Knowledge Engineering

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Dear students,
Please download and install the Protege ontology editor before the lecture on Monday, 9th of May.

The lecture on Monday 9th of May, will be online https://unicam.webex.com/meet/knut.hinkelmann
Best regards
Knut

Dear students,
Please share your credentials with us: Google Form. We would be happy to have you matriculation number and your e-mail addresses because typing your grades into the ESS3 system gets much easier. Also we might need to inform you about something via e-mail sometimes.
Best regards
Holger

Dear students,
we welcome you to our lecture. Looking forward to inspire you with “Knowledge Engineering”.
Best regards
Knut and Holger

Teacher:

ESSE3 Link

Knowledge Engineering - AY 2021/22

Scheduling of Lectures:

We teach block-wise, i.e. not every week. Please refer to our schedule.
In general scheduling is available at the following link (WARNING: The English page seems to be outdated)

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

There are several moethods to formally represent knowledge. Graphical models can be an intuitive means to add knowledge into knowledge-based systems. Instead of manually creating knowledge bases, knowledge can also be learned from data. We distinguish between symbolic learning (learning decision trees andss 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
Object-centred Systems
- F-Logic/Objectlogic
Fuzzy Logic
Human-interpretation vs. machine-interpretation
- Graphical modeling
- Ontology-based modeling
Machine Learning: Learning Decision Trees
Case-Based Reasoning
Neuronal Networks

Course Material

Organisation

Lecture “Introduction”
Lecture “Knowledge in Processes”
- Slides: Decision-Aware Business Processes
- Slides: Example: Admission as a Decision-aware Business Process
Lecture “Decision Tables”
- Slides: Decision Tables - DMN
  - Exercise: DMN for Booking Price
  - Example: Price Calculation for Room Booking
- Reading: Introduction into DMN
- Exercise: Reduction of Decision Tables, Sample Table
- Exercise: Decision Modeling for Admission
- Tools:
  - Download: Camunda Workflow and Decision Modeler
  - Online: Camunda Decision Simulator

Lecture “Rule-based Systems”
- Slides: Rule-based Systems
- Reasoning examples: simple and ancestor
- Exercise: University and Solution
- Exercise: Family and Solution
- Exercise: Further small examples and Solution
- Exercise: Mini Sudoku and Solution
- Exercise: Friendship and Solution
- Home Work with Solution
- Nice browser-based Prolog Engine

Lecture “Forward- and Backward Chaining”
- Slides: Forward- and Backward Chaining

Lecture “Fuzzy logic”
- Slides: Fuzzy Logic
- Exercise: Fuzzy Sets and Solution
- Exercise: Fuzzy Set Operations and Solution
- Exercise: Credit Analysis
- Jamboard as PDF
- Home Work and Solution

Lecture “Knowledge Nets and RDF”
- Slides: Knowledge Nets and RDF
- Exercise: RDF Graphs and Solution
- Exercise: RDF Schema and Solution
- Exercise: RDF Schema Inferences and Solution