Knowledge Engineering

Teacher:

• Prof. Knut Hinkelmann
• Prof. Emanuele Laurenzi

ESSE3 Link

• Knowledge Engineering - AY 2024/25

Webex Link:

• https://unicam.webex.com/meet/knut.hinkelmann
• https://unicam.webex.com/meet/emanuele.laurenzi

Scheduling of Lectures:

• Scheduling is available at the following link
• Schedule of KEBI Lectures

Degrees:

• MSc in Computer Science (LM-18)

The grading is done via a project work

• Project Description
• Enter your name

There will be several submission deadlines

• First Submission: 1st of July 2025 via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)
• Second Submission: 21st of August 2025 via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)
• Third Submission: 24th of November 2025 via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)

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
  • RDF, RDFS, SWRL, SHACL
  • Ontology Engineering
• Graphical Ontology-based Models
  • Modelling and Meta-modeling
  • Ontology-based meta-modeling

Course Material

• Organisation
  • Slides: About Lecture and Lecturers

• Project work:
  • Project description: Project description
  • Guide on how to install AOAME locally for Task 2: https://github.com/BPaaSModelling/AOAME

• Lecture “Introduction”
  • Slides: Introduction
  • Davenport, T. H. (2010). Process Management for Knowledge Work. In J. vom Brocke & M. Rosemann (Eds.), Handbook on Business Process Management 1 (pp. 17–36). Berlin, Heidelberg: Springer.
  • Exercise/Homework: Types of Knowledge

• Lecture “Knowledge in Processes”
  • Slides: Decision-Aware Business Processes
  • Example: Example Decision-Aware Process Model

• Lecture “Decision Tables”
  • Slides: Decision Tables - DMN
  • Reading: Introduction into DMN
  • Exercise: Reduction of Decision Table, Sample Table
  • Exercise: DMN for Booking Price
  • Homework: exercise_decision_modeling_admission.pdf |Decision Modeling for Admission}}
  • Tool:
    • Sign up online with your Unicam student account: Trisotech

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

• 17th of March 2025: Introduction: What is knowledge
• 18th of March 2025: Knowledge in Processes, Decision Modeling, Decision Tables