====== Knowledge Engineering ====== ---- ===== News ===== === Dear students, === Please [[https://www.adoxx.org/live/download-15 | download and install the ADOxx software]] for the next lecture Best regards, Knut Welcome to the lecture in Knowledge Engineering! We are happy that you plan to participate in our amazing module. However, we would be happy to know who you are. :-) Therefore, please fill the following google 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/nxZHWd2wcPt9Mymw7]] Best regards, Holger & Knut /* === Dear students, === For the lecture on 11th of May 2020 please use this room: [[https://fhnw.zoom.us/j/95153387074]]. Best regards, Knut Hinkelmann === Dear students, === Good news: We start definitely on **20th of April at 2pm**. To participate in the lecture, please join us in Zoom. The {{ :didattica:magistrale:kebi:ay_2021:orario_2_semestre_19-20_-_kebi-2.pdf |schedule}} is updated. Best regards, Knut Hinkelmann\\ Holger Wache */ ---- ===== General Info ===== **Teachers**: * Knut Hinkelmann * Holger Wache **Schedule**: The lecture dates are as follows: * 12/4 from 2pm to 6pm [[https://unicam.webex.com/meet/holgererik.wache]] * 13/4 from 9am to 1pm [[https://unicam.webex.com/meet/knut.hinkelmann]] * 19/4 from 2pm to 6pm [[https://unicam.webex.com/meet/holgererik.wache]] * 20/4 from 9am to 1pm [[https://unicam.webex.com/meet/holgererik.wache]] * 26/4 from 2pm to 6pm [[https://unicam.webex.com/meet/holgererik.wache]] * 27/4 from 9am to 1pm [[https://unicam.webex.com/meet/holgererik.wache]] * 10/5 from 2pm to 6pm [[https://unicam.webex.com/meet/holgererik.wache]] * 11/5 from 9am to 1pm **Room LB1** and [[https://unicam.webex.com/meet/knut.hinkelmann]] * 17/5 from 2pm to 6pm **Room LB1** and [[https://unicam.webex.com/meet/knut.hinkelmann]] * 25/5 from 9am to 1pm **Room LB1** and [[https://unicam.webex.com/meet/knut.hinkelmann]] * 31/5 from 2pm to 6pm **Room LB1** and [[https://unicam.webex.com/meet/knut.hinkelmann]] * 01/6 from 9am to 1pm **Room LB1** and [[https://unicam.webex.com/meet/knut.hinkelmann]] /* {{:didattica:magistrale:kebi:ay_2021:orario_2_semestre_19-20_-_kebi-2.pdf |Knowledge Engineering and Business Intelligence}} */ **ESSE3 Link** * /* [[https://didattica.unicam.it/Guide/PaginaADErogata.do?ad_er_id=2018*N0*N0*S2*12329*8709&ANNO_ACCADEMICO=2019&mostra_percorsi=S|Knowledge Engineering and Business Intelligence - AY 2019/2020]] */ ---- ===== 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 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 ---- ===== Study material ===== **Course Material** * {{ :didattica:magistrale:kebi:ay_2021:ke-0-organization.pdf |Organisation}} * Lecture "Introduction" * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-1-introduction.pdf |Introduction}} * {{ :didattica:magistrale:kebi:ay_2021: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.}} * Exercise/Homework: {{ :didattica:magistrale:kebi:ay_2021:exercise_knowledge_types_for_admission.pdf |Types of Knowledge}} * Lecture "Knowledge in Processes" * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-2_knowledge_and_processes.pdf |Decision-Aware Business Processes}} /* * Example: {{ :didattica:magistrale:kebi:ay_2021:example_decision-aware_process_modeling.pdf |Decision-Aware Business Process }} */ * Lecture "Decision Tables" * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-3-decisontables.pdf |Decision Tables - DMN}} * Example: {{ :didattica:magistrale:kebi:ay_2021:booking_price_calculation.zip |Price Calculation for Room Booking}} * Reading: [[http://blog.maxconsilium.com/2014/09/introduction-to-decision-model-notation.html|Introduction into DMN]] * Exercise: {{ :didattica:magistrale:kebi:ay_2021:exercise_decision_table_reduction.pdf |Reduction of Decision Tables}}, {{ :didattica:magistrale:kebi:ay_2021:dmn_decision_table_reimbursement.xlsx |Sample Table}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:exercise_decision_modeling_admission.pdf |Decision Modeling for Admission}} /* * Exercise and Solution: {{ :didattica:magistrale:kebi:ay_2021:exercise_decision_modeling_admission_with_solution.pdf |Decision Modelling for Admission}}, {{ :didattica:magistrale:kebi:ay_2021:eligibility_decision_table.zip |DMN file of the decision table}} */ * 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” * Whiteboard: [[https://drive.google.com/drive/folders/10-IMhx-3CnDdNI35S1OYOOAMReON3yZk?usp=sharing | Holger's Whiteboard]] * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-4-logic_programming.pdf |Rule-based Systems}} * Reasoning example: {{ :didattica:magistrale:kebi:ay_2021:ke-4-logic_programming-reasoningexample_ancestor.pdf |ancestor}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-4-1-exercise_university.pdf |University}} and Solution * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-4-2-exercise_family_rules.pdf |Family}} and {{ :didattica:magistrale:kebi:ay_2021:ke-4-2-exercise_family_rules_solution.pdf |Solution}} * Exercise: Further {{ :didattica:magistrale:kebi:ay_2021:ke-4-3-exercise_smallexamples.pdf |small examples}} and {{ :didattica:magistrale:kebi:ay_2021:ke-4-3-exercise_smallexamples_solution.pdf |Solution}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-4-5-exercise_minisudoku.pdf |Mini Sudoku}} and {{ :didattica:magistrale:kebi:ay_2021:ke-4-5-exercise_minisudoku_solution.pdf |Solution}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-4-7-exercise_friendship.pdf |Friendship}} and {{ :didattica:magistrale:kebi:ay_2021:ke-4-7-exercise_friendship_solution.pdf |Solution}} * {{ :didattica:magistrale:kebi:ay_2021:ke-4-homeexercise_masterdecisions.pdf |Home Work}} with {{ :didattica:magistrale:kebi:ay_2021:ke-4-homeexercise_masterdecisions_solution.pdf |Solution}} * Nice browser-based [[http://swish.swi-prolog.org|Prolog Engine]] * Lecture “Forward- and Backward Chaining” * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-5_fc_vs_bc.pdf |Forward- and Backward Chaining}} * Lecture “Fuzzy logic” * Whiteboard: [[https://drive.google.com/drive/folders/10-IMhx-3CnDdNI35S1OYOOAMReON3yZk?usp=sharing | Holger's Whiteboard]] * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-6-fuzzylogic.pdf |Fuzzy Logic}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-6-1-exercise_define_fuzzy_set.pdf |Fuzzy Sets}} and {{ :didattica:magistrale:kebi:ay_2021:ke-6-1-exercise_define_fuzzy_set_solution.pdf |Solution}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-6-2-exercise_fuzzy_set_operations.pdf |Fuzzy Set Operations}} and {{ :didattica:magistrale:kebi:ay_2021:ke-6-2-exercise_fuzzy_set_operations_solution.pdf |Solution}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-6-3-credit_analysis.pdf |Credit Analysis}} * {{ :didattica:magistrale:kebi:ay_2021:ke-6-homeexercise_masterdecisions.pdf |Home Work}} and {{ :didattica:magistrale:kebi:ay_2021:ke-6-homeexercise_masterdecisions_solution.pdf | Solution}} * Lecture “Knowledge Nets and RDF” * Whiteboard: [[https://drive.google.com/drive/folders/10-IMhx-3CnDdNI35S1OYOOAMReON3yZk?usp=sharing | Holger's Whiteboard]] * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-7-rdf_knowledgenets.pdf |Knowledge Nets and