Department of Information and Computing Sciences

Departement Informatica Onderwijs
Bachelor Informatica Informatiekunde Kunstmatige intelligentie Master Computing Science Game&Media Technology Artifical Intelligence Business Informatics

Onderwijs Informatica en Informatiekunde

Vak-informatie Informatica en Informatiekunde

Kennissystemen

Website:website met extra informatie
Vakcode:INFOB3KSY
Studiepunten:7.5 ECTS
Periode:periode 4 (week 17 t/m 26, d.w.z. 23-4-2018 t/m 29-6-2018; herkansing week 28)
Timeslot:C
Deelnemers:tot nu toe 0 inschrijvingen
Rooster:De officiële roosters staan ook in Osiris
Docenten:
vormgroeptijdweekzaaldocent
college          Floris Bex
 
werkcollege groep 1        Remi Wieten
Kalliopi Zervanou
   
Tentamen:
week: 26ma 25-6-201813.30-16.30 uurzaal: -
week: 28ma 9-7-201813.30-16.30 uurzaal: -aanvullende toets
Nota bene:Er is geen recente vakbeschrijving beschikbaar.
Onderstaande tekst is een oude vakbeschrijving uit collegejaar 2016/2017
Inhoud:

In many organisations, knowledge intensive-tasks like diagnosis, assessment, scheduling, monitoring, etc., are performed in a specific domain. To conduct successful knowledge intensive-tasks, it important to manage this knowledge.

Knowledge is managed in so-called knowledge systems. Knowledge systems establish the knowledge for a particular field for its further application. Nowadays, knowledge systems are the core of e-learning systems, coaching systems, decision support systems, and serious games.

In order to develop a knowledge system, it is necessary to acquire knowledge ('knowledge acquisition' or knowledge elicitation), usually from domain experts and / or literature. A good model of knowledge is crucial for the ultimate knowledge system. However, acquiring and modeling knowledge is a complicated and time consuming process.

This course deals with different phases in developing a knowledge system such as problem analysis, knowledge analysis, and implementation. To conduct these phases, some models need to be developed, such as a task models, domain models, and inference models. Furthermore, various techniques for eliciting knowledge from domain experts are going to be explored.

To successfully follow the course, students need to have a background in information systems, information analysis or business process modelling.

Literatuur:Kan veranderen!
  • A.Th. Schreiber, J.M. Akkermans, A.A. Anjewierden, R. de Hoog, N.R. Shadbolt, W. Van de Velde, B.J. Wielinga (2000). 'Knowledge Engineering and Management: The CommonKADS Methodology'. MIT Press. ISBN: 0-262-19300-0.
  • Research articles
Werkvorm:The course consists of two 2-hour lectures and a 2-hour workshop every week. The rest of the work is self study.
Toetsvorm:

Deadlines

An overview of all the deadlines can be found in the schedule page. Hand in each requested assignment before the specified deadline, one minute late is too late. Late submissions are not going to be graded/reviewed.

Grading

The course is run as a project, where several deliverables have to be produced:

  • Assignment part 1 (0%)
  • Assignment part 2 and 3 (0%)
  • Assignment part 4 and 5 (0%)
  • Final report (50%)
  • The deadlines of the assignments and final report are strict. The assignments part 1, part 2, part 3, part 4, and part 5 are not graded. However, failing to perform any of the assignments will result in no mark for the course. In addition, there is one exam in the course: a final exam in week 25. The two parts (final report and exam) contribute to your final grade as follows:
  • Final report: 50%
  • Final exam: 50 %
  • In order to pass the course your final grade should be 5.5 or higher. Furthermore, we use the following constraints:

  • The Final report should be 5.0 or higher;
  • The final exam grade should be 5.0 or higher.
  • Inspanningsverplichting voor aanvullende toets:Om aan de aanvullende toets te mogen meedoen moet de oorspronkelijke uitslag minstens 4 zijn.
    Beschrijving:

    Learning objectives

  • Skills: Apply methods and techniques for knowledge acquisition Develop an organisational knowledge model Implement a knowledge system
  • Knowledge: Theoretical aspects for modelling and eliciting knowledge systems Tools and techniques for developing knowledge systems
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