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Knowledge Engineering
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General Info
Teacher:
ESSE3 Link
Webex Link:
Scheduling of Lectures:
- Scheduling is available at the following link
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
- 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
- Lecture “Knowledge in 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
- Lecture “Rule-based Systems”
- Slides: Rule-based Systems (Prolog)
- Exercise: University (with Solution)
- Exercise: Further small examples (with Solution)
- Exercise: Travelling (with Solution)
- Exercise: Friendship (with Solution)
- Home Work: Admission for Master Program
- Nice browser-based Prolog Engine
- Lecture “Forward- and Backward Chaining”
- Slides: Forward- and Backward Chaining
- Lecture “Fuzzy logic”
- Slides: Fuzzy Logic
- Exercise: Fuzzy Sets (with Solution)
- Exercise: Fuzzy Set Operations (with Solution)
- Lecture “Machine Learning”
- Slides: Introduction to Machine Learning
- Reading Material: Decision Tree Learning
- Exercise: Auto Traders
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- Lecture “Combining Machine Learning and Knowledge Engineering”
- Lecture “Knowledge Graphs”
- Download: Graph Database GraphDB
- Class exercise: Querying the KG family tree trhough SPARQL
- Class exercise: Reasoning with RDF(S)
- Optional exercise: RDF Graphs and Solution
- Optional exercise: RDF Schema and Solution
- Optional exercise: RDF Schema Inferences and Solution
- Lecture “Ontology Engineering”
- Slides: Ontology Engineering
- Class exercise Develop an ontology for Courses and Lecturers and A solution
- Literature: Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory Technical Report KSL-01-05.
- Solution: A solution MSc BIS ontology
- Lecture “Machine Reasoning”
- Slides: Machine Reasoning
- Class hands-on: Family tree TTL file for hands-on with CONSTRUCT SPARQL
- Class exercise Protégé File: Family Tree with Contradictions and possible solutions
- Class exercise Exercise and solution and the SHACL constraint "no more than 2 parents"
Recordings
Recordings of the lectures are password protected (passwords on request from the lecturers)