Knowledge and Representation Engineering

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Credits
6
Types
Elective
Requirements
This subject has not requirements, but it has got previous capacities
Department
CS;URV
Web
http://moodle.urv.net
In the context of computer applications, the need to implement intelligent solutions to increasingly 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.

Weekly hours

Theory
3.6
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
3.7

Objectives

  1. Differentiate between the concepts data, information and knowledge, and their technologies.
    Related competences: CB6,
  2. Know and know how to use alternative knowledge representation formalisms.
    Related competences: CT4, CT6, CEA13, CT3, CG3,
  3. Know how to apply knowledge engineering methods for concrete problems.
    Related competences: CB6, CT5, CT6, CEA12, CEA13, CEP2,

Contents

  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.
  4. Knowledge Representation in the Web
    Representing data with HTML; Formalization and representation of information with DTD, XMLSchema, XML; Tools for data and information management on the web with XPath and XSL; Formalization and representation of knowledge with RDF and OWL2.

Activities

Activity Evaluation act


Introduction

Academic description of the subject, contents, evaluation process, etc.

Theory
1h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Regular master class

Introduction of the important concepts of the course, the relevant technologies, and the promotion of assimilation with specific and clear examples.
Objectives: 1 2 3
Contents:
Theory
49h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
15h

Knowledge representation test

Test with practical exercises and theoretical questions.
Objectives: 1 2
Week: 8
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

Knowledge Enginering test

Test of practical exercises and theoretical questions on knowledge engineering.
Objectives: 3
Week: 15
Theory
2h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
10h

Practical work of representation of knowledge

Work in a group where the construction of a knowledge base through software is exercised
Objectives: 2
Week: 7
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
16.5h

Practical work of representation of knowledge on the Web

Work in a group where the construction of a web ontology is carried out through Protege.

Week: 14
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
4h

Teaching methodology

Introductory Activities: Introduction of the lecturer, the objectives of the subject, the contents, the teaching methodology, evaluation process, and the supporting material.

Master Session: The lecturer will explain the basic contents of the subject with examples. (S)he will provide the student all the material required to prepare the subject.

Solving problems and exercises in ordinary class: In groups we'll study a tool for knowledge management and we'll do a practical work. Each group will present the results to the lecturer.

Evaluation methodology

(50%) Problems and exercises resolution in ordinary class: Thorough the course there will be several partial tests.

(50%) Objective tests with short questions: Objective tests with short questions every other week of 30 min each. We'll devote one of these tests (this one of 2h) to evaluate the total content of the subject.

The student who don't pass the evaluation, will have a reparatory exam on the full contents of the subject (100% of the final mark).

Bibliography

Basic:

Complementary:

Previous capacities

Self-contained subject.