didattica:ay2425:kebi:main

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Knowledge Engineering

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General Info

Teacher:

ESSE3 Link

Webex Link:

Scheduling of Lectures:

Degrees:

Exam: Project

The grading is done via a project work

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)

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

Study material

Course Material

  • didattica/ay2425/kebi/main.1742124273.txt.gz
  • Last modified: 2025/03/16 12:24
  • by e.laurenzi