didattica:ay2122:ke:main

Knowledge Engineering

News

Dear students,
If you do not want to accept your grade in Knowledge Engineering, you can improve the project and add a graphical modeling language for meals using ADOxx. Holger and I will offer a coaching session about this improvement on

In the beginning we will provide general information about what we expect. Then we will make individual coachings with each team.

Best regards, Holger and Knut

Dear students,
Please share your credentials with us: Google Form. We would be happy to have you matriculation number and your e-mail addresses because typing your grades into the ESS3 system gets much easier. Also we might need to inform you about something via e-mail sometimes.
Best regards
Holger

Dear students,
we welcome you to our lecture. Looking forward to inspire you with “Knowledge Engineering”.
Best regards
Knut and Holger

General Info

Teacher:

ESSE3 Link

Scheduling of Lectures:

  • We teach block-wise, i.e. not every week. Please refer to our schedule.
  • In general scheduling is available at the following link (WARNING: The English page seems to be outdated)

Degrees:

Course Objectives

Supporting Knowledge-Intensive Processes

Knowledge-intensive processes are more unstructured processes with a lot of involvements of users with their experience. Supporting such processes at their levels requires modelling and enacting several different forms of knowledge. In general more explicit represented knowledge allows better support. But different forms of knowledge need different intuitive and adequate representations and inferences.

There are several moethods to formally represent knowledge. Graphical models can be an intuitive means to add knowledge into knowledge-based systems. Instead of manually creating knowledge bases, knowledge can also be learned from data. We distinguish between symbolic learning (learning decision trees andss case-based reasoning) and sub-symbolic learning (neural networks)

After completion of this module, the participants will be able to assess which kind of knowledge representation and reasoning is adequate and are able to develop appropriate knowledge-based systems. They can value the advantages of knowledge-based systems with respect to their costs and apply several methods to create knowledge bases.

Course Contents

  • Introduction: Knowledge in processes
  • Decision Tables
  • Rules
    • Textual represented rule (i.e. Horn clauses)
    • Forward and backward chaining
    • Data-driven and Goal-oriented
    • Negation-as-failure
  • Object-centred Systems
    • F-Logic/Objectlogic
  • Fuzzy Logic
  • Human-interpretation vs. machine-interpretation
    • Graphical modeling
    • Ontology-based modeling
  • Machine Learning: Learning Decision Trees
  • Case-Based Reasoning
  • Neuronal Networks

Study material

Course Material

Recordings

Recordings of the lectures are password protected (passwords on request from the lecturers)

Exams

This module is examined by a project. The current version of the project description is V1 (25.04.2022).

  • didattica/ay2122/ke/main.txt
  • Last modified: 2022/08/17 14:22
  • by knut