rd edition is also available in German: Stuart Russell und Peter Norvig: Künstliche Intelligenz - Ein Moderner Ansatz, 3. aktualisierte Auflage, Pearson 2012. The course also uses additional material from the books R. Brachman, H. Levesque: Knowledge Representation & Reasoning, Morgan Kaufmann, 2004 and F. Baader, D. Calvanese, D. McGuinness, D. Nardi, P. Patel-Schneider: The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press, 2nd edition, 2007.

Which topics will be covered?

Module Representation of Conceptual Knowledge

  • Introduction
    • Types of knowledge
    • Ontologies and taxonomies
    • Frames and semantic nets
  • Description logic systems
    • Relation with first-order predicate logic
    • TBox and ABox representations
  • Description logic ALC
    • Syntax and Semantics
    • Complex concept descriptions
    • Interpretation of concepts and roles
    • Expressivity and decidability
  • Reasoning services in description logics
    • General concept inclusion and subsumption
    • Combined TBox/ABox reasoning
    • Reasoning as satisfiability checking
    • Reasoning with structural rules

Module Web ontologies and the Semantic Web

  • Introduction to the Semantic Web
    • The vision and architecture of the semantic web
    • RDF and RDF Schema
    • The Ontology web language family OWL
    • The Ontology editor Protegé
  • Querying the semantic web with SPARQL
    • Subgraph Matching
    • Query Pattern
    • Query Federation

Module Challenges in Knowledge Representation

  • The Frame problem in AI
  • Non-monotonic reasoning and belief revision
  • Dealing with exceptions and default assumptions in knowledge bases
  • Open research problems in knowledge representation


What will I achieve?

By the end of the course, you‘ll be able to

  • distinguish different types of knowledge,
  • understand the foundations of conceptual knowledge representation, the semantic web and knowledge graphs,
  • discuss reasoning services needed to derive implicit knowledge,
  • understand the basics of the ontology query language SPARQL,
  • assess limitations of currently ontology representations when dealing with exceptions and non-monotonic revisions.


Which prerequisites do I need to fulfill?

A solid understanding of first-order predicate logic is required to fully understand the theoretical material presented in this course. The course III in this course series on Logic and Satisfiability provides these foundations. Participants, who are mostly interested in learning about the basics of ontologies and for what they can be used in practice, can follow the main ideas presented in this course and are recommended to concentrate on the practical illustrating examples.

This course is offered by
Jana Koehler

Prof. Dr. Jana Koehler

Deutsches Forschungszentrum für Künstliche Intelligenz
Universität des Saarlandes
Artificial Intelligence Group / Saarland University
Course information
Learning format:
Online course
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Fundamental methods of AI