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 distinguishingfeatures, and write simple knowledge bases modeling different domains in them.(2) The student will formalize data management and artificial intelligence problems indifferent application domains, as standard and non-standard reasoning services in DLs.(3) The student will classify main reasoning problems and DLs according to theircomputational complexity, and explain selected algorithms for solving these reasoningproblems in different DLs.(4) The student will be capable of locating existing ontologies for selected applicationdomains. Given an ontology with a suitable description, the student will understand thedescription, match the ontology to a DL with the necessary expressiveness and minimalcomplexity, 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 existingreasoner. Given a basic description of the algorithms the reasoner implements, thestudent will argue about its adequacy for the task.(6) The student will explain, at a high-level but clearly, some selected problems thatare receiving attention in the DL research community, and provide examples illustratingthem. The student will have the ability to read recent research papersand 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
Part 2: Foundations of DLs
Part 3: Applications of DL ontolgies (covering also reasoning tools)
(a) DLs on the Web
(b) DLs for life sciences
(c) Ontology Based data management
Part 4: Current research trends
For the SS 2018, the introduction and first lecture will take place on 17.04, 8:45 am.
The lectures will take place in the time 8:30 -10:30 (sharp).
The course will be held in blocked form,; see individual appointments.
ECTS breakdown:
Lectures: 16 hours (8 lectures of 2 hours each)
Exercises: 32 hours (4 exercise sheets, each 6 h house work + 2 h discussion)
Small research assignments: 27 hours (3 x 9 hours)
---- Total: 75 hours
Lectures: oral presentation using blackboard and slides
Exercises: the students solve individual assignments. Selected solutions are discussedin class.
Small projects: the students do three small research and reading assignments on selected topics (of their own choice), and share their findings with the group via a short presentation. Examination: grades will be assigned based on the assignments and projects. Studentswill have the opportunity of taking an optional oral exam to improve their grade.
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.