Knowledge Representation and Engineering

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Credits
6
Types
Elective
Requirements
This subject has not requirements

Department
URV
Mail
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.

Teachers

Person in charge

  • David Riaño ( )

Weekly hours

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

Competences

Generic Technical Competences

Generic

  • CG3 - Capacity for modeling, calculation, simulation, development and implementation in technology and company engineering centers, particularly in research, development and innovation in all areas related to Artificial Intelligence.

Technical Competences of each Specialization

Academic

  • CEA12 - Capability to understand the advanced techniques of Knowledge Engineering, Machine Learning and Decision Support Systems, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.
  • CEA13 - Capability to understand advanced techniques of Modeling , Reasoning and Problem Solving, and to know how to design, implement and apply these techniques in the development of intelligent applications, services or systems.

Professional

  • CEP2 - Capability to solve the decision making problems from different organizations, integrating intelligent tools.

Transversal Competences

Teamwork

  • CT3 - Ability to work as a member of an interdisciplinary team, as a normal member or performing direction tasks, in order to develop projects with pragmatism and sense of responsibility, making commitments taking into account the available resources.

Solvent use of the information resources

  • CT4 - Capacity for managing the acquisition, the structuring, analysis and visualization of data and information in the field of specialisation, and for critically assessing the results of this management.

Appropiate attitude towards work

  • CT5 - Capability to be motivated for professional development, to meet new challenges and for continuous improvement. Capability to work in situations with lack of information.

Reasoning

  • CT6 - Capability to evaluate and analyze on a reasoned and critical way about situations, projects, proposals, reports and scientific-technical surveys. Capability to argue the reasons that explain or justify such situations, proposals, etc..

Basic

  • CB6 - Ability to apply the acquired knowledge and capacity for solving problems in new or unknown environments within broader (or multidisciplinary) contexts related to their area of study.

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: CT3, CT4, CT6, CEA13, CG3,
  3. Know how to apply knowledge engineering methods for concrete problems.
    Related competences: CT5, CT6, CEA12, CEA13, CEP2, CB6,

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

Activities

Introduction

Academic description of the subject, contents, evaluation process, etc.
Theory
1
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
0.5

Regular master class

Theory
48
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
24
Objectives: 1 2 3
Contents:

Evaluation tests

Theory
5
Problems
0
Laboratory
0
Guided learning
0
Autonomous learning
10

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 exercices resolution in ordinari 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.

Bibliografy

Basic:

  • Knowledge Representation and Reasoning - Brachman, Ronald J; Levesque, Hector J., Morgan Kaufmann , 2004. ISBN: 1-55860-932-6

Complementary: