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;

- be familiar with the main DL reasoning tools, and assuming the availability of suitable documentation, judge the adequacy of different tools for providing a requested reasoning service over ontologies in different DLs; 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) 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.

(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: 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 

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

Lectures: oral presentation using blackboard and slides

Exercises: the students solve individual assignments. Selected solutions are discussed
in class.

Projects: the students must carry out a few small reseach assignments on concrete topics (such as investigating an specific reasoner or a non-cassical reasoning problem) and present their conclusions in the lectures.

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