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Course info
KIP / 7ZNAI
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Course description
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Department/Unit / Abbreviation
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KIP
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7ZNAI
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Academic Year
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2023/2024
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Academic Year
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2023/2024
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Title
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Knowledge representation
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Form of course completion
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Exam
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Form of course completion
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Exam
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Accredited / Credits
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Yes,
6
Cred.
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Type of completion
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Combined
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Type of completion
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Combined
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Time requirements
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lecture
2
[Hours/Week]
practical class
2
[Hours/Week]
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Course credit prior to examination
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No
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Course credit prior to examination
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No
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Automatic acceptance of credit before examination
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No
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Included in study average
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YES
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Language of instruction
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Czech
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Occ/max
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Automatic acceptance of credit before examination
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No
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Summer semester
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0 / -
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0 / -
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0 / -
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Included in study average
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YES
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Winter semester
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11 / -
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0 / -
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0 / 0
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Repeated registration
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NO
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Repeated registration
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NO
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Timetable
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Yes
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Semester taught
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Winter semester
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Semester taught
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Winter semester
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Minimum (B + C) students
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not determined
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Optional course |
Yes
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Optional course
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Yes
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Language of instruction
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Czech
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Internship duration
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0
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| No. of hours of on-premise lessons |
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Evaluation scale |
A|B|C|D|E|F |
| Periodicity |
every year
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| Specification periodicity |
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Fundamental theoretical course |
Yes
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| Fundamental course |
No
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| Fundamental theoretical course |
Yes
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| Evaluation scale |
A|B|C|D|E|F |
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Substituted course
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KIP/ZNAIN
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Preclusive courses
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N/A
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Prerequisite courses
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N/A
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Informally recommended courses
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N/A
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Courses depending on this Course
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N/A
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Histogram of students' grades over the years:
Graphic PNG
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XLS
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Course objectives:
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Subject Knowledge Engineering focuses on automated retrieval of information and consequently knowledge of data. The aim of the course is to acquaint students with the tools and approaches for the extraction of information from data with a representation of the information in the form of models and using these models for acquiring knowledge about problem solving.
Knowledge engineering uses a variety of approaches and techniques, data mining / text mining from various sources (databases, web), machine learning, expert and knowledge systems, computational intelligence, visualization and other techniques.
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Requirements on student
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The exam is awarded to the student following the valid Rules of Study and Examination Rules of the OU.
During the semester, students draw up a term paper, which will be assessed pass/fail.
After meeting the semester project, students will be admitted to the oral exam, which will be evaluated by a maximum of 100 points.
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Content
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1. Knowledge Systems - Introduction
2. Representation of knowledge - information, knowledge
3. Methods of representation and processing of knowledge in the UI
4. Regular, non-regular and hybrid systems
5. Associative (semantic) networks
6. Semantic Web
7. Formal ontology and RDF model
8. Basic Mining Data Tasks
9. Data mining
10. Machine Learning
11. RELE algorithm and board type systems
12. Design and implementation of knowledge systems
13. Presentation and defense of projects
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Activities
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Fields of study
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Guarantors and lecturers
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Literature
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Basic:
POLI, Roberto (ed.). Handbook of anticipation: Theoretical and applied aspects of the use of future in decision making. New York: Springer, 2019. ISBN 9783319317373.
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Basic:
Žáček, Martin. Reprezentace znalostí, inovovaný text, Ostravská univerzita v Ostravě 2013.
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Basic:
Mařík, V., Štěpánková, O. Umělá inteligence (6). Praha, 2013. ISBN 978-80-200-2276-9.
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Extending:
Cuesta, Hector. Analýza dat v praxi. Brno, 2015. ISBN 978-80-251-4361-2.
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Extending:
SKLENÁK, V. a kol. Data, informace, znalosti a Internet. Praha: C. H. Beck, 2001. ISBN 80-7179-409-0.
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Extending:
Fagin, R., Halpern, J.Y., Moses, Y., Vardi. Reasoning about Knowledge. MIT Press, 1995. ISBN 0-262-56200-6.
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Recommended:
Přemysl Janíček, Jiří Marek a kolektiv. Expertní inženýrství v systémovém pojetí. Praha, 2013. ISBN 978-80-247-4127-7.
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Recommended:
Staab,S.Studer, R. (eds.). Handbook on Ontologies.. Springer-Verlag, 2004. ISBN 3540709991.
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Recommended:
Beader et col.(eds.). The Description Logic Handbook-Theory, Interprelation and Applications.. Cambridge Univ.Press, 2003. ISBN 9780521150118.
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Recommended:
Sklenák, V. Znalostní technologie - teorie vs. praxe.
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On-line library catalogues
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Time requirements
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All forms of study
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Activities
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Time requirements for activity [h]
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Being present in classes
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52
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Preparation for an exam
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20
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Self-tutoring
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40
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Semestral work
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35
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Total
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147
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Prerequisites
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Competences - students are expected to possess the following competences before the course commences to finish it successfully: |
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| Students are asked from the field of artificial intelligence fundamentals - propositional and predicate logic. |
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Learning outcomes
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Knowledge - knowledge resulting from the course: |
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Znalost reprezentace znalostí - metody a zpracování znalostí v UI,
znalost nových sémanticky orientovaných přístupů k pojetí webu jako rozsáhlé znalostní báze, úlohy data miningu, dolování dat a strojové učení.
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Skills - skills resulting from the course: |
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| Design of a knowledge system, working with large data. |
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Assessment methods
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Knowledge - knowledge achieved by taking this course are verified by the following means: |
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| IIB25 - Seminar work / report |
| IC11 - Activity in lessons (in discussion, group work, etc.) |
| IC6 - Oral examiantion |
| IIC29 - Creation of an ICT product - PC software / educational software, audio/video material, web pages |
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Teaching methods
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Knowledge - the following training methods are used to achieve the required knowledge: |
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| A1 - Lecture |
| B1 - Discussion |
| B3- Lecture based on problem exposition |
| C3 - Work with graphs/schemes/concept map |
| C7 - Computer simulation |
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