Knowledge Representation for the Semantic Web

Advanced lecture, 6 ECTS credits, winter semester 2017/2018

Organization

Lecturer:  Daria Stepanova

Lectures: E1 3, 0.14, Thursday, 14:00 - 16:00

Tutorials: E1 4, Rotunda room (dates are to be announced)

Registration

  • Participation on the 1st lecture on 19.10.2017 is mandatory
  • Registration: send an email titled "Registration for KRSW" to dstepano@mpi-inf.mpg.de by 23.10.207 with the following details:
    • Name, surname
    • Matriculation number
    • Semester
    • Related courses taken

Course description

Semantic Web is a maturing field of technology that continues to be the emphasis of much focused research and industrial investigation. Its central idea is to add meaning (semantics) to the data on the Web thus making it machine processable. In this course we cover the standardized knowledge representation languages for enriching the data with meaning. More specifically, on the theoretical side we will study the syntax and semantics of the main ontology and rule-based languages. On the practical side we will exploit the available tools for the knowledge representation and reasoning.

Prerequisites

The basic knowledge of first order logic is highly recommended.

Tentative agenda

1. Introduction and motivation
- organization
- digital knowledge
- knowledge graphs
- semantic web architecture
- reasoning on the web


2. Description Logics 1
- introduction and motivation
- syntax
- semantics


Assignment 1


3. Description Logics 2
- reasoning tasks and their complexity
- modeling
- DLs and OWL
- tools and applications

 

Project 1

 

4. Answer Set Programming 1
- introduction and motivation
- Horn logic programming
- negation in logic programs
- answer set semantics


Assignment 2

 

5. Answer Set Programming 2
- guess and check methodology
- modeling
- tools and applications

 

Project 2

 

6. Combination of ASP and DLs
- difference between ASP and DLs
- inconsistency handling
- other extensions


7. Knowledge Mining
- introduction and motivation
- relational association rule mining
- exceptions handling
- completeness-aware rule mining
- other topics

 

8. Project presentations


9. Final exam