didattica:ay2223:kebi:main

Knowledge Engineering and Business Intelligence

News

Welcome to the lecture in Knowledge Engineering!

We are happy that you plan to participate in our amazing module. However, we would be happy to know who you are. :-) Therefore, please fill the following google with your name, matriculation number and Unicam e-mail. That would allow us to inform you if something is changed (e.g. lecture needs to be postponed, other conference tool etc) and to register your grade in the system.

https://forms.gle/33PZNW31dNc3qHay6

We also have an schedule which you can find here.

Best regards, Holger & Knut

General Info

Teacher:

ESSE3 Link

Scheduling of Lectures:

  • We teach block-wise, i.e. not every week. Please refer to our schedule

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
  • Fuzzy Logic
  • Knowledge Graphs
    • RDFS
    • Ontology Engineering
  • Graphical Models
    • Modelling and Meta-modeling
    • Ontology-based meta-modeling
  • Machine Learning
    • Learning Decision Trees

Study material

Course Material

Recordings

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

Exams

  • didattica/ay2223/kebi/main.txt
  • Last modified: 2023/03/21 18:55
  • by holger