Knowledge Engineering and Business Intelligence

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.

https://forms.gle/2Kt1HXC7QAp958VNA

Best regards, Emanuele & Knut

Teacher:

Prof. Knut Hinkelmann
Dr Emanuele Laurenzi

ESSE3 Link

Knowledge Engineering and Business Intelligence - AY 2023/24

Webex Link * https://unicam.webex.com/meet/knut.hinkelmann

Scheduling of Lectures:

Degrees:

MSc in Computer Science (LM-18)

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.

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