Course: 707.009 Technological Foundations of Knowledge Management

"Knowledge Acquisition and Organisation"

(707.009 Technologische Grundlagen des Wissensmanagements)

Graz University of Technology Fall/Winter 2011/12

Class Schedule: Oct - Nov 2011, Room HSi3
Markus Strohmaier


Instructor: Markus Strohmaier
Adress: Inffeldgasse 21a, 2nd floor, Room IMO2152, 8010 Graz, Austria
e-mail: markus.strohmaier at@ (remove spaces, replace at@ with @), please start subject line with [707.009]

Students with special needs: If you need accomodation for any type of physical or learning disability, please contact me via e-mail to set up a meeting where we can discuss potential modifications for your participation.


About the course:

This course aims to give students a basic understanding about the fundamental principles, concepts and challenges underlying knowledge management (KM). At the end of this course, students will have a thorough theoretical understanding of these issues, and the ability to relate and apply KM techniques and methods in the light of simple examples. Selected examples are used to illustrate the utility of knowledge management approaches in specific situations, but also to highlight current gaps between KM theory and practice.


Students need to take an exam. For more information on taking the exam, please read the according policy at the end of this page.

Preliminary course schedule and weekly readings:

Note to students: This schedule is preliminary, changes will likely be made. Additional/other readings may be assigned. Access credentials for protected resources will be handed out in class.

Note to instructors: All teaching materials on this website are available for use under a Creative Commons Attribution-Noncommercial-Share Alike 2.0 Austria License, except for cited material / where noted otherwise. Access to protected areas is only available to enrolled students.


Title, Slides Comments and Links
(HS i3, 12:15)

Overview and Motivation


In this class, we will discuss the course organization and give a basic motivation for and introduction to the course.

(HS i3, 12:15)

Knowledge Acquisition I


What is knowledge? What forms of knowledge can we identify? We will discuss some basic distinctions and characterizations.

Readings: D. Kirsh, When is information explicitly represented?, Information, Language and Cognition - The Vancouver Studies in Cognitive Science.: 340--365, 1990. [Protected Access]

10.10.2011 (HS i3, 12:15)

Knowledge Organization


How can knowledge be organized? We will discuss some basic principles of knowledge organization, such as categorization, taxonomies, ontologies and concept systems.

Readings: C.B. Mervis and E. Rosch, Categorization of Natural Objects, Annual Review of Psychology 32 89--115, 1981 [Protected Access]

12.10.2011 (HS i3, 12:15)

Categorization & Formal Concept Analysis


How can categorization be formalized?
Readings: Chapters 1 - 2.3.2, Formal Concept Analysis: Methods and Applications in Computer Science, Bernhard Ganter, Adapted and extended by Gerd Stumme, Summer 2003


(HS i3, 12:15)

Broad Knowledge Bases


What kinds of broad knowledge bases exist? We will discuss different forms of knowledge bases and representations, such as metadata, wordnet, framenet, cyc, openmind and others.

Readings: T. Berners-Lee and J. Hendler and O. Lassila, The semantic Web, Scientific American, 284 (5) 2001.

20.10.2011 (HS i3, 11:15)

Participative Knowledge Acquisition Methods


How can knowledge be acquired from users in a way that makes knowledge amenable to computation and/or analysis?

Readings: L. von Ahn, Games with a Purpose, Computer, 39(6): 92--94, 2006


24.10.2011 (HS i3, 12:15)

Latent Semantic Analysis

(slides1, slides 2)

Latent Semantic Analysis

Readings: An Introduction to Latent Semantic Analysis:


(HS i3, 14:15)

Probabilistic Topic Models


Topic Modeling

Matlab Toolbox Weblink

Readings: Probabilistic Topic Models SteyversGriffithsLSABookFormatted.pdf
Optional: Topics in Semantic Representation topicsreview.pdf

(HS i3, 13:15)

Inductive Concept Learning and ILP


Introduction to Inductive Concept Learning and ILP.

Readings: Nada Lavrac and Saso Dzeroski: Inductive Logic Programming: Theory and Applications: (Chapters 1.,2. and 3.2: Bookwebsite + pdf)



7.11.2011 (HS i3, 10:15)

Multimedia & Semantic Metadata


In this class, we will discuss different forms of semantic annotation of multimedia documents.

Guest Lecture: Mathias Lux

9.11.2011 (HS i3,

Association Rule Learning


Readings: Data Mining: Concepts and Techniques (Chapters 5.1 - 5.2.4, protected access)

An introduction to association rules and association rule learning.


(HS i3,


Evaluation Strategies and Methods



In this lecture, we will talk about case studies involving the evaluation of knowledge bases.

Guest Lecture: Christian Körner




(HS i7, 14:00)

Final Exam

The final exam will take place in HSi7, 14:00 - 16:00. All you need to bring is a pen and your student ID.


Policy for Exams:

There are two different ways of obtaining a grade for this course:

  1. There will be one written exam at the *middle* of each semester (fall/winter and summer). Examination dates for written exams will be announced via TUGonline. There is no limit on the number of students that can take the written exam.
  2. In addition to written exams, oral exams will be offered. In case you want to take an oral exam, please contact me at least 4 weeks ahead of time to arrange for a date. Please note that there is limited availability for oral exams, and they are available on a request / first come first serve basis only.

To pass either exam (written/oral), you need to have in-depth knowledge about the entire course contents (all lectures including guest lectures, see slides) and the accompanying literature (see papers).

This policy is preliminary. Changes will likely be made.