RDF}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-7-1-exercise-rdf-graph.pdf |RDF Graphs}} and {{ :didattica:magistrale:kebi:ay_2021:ke-7-1-exercise-rdf-graph_SOLUTION.pdf |Solution}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-7-2-exercise-rdf-schema.pdf |RDF Schema}} and {{ :didattica:magistrale:kebi:ay_2021:ke-7-2-exercise-rdf-schema_SOLUTION.pdf |Solution}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:ke-7-3-exercise-rdfs-inferences.pdf |RDF Schema Inferences}} and {{ :didattica:magistrale:kebi:ay_2021:ke-7-3-exercise-rdfs-inferences_SOLUTION.pdf |Solution}} * Lecture “Ontology Engineering” * Slides:{{ :didattica:magistrale:kebi:ay_2021:ke-8_ontology_engineering.pdf | Ontology Engineering}} * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-8-2_ontologies_and_rules.pdf |Ontologies and Rules}} * Example: {{ :didattica:magistrale:kebi:ay_2021:ke2021.zip |Ontology for university teaching to open in Protege}} * Exercise: {{ :didattica:magistrale:kebi:ay_2021:exercise_-_business_process_ontology_-_preliminary.pdf |Business Process Ontology - preliminary version from 11 May 2021}} * {{ :didattica:magistrale:kebi:ay_2021:2021-05-11_bpm.zip |University and Business Process Ontologies to open in Protege (preliminary version from 11 May 2021)}} /* * Exercise: {{ :didattica:magistrale:kebi:ay_2021:exercise_-_business_process_ontology.pdf |Business Process Ontology}} * {{ :didattica:magistrale:kebi:ay_2021:process_and_university_ontology.zip |University and Business Process Ontologies to open in Protege}} */ * Literature: Noy, N. F., & McGuinness, D. L. (2001). [[http://protege.stanford.edu/publications/ontology_development/ontology101.pdf | Ontology development 101: A guide to creating your first ontology.]] Stanford Knowledge Systems Laboratory Technical Report KSL-01-05. * Download: [[https://protege.stanford.edu/ |Protege: Desktop Version (Platform independent or Windows)]] * Lecture “Conceptual Modelling” * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-9_conceptual_modelling.pdf |Conceptual Modelling}} * Download: [[https://www.adoxx.org/live/download-15 |ADOxx Model Engineering Environment]] * Lecture “Ontology-based Modelling” * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-10_ontology-based_modeling.pdf |Ontology-based Modelling}} * Literature: [[https://docenti.unicam.it/ApriMat.aspx?id=12200 | Hinkelmann et al. (2016). A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontologies]] * Literature: [[https://docenti.unicam.it/ApriMat.aspx?id=12248 | Hinkelmann et al. (2018). Ontology-based Metamodelling.]] * AOAME * Data/Ontologies: https://aoame-fuseki.herokuapp.com/dataset.html * Modeling Environment: https://aoame.herokuapp.com/modeller * Lecture "Machine Learning" * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-11-1_machine_learning.pdf |Introduction to Machine Learning}} * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-11-2_learning_decision_trees.pdf |Symbolic Machine Learning: Learning Decision Trees}} * Reading Material: {{ :didattica:magistrale:kebi:ay_1718:decision_tree_learning_lecture.pdf |Decision Tree Learning}} * Exercise: {{ :didattica:magistrale:kebi:ay_1718:exercise_learning_carsales.pdf |Auto Traders}} * Exercise: {{ :didattica:magistrale:kebi:ay_1819:exercise_health_insurance_learning.pdf |Health Insurance: Learning Risk Assessment}} * Tool: {{ :didattica:magistrale:kebi:ay_1718:weka_introduction.pdf |WEKA Learning Environment}} * Data Sets: {{ :didattica:magistrale:kebi:ay_1718:datasets.zip |playing tennis, creditworthyness (CSV Files), car sales and Health