At the end of the course, the students will be 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 specific learning objectivesare:

(1) The students will name the main description logics, list their distinguishing
features, and write simple knowledge bases modeling different domains in them.

(2) The student will formalize data management and artificial intelligence problems in
different application domains, as standard and non-standard reasoning services in DLs.

(3) The student will classify main reasoning problems and DLs according to their
computational complexity, and explain selected algorithms for solving these reasoning
problems in different DLs.

(4) The student will be capable of locating existing ontologies for selected application
domains. Given an ontology with a suitable description, the student will understand the
description, match the ontology to a DL with the necessary expressiveness and minimal
complexity, and judge the quality of the ontology and criticize its modeling principles.

(5) Given a DL and a reasoning task, the student will be able to locate an existing
reasoner. Given a basic description of the algorithms the reasoner implements, the
student will argue about its adequacy for the task.

(6) The student will explain, at a high-level but clearly, some selected problems that
are receiving attention in the DL research community, and provide examples illustrating
them. The student will have the ability to read recent research papers
and understand their core contributions.

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: Applications of DL ontolgies (covering also reasoning tools)

(a) DLs on the Web

• The OWL languages
• Ontologies in the Semantic Web
• OWL reasoners and their underlying algorithms

(b) DLs for life sciences

• Biomedical and life sicences ontology repositories
• The EL profile, reasoning in EL

(c) Ontology Based data management 

• The DL-Lite family
• Basics of query rewriting and query answering
• OBDA systems

Part 4: Current research trends

• Selected topics of research in DLs 

ECTS breakdown:

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

Exercises: 30 hours (4 exercise sheets,  each 6 h house work + 1.5 h discussion)

Final project:  25 hours (20 hours work + 5 hours presentation)

Final exam (optional): 2 hours 

 ----  Total: 75 hours

Lectures: oral presentation using blackboard and slides
Exercises: the students solve individual assignments. Selected solutions are discussed
in class.
Project: the students carry out a final project and present their results.
There are two types of projects:
Practical project: the students use existing tools for solving a specific problem
Research project: the students study recent research papers on a chosen topic.

Examination: grades will be assigned based on the assignments and projects. Students
will have the opportunity of taking an optional oral exam to improve their grade.