Show pageOld revisionsBacklinksBack to top This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong. ====== Knowledge Engineering and Business Intelligence ====== ---- ===== News ===== <WRAP box round 95% center> I was made aware that 2nd of April is a holiday and the university is closed. Therefore, I had to change the schedule of the lectures. **The lectures from 2nd and 15th of April will be moved to 29th and 30th of May** Here is the link to the new schedule: {{ :didattica:ay2324:kebi:2024_kebi_weekschedule.pdf | Schedule of KEBI lectures}} Best regards, Knut </WRAP> <WRAP box round 95% center> Welcome to the lecture in Knowledge Engineering! We are happy that you plan to participate in our module. However, we would be happy to know who you are. :-) Therefore, please fill the following google form with your name, matriculation number and Unicam e-mail. That would allow us to inform you if something is changed (e.g. lecture needs to be postponed, other conference tool etc) and to register your grade in the system. [[https://forms.gle/2Kt1HXC7QAp958VNA]] Best regards, Emanuele & Knut </WRAP> ---- ===== General Info ===== <WRAP box round 95% center> **Teacher**: * [[https://knut.hinkelmann.ch|Prof. Knut Hinkelmann]] * Dr Emanuele Laurenzi **ESSE3 Link** * [[|Knowledge Engineering and Business Intelligence - AY 2023/24]] **Webex Link** * [[https://unicam.webex.com/meet/knut.hinkelmann]] **Scheduling of Lectures**: * {{ :didattica:ay2324:kebi:2024_kebi_weekschedule.pdf |Schedule of KEBI Lectures}} * [[:didattica:ay2324:orario_en|Scheduling of MSc in Computer Science (LM-18)]] **Degrees**: * [[didattica:mscs|MSc in Computer Science (LM-18)]] </WRAP> ===== Course Objectives ===== <WRAP box round 95% center> == 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. </WRAP> ---- ===== Course Contents ===== <WRAP round 95% center box> * 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 </WRAP> ---- ===== Study material ===== <WRAP box round center 95%> **Course Material** * Organisation * Slides: {{ :didattica:ay2324:kebi:ke-0-organization.pdf |About Lecture and Lecturers}} * Lecture "Introduction" * Slides: {{ :didattica:ay2324:kebi:ke-1-introduction.pdf |Introduction}} * {{ :didattica:ay2324:kebi:davenport_2010_process_management_for_knowledge_work.pdf |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.}} * {{ :didattica:ay2324:kebi:exercise_knowledge_types_for_admission.pdf |Exercise/Homework: Types of Knowledge}} * Lecture "Knowledge in Processes" * Slides: {{ :didattica:ay2324:kebi:ke-2_knowledge_and_processes.pdf |Decision-Aware Business Processes}} * Slides: Example: Admission as a Decision-aware Business Process * Lecture "Decision Tables" * Slides: {{ :didattica:ay2324:kebi:ke-3-decisontables.pdf |Decision Tables - DMN}} * Reading: [[http://blog.maxconsilium.com/2014/09/introduction-to-decision-model-notation.html|Introduction into DMN]] * Exercise: {{ :didattica:ay2324:kebi:exercise_decision_table_reduction.pdf |Reduction of Decision Table}}, {{ :didattica:ay2324:kebi:dmn_decision_table_reimbursement.xlsx |Sample Table}} * Exercise: {{ :didattica:ay2324:kebi:exercise_dmn_for_booking_price.pdf |DMN for Booking Price}} * Homework: {{ :didattica:ay2324:kebi:exercise_decision_modeling_admission.pdf |Decision Modeling for Admission}} * Tools: * Download: [[https://camunda.com/download/modeler/|Camunda Workflow and Decision Modeler]] * Online: [[https://camunda.com/dmn/simulator/|Camunda Decision Simulator]] * Lecture “Rule-based Systems” * Slides: {{ :didattica:ay2324:kebi:ke-4-logic_programming_hidden.pdf |Rule-based Systems (Prolog)}} (with hidden information) * Reasoning examples: {{ :didattica:ay2223:kebi:ke-4-logic_programming-reasoningexample_simpe.pdf |simple}} and {{ :didattica:ay2223:kebi:ke-4-logic_programming-reasoningexample_ancestor.pdf |ancestor}} * Exercise: {{ :didattica:ay2223:kebi:ke-4-1-exercise_university.pdf |University}} * Exercise: {{ :didattica:ay2324:kebi:ke-4-2-exercise_family_rules.pdf |Family}} * Exercise: {{ :didattica:ay2324:kebi:ke-4-3-exercise_smallexamples.pdf |Further small examples}} * Exercise: {{ :didattica:ay2324:kebi:ke-4-5-exercise_minisudoku.pdf |Mini Sudoku}} * Exercise: {{ :didattica:ay2324:kebi:ke-4-6-exercise_traveling.pdf |Travelling}} * Exercise: {{ :didattica:ay2324:kebi:ke-4-7-exercise_friendship.pdf |Friendship}} * {{ :didattica:ay2324:kebi:ke-4-9-additional-exercise-assignment_creditcard.pdf |Fraud Detection}} * {{ :didattica:ay2324:kebi:ke-4-homeexercise_masterdecisions.pdf |Home Work}} * Nice browser-based [[http://swish.swi-prolog.org|Prolog Engine]] * Lecture “Forward- and Backward Chaining” * Slides: {{ :didattica:ay2324:kebi:ke-5_fc_vs_bc_-_compatibility_mode.pdf |Forward- and Backward Chaining}} * Lecture "Knowledge Graphs" * Download and launch GraphDB: [[https://www.ontotext.com/products/graphdb/]] * Slides: {{ :didattica:ay2324:kebi:kebi_knowledge_graphs.pdf |Knowledge Graphs}} * Exercise: {{ :didattica:ay2324:kebi:kg_exercises_and_solutions.zip |Class exercises and solutions}} * Slides: {{ :didattica:ay2324:kebi:kebi_knowledge_graphs_-_2nd_part_rdf_s_and_reasoning.pdf |Knowledge Graphs_Part2}} * Exercise: {{ :didattica:ay2324:kebi:familytree_with_schema.zip |File family tree with schema}} * Homework: {{ :didattica:ay2324:kebi:kebi_schema_inferences_students_exercise.pdf |Homework on RDF(S) reasoning}} * Lecture "Ontology Engineering" * Download and install Protégé: [[https://protege.stanford.edu/]] * Slides: {{ :didattica:ay2324:kebi:kebi_ontology_engineering.pdf |Ontology Engineering}} * Homework: {{ :didattica:ay2324:kebi:exercise_on_ontology_development_101.docx |Ontology Engineering on Teaching Domain}} * Solution: {{ :didattica:ay2324:kebi:ontology_development_101_solution_.pptx |Solution}} * Ontology: {{ :didattica:ay2324:ontology_msc_bis_homework_solution_.zip |Ontology}} * Lecture "Machine Reasoning" * Slides: {{ :didattica:ay2324:kebi:kebi_machine_reasoning.pdf |Machine Reasoning}} * Exercise: {{ :didattica:ay2324:kebi:familytree_with_schema__and_contradiction.zip |File family tree with contradiction}} * Exercise: {{ :didattica:ay2324:kebi:possible_solutions_for_contradiction_exercise.pdf |SPARQL solutions to detect contradictions in family tree ontology}} </WRAP> ---- ===== Recordings ===== <WRAP box round center 95%> Recordings of the lectures are password protected (passwords on request from the lecturers) * [[https://unicam.webex.com/unicam/ldr.php?RCID=30d02cd258badb97b224e53931d80a4c | 6th of March 2023: Introduction: What is knowledge]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=c2b0eedd6318717c58be8c3397730611 | 8th of March 2023: Knowledge in Processes, Decision Modeling, Decision Tables]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=a373f64361d7058e991fb0db3ab99c09 | 8th of April 2024: Decision Tables and Prolog]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=8acd51785f96defef24b49ec70f67b61 | 9th of April 2024: Prolog]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=4359dea26885770f02bc331e0aae18c9 | 29th of April 2024: Knowledge Graphs]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=a6690870ca355dc230a8e9cc69662848 | 30th of April 2024: Knowledge Graphs_Part 2 and Ontology Engineering]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=4359dea26885770f02bc331e0aae18c9 | 6th of May 2024: Machine Reasoning]] </WRAP> didattica/ay2324/kebi/main.txt Last modified: 2024/05/10 10:13by e.laurenzi