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Knowledge Engineering and Business Intelligence
News
14th of November 2024
The third submission date for the assignment is the 24th of November 2024. Please send the report and knowledge bases via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)
Best regards, Knut
General Info
Teacher:
- Dr Emanuele Laurenzi
ESSE3 Link
Webex Link * https://unicam.webex.com/meet/knut.hinkelmann
Scheduling of Lectures:
Degrees:
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
- RDFS
- Ontology Engineering
- Graphical 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”
- Slides: Example: Admission as a Decision-aware Business Process
- Lecture “Decision Tables”
- Slides: Decision Tables - DMN
- Reading: Introduction into DMN
- Exercise: Reduction of Decision Table, Sample Table
- Exercise: DMN for Booking Price
- Homework: Decision Modeling for Admission
- Tools:
- Download: Camunda Workflow and Decision Modeler
- Online: Camunda Decision Simulator
- Lecture “Rule-based Systems”
- Slides: Rule-based Systems (Prolog) (with hidden information)
- Exercise: University and Solution
- Exercise: Further small examples and Solutions
- Exercise: Mini Sudoku and Solution
- Exercise: Travelling and Solution
- Exercise: Friendship
- Additional exercise (not discussed in class): Fraud Detection and Solution
- Home Work: Admission for Master Program and Solution
- Nice browser-based Prolog Engine
- Lecture “Forward- and Backward Chaining”
- Slides: Forward- and Backward Chaining
- Lecture “Knowledge Graphs”
- Download and launch GraphDB: https://www.ontotext.com/products/graphdb/
- Slides: Knowledge Graphs
- Exercise: Class exercises and solutions
- Slides: Knowledge Graphs_Part2
- Exercise: File family tree with schema
- Homework: Homework on RDF(S) reasoning
- Lecture “Ontology Engineering”
- Download and install Protégé: https://protege.stanford.edu/
- Slides: Ontology Engineering
- Homework: Ontology Engineering on Teaching Domain
- Lecture “Machine Reasoning”
- Slides: Machine Reasoning
- Exercise: File family tree with contradiction
-
- Solution homework: SHACL solution against a person with more than 2 parents
- Solution homework: Ontology file with the contradiction
- Lecture “Convergence of Ontologies/Knowledge Graphs and Enterprise Models”
- Slides: Convergence of KG and EM
- Slides: Semantic Lifting
- Exercise: Enterprise models created in BeeUp
- Walkthrough 1: Walthrough on ontology-based modelling
- Walkthrough 2: Walkthrough on agile meta-modelling
- Solution Exercise: SPARQL query result
- Lecture “Fuzzy logic”
- Slides: Fuzzy Logic
- Exercise: Fuzzy Sets and Solution
- Exercise: Fuzzy Set Operations and Solution
- Exercise: Credit Analysis
- Homework: Admission for Master Program
- 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”
- Assignment with Solution: Health Insurance: Combining Learning with Knowledge Engineering
Recordings
Recordings of the lectures are password protected (passwords on request from the lecturers)
Exam: Project
The grading is done via a project work
Coaching Sessions:
- Link to the coaching sessions Webex
There will be several submission deadlines
- First Submission: 1st of July 2024 via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)
- Second Submission: 21st of August 2024 via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)
- Third Submission: 24th of November 2024 via email to Emanuele (emanuele.laurenzi@unicam.it) and Knut (karlknut.hinkelmann@unicam.it)