After successful completion of the course, students are able to...

- model knowledge from diverse domains in an adequately chosen DL and formalize inference problems arising in those areas as reasoning services, explaining the advantages and disadvantages of different choices by arguing about the complexity of reasoning, expressiveness features, model-theoretic properties, and the availability of reasoning tools;

- find existing ontologies and reasoning tools, and assuming the availability of suitable documentation, judge the adequacy of different tools for providing a requested reasoning service over a given ontology; and

- read and understand introductory texts on current DL research trends, and formulate clearly a basic explanation of selected problems being studied currently by the DL research community.

The course will provide the theoretical foundations of Description Logics (DLs) as well as basic skills for using DL ontologies in Information systems. It will cover both theory and practice, and give an overview of current research in the field.

Part 1: Knowledge representation using DLs

• DL basics, knowledge bases
• Reasoning services 
• DLs as a toolbox: constructors and expressiveness  

Part 2: Foundations of DLs 

• Reasoning algorithms 
• Complexity of reasoning 

 Part 3: Lightweight DLs and applications of DL ontolgies

(a) The EL family

• Consequence-driven reasoning and EL reasoners
• Biomedical and life sicences ontology repositories

(b) The DL-Lite family 

• The DL-Lite family and its applications in data magement
• Reasoning in DL-Lite

(c) Ontologies for data management 

• Query answering in DLs
• OBDA systems

(d) Using DL ontologies

• The OWL languages
• Existing reasoners and their underlying algorithms

Part 4: Current research trends

• Other non-standard reasoning problems
• Extensions of DLs
• Selected topics of research in DLs 

The course consists mainly of lectures, where the material will be presented orally and on the blackboard. Additional learning material in the form of slides and textbook chapters will be provided.

Students will solve exercises at home, based on the materials in the lectures. 

The course also includes a few reading assignments and short presentations.

ECTS breakdown:

Lectures: 18 hours (9 lectures of 2 hours each) 

Exercises: 33 hours (3 exercise sheets,  each 10 h house work + 1 h discussion)

Small projects:  22.5 hours (3 small projects, each 7 hours work + 0.5 hours presentation)

Final exam (optional): 1.5 hours 

 ----  Total: 75 hours

The students will solve homework problems where they model knowledge in different DLs, distinguish different reasoning services, apply different algorithms to these services, and analyze key computational properties of the different algorithms. 

The exercise solutions will be presented to the class by the students in the exercise sessions. 

The students will research selected topics and present them to the class.

Basic knowledge in these areas  is an advantage, but not a requirement: logic, thoery of databases, complexity theory, foundations of semantic web, knowledge representation and reasoning.

By having a broad selection of exercises and reading topics, the course is tailored to accomodate for both people with a more theory-oriented interestinterested in DLs as computational logics, and people with interest in the practical use of ontologies, who want to properly understand the foundations of modeling and reasoning with them.