Insurance (ARFF and Excel file)}} * Lecture "Combining Machine Learning and Knowledge Engineering" * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-12_combining_machine_learning_and_knowledge_engineering.pdf |Combining Machine Learning and Knowledge Engineering}} * Example: {{ :didattica:magistrale:kebi:ay_2021:example_machine_learning_and_knowledge.pdf |Machine Learning and Knowledge}} /* * Assignment: {{ :didattica:magistrale:kebi:ay_2021:assignment_health_insurance_knowledge.pdf |Health Insurance: Combining Learning with Knowledge Engineering}} */ * Assignment with Solution: {{ :didattica:magistrale:kebi:ay_2021:assignment_health_insurance_knowledge_with_solution.pdf |Health Insurance: Combining Learning with Knowledge Engineering}} * {{ :didattica:magistrale:kebi:ay_2021:prolog_and_ontologies.pdf |Representing the knowledge in Prolog and RDFS}} * Lecture “Case-Based Reasoning” * Slides: {{ :didattica:magistrale:kebi:ay_2021:ke-13-cbr.pdf |Case-Based Reasoning}} /* * Assignment: {{ :didattica:magistrale:kebi:ay_2021:assignment_health_insurance_cbr.pdf |CBR for Health Insurance Applications}} */ * Assignment and Solution: {{ :didattica:magistrale:kebi:ay_2021:assignment-solution_health_insurance_cbr.pdf |CBR for Health Insurance Applications}} ---- ===== Recordings ===== Recordings of the lectures are password protected (passwords on request from the lecturers) * [[https://unicam.webex.com/unicam/ldr.php?RCID=fac19d05428a2d3e38cce026a08bc253 | Recording from 12th of April - Introduction]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=6928b0b2e6212f805e11c64b8ed67dd5 | Recording from 13th of April - Knowledge in Processes, Decision Modeling]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=1891ce65f3a8625d3d893a0953a3d452 | Recording from 19th of April - Representing Knowledge in Rules]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=c1625571dcf8bb1381231b763364fce6 | Recording from 20th of April - Recursion, Inference Procedure, Negation]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=55636bfd3e2b18a77fed6267f77bdc8a | Recording from 26th of April - Fuzzy Set and Fuzzy Theory]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=f4b6ef67c6180a2505fab21fc8f326f8 | Recording from 27th of April - Fuzzy Logic and Fuzzy Logic Controller]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=35d34bd256cd6e00e5883af064b1d637 | Recording from 10th of May - Knowledge Nets & RDF]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=4227c7fee23cca112c931a886a62e3ec | Recording from 11th of May - Ontology Engineering]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=0f9fb7fd1a60d92b644aa577f35a93ef | Recording from 17th of May - Conceptual Modeling]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=f29bb0bf32ae4160231d6f1927dfec7f | Recording from 25th of May - Ontology-based Modeling]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=01d9098fc66ab7e7f592cc180585db1a | Recording from 31st of May - Machine Learning]] * [[https://unicam.webex.com/unicam/ldr.php?RCID=9671721299574c2ee862f4fca5ed626a | Recording from 1st of June - Machine Learning, Knowledge Engineering, CBR]] ---- ===== Exams ===== **Exam Dates A.Y. 2020/2021** * 8th of June, 10:30 - 12:00, room LB1 del Polo Lodovici edificio B * 6th of July, 10:30 - 12:00, room LB1 del Polo Lodovici edificio B * 27th of September, 10:30 - 12:00, room "G" in the Department of Physics /* * [[https://docs.google.com/spreadsheets/d/1DAifL0KXWsyr-rwhFDISwb2RKcNn21TBptM1Kqz2Nlo/edit?usp=sharing | You can choose a date from the schedule]] **Exam rules**: */