IDENTIFYING DATA 2016_17
Subject (*) KNOWLEDGE REPRESENTATION AND ENGINEERING Code 17685205
Study programme
Computer Security Engineering and Artificial Intelligence (2016)
 Cycle 2nd
Descriptors Credits Type Year Period
3 Optional 2Q
Language
Anglès
Department Computer Engineering and Mathematics
Coordinator
RIAÑO RAMOS, DAVID
 E-mail david.riano@urv.cat
Lecturers
RIAÑO RAMOS, DAVID
Web http://moodle.urv.cat
General description and relevant information In the context of computer applications, the need to implement intelligent solutions to increasing complex problems (such as business intelligence, intelligent control systems, decision support sytems, Internet browsing, etc.) is becoming every time more frequent. Many of these intelligent solutions are based on the existence of a knowledge base that regulates or affects the performance of computer systems and gives these systems the (distinguishing) character of intelligent. These knowledge bases are expressed according to some formats, structures and formal representation languages that, in some cases, define international standards. The field of "knowledge representation" in this course sets the fundamentals for these formats and languages ??for knowledge formalization. The field of "knowledge engineering" addresses the learning and practice of techniques and methods for building knowledge bases.

Competences
Type A Code Competences Specific
 A9 Apply computational, mathematical, statistical and artificial intelligence methods in order to model, design and develop applications, services, smart systems and knowledge-based systems.
Type B Code Competences Transversal
  CT1 Gestionar i comunicar informació complexa, de temes diversos, amb naturalitat, en llengua estrangera.
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
Type C Code Competences Nuclear

Learning outcomes
Type A Code Learning outcomes
 A9 Comprèn la importància pràctica de la representació i l'adquisició eficient de coneixement dins de la Intel·ligència Artificial.
Coneix i comprèn l'evolució històrica dels mecanismes de gestió i representació del coneixement.
Es familiaritza amb l'ús pràctic d'alguna eina moderna d'enginyeria del coneixement.
Type B Code Learning outcomes
  CT1 Gestionar i comunicar informació complexa, de temes diversos, amb naturalitat, en llengua estrangera.
 CT2 Formular valoracions a partir de la gestió i ús eficient de la informació.
 CT4 Treballar en equips multidisciplinars i en contextos complexes.
Type C Code Learning outcomes

Contents
Topic Sub-topic
1. Introduction and Concepts Data, Information and Knowledge; Knowledge Types and Uses; Knowledge Representation; Knowledge Engineering; Syntax and Semantics
2. Knowledge Representation First order logic; Rules and production systems; Object-Oriented Representations; Network Representation; Ontologies
3. Knowledge Engineering Knowledge Life-Cycle; Knowledge Audit; Knowledge Acquisition; Detailed case study

Planning
Methodologies  ::  Tests
  Competences (*) Class hours
 Hours outside the classroom
 (**) Total hours
Introductory activities
1 1.5 2.5
Lecture
A9
 14 17.5 31.5
Problem solving, exercises
A9
 3 4.5 7.5
Presentations / expositions
A9
 1 1.5 2.5
Practicals using information and communication technologies (ICTs) in computer rooms
A9
CT4
 6 9 15
Personal tuition
1 0 1
 
Extended-answer tests
A9
CT1
CT2
 3 6 9
Practical tests
A9
CT4
 1 5 6
 
(*) On e-learning, hours of virtual attendance of the teacher.
(**) The information in the planning table is for guidance only and does not take into account the heterogeneity of the students.

Methodologies
Methodologies
  Description
Introductory activities Course presentation: contents, calendar of activities, evaluation, bibliography, ...
Lecture Regular lectures in which the contents of the subject is explained.
Problem solving, exercises Practical classes in which the professor and the students will solve problems.
Presentations / expositions
Practicals using information and communication technologies (ICTs) in computer rooms
Personal tuition Personalized attention to solve student doubts. Out of class hours.

Personalized attention
Description
The lecturer provides six outclass hours per week to attend individual and group doubts.

Assessment
Methodologies Competences Description Weight        
Practical tests
A9
CT4
 Practical work in groups of two people where knowledge have to be represented. 40%
Extended-answer tests
A9
CT1
CT2
 Two exams for the student to show skills in Knowledge Representation (test 1) and Knowledge Engineering (test 2) by means of exercises. 60%
Others  

There is a remedial test covering all the course with practical exercises to solve.

 
Other comments and second exam session

L'examen de segona convocatòria serà unic i tindrà un pes del 100% de la nota.


Sources of information

Basic Brachman, Ronald J; Levesque, Hector J., Knowledge Representation and Reasoning, 2004, Morgan Kaufmann
Riaño, David, Knowledge Representation and Engineering Notes, yearly,

Complementary

Recommendations
(*)The teaching guide is the document in which the URV publishes the information about all its courses. It is a public document and cannot be modified. Only in exceptional cases can it be revised by the competent agent or duly revised so that it is in line with current legislation.