Knowledge Engineering and Business Intelligence

3rd of June

Dear students

We will offer online coaching sessions on 20th and 21st of June.

Best regards, Knut

I was made aware that 2nd of April is a holiday and the university is closed. Therefore, I had to change the schedule of the lectures.

The lectures from 2nd and 15th of April will be moved to 29th and 30th of May

Here is the link to the new schedule: Schedule of KEBI lectures

Best regards, Knut

Welcome to the lecture in Knowledge Engineering!

We are happy that you plan to participate in our module. However, we would be happy to know who you are. :-) Therefore, please fill the following google form 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.


Best regards, Emanuele & Knut

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 modeling and enacting several different forms of knowledge. In general, more explicitly represented knowledge allows better support. But different forms of knowledge need different intuitive and adequate representations and inferences.

There are several methods to formally represent knowledge. Graphical models can be an intuitive means to add knowledge to knowledge-based systems. Instead of manually creating knowledge bases, knowledge can also be learned from data. We distinguish between symbolic learning (learning decision trees and 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.

  • 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

Course Material

The grading is done via a project work

Coaching Sessions:

There will be several submission deadlines

  • First Submission: 1st of July 2024 via email to Emanuele and Knut (karlknut.hinkelmann@unicam.it)
  • didattica/ay2324/kebi/main.txt
  • Last modified: 2024/06/03 08:20
  • by knut