Applications of Knowledge-Based Techniques
Module IN2058
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2012
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
| available module versions | |
|---|---|
| SS 2015 | SS 2012 |
Basic Information
IN2058 is a semester module in German or English language at Bachelor’s level and Master’s level which is offered irregularly.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
| Total workload | Contact hours | Credits (ECTS) |
|---|---|---|
| 150 h | 60 h | 5 CP |
Content, Learning Outcome and Preconditions
Content
The lecture course teaches state-of-the-art techniques for implementing knowledge-based systems and applying them to real problems, in particular in the contexts of the World Wide Web and controlling intelligent systems. The lecture course covers the following topics:
- description logics: representation of taxonomic knowledge and reasoning about it;
- principles of the Semantic Web: Semantic Markup Languages, Inference mechanisms and engines for the Semantic Web;
- information systems in the WWW: data integration from distributed information sources; wrappers and wrapper learning; planning mechanisms for data integration; global-as-view and local-as-view abstractions;
- Semantic Web Services and planning complex services;
- selected examples of intelligent web services;
- grounded knowledge representation: principles of data mining and the integration of data mining mechanisms into knowledge-based systems
- description logics: representation of taxonomic knowledge and reasoning about it;
- principles of the Semantic Web: Semantic Markup Languages, Inference mechanisms and engines for the Semantic Web;
- information systems in the WWW: data integration from distributed information sources; wrappers and wrapper learning; planning mechanisms for data integration; global-as-view and local-as-view abstractions;
- Semantic Web Services and planning complex services;
- selected examples of intelligent web services;
- grounded knowledge representation: principles of data mining and the integration of data mining mechanisms into knowledge-based systems
Learning Outcome
The participants attain detailed knowledge about current techniques for designing and realizing knowledge-based systems and their applications to real-world problems, especially in the context of the world-wide web and the control of intelligent software systems. In the accompanying exercises, participants will gain knowledge about the design and realization of central components of such systems.
Preconditions
IN2062 Techniques in Artificial Intelligence, basic courses in informatics
Courses, Learning and Teaching Methods and Literature
Learning and Teaching Methods
The module consists of lectures, exercise courses and problems for individual study. In the lectures, the material is presented by the teacher, in dialogue with the students. During the exercise courses, the students work on given exercises either individually or in small groups with help from the tutors and discuss the solutions to problems for individual studies.
Media
Slides, videos, homework assignments
Literature
Stuart Russell and Peter Norvig: Artificial Intelligence - A Modern Approach, Prentice Hall.
Ben Taskar and Lise Getoor: Introduction to Statistical Relational Learning, The MIT Press.
Ben Taskar and Lise Getoor: Introduction to Statistical Relational Learning, The MIT Press.
Module Exam
Description of exams and course work
The exam takes the form of a 75 minutes written test. In the written exam students should prove their ability to choose and utilize adequate knowledge-based systems and their ability to design central components of such systems for a given task.
Exam Repetition
The exam may be repeated at the end of the semester.