Knowledge Representation

Code:

MCI0001

Acronym:

RC

Keywords
Classification	Keyword
OFICIAL	Information Science

Instance: 2011/2012 - 1S

Active?	Yes
E-learning page:	http://moodle.fe.up.pt/
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Information Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MCI	19	Plano de estudos oficial	1	-	6	60	162

Teaching language

Portuguese

Objectives

The "Knowledge Representation" course has the main goal of leading students to build a faceted view of the theory and practice of knowledge representation, linking them to their former experience with domain modeling, information description and databases.

On completion of this course, the student should be able to:
-Briefly describe the milestones in knowledge representation, in the philosophy and computing domains;
-Use the conceptual map technique to capture reality in a selected domain;
-Use First-Order Logic as a tool for knowledge representation and inference;
-Relate knowledge representation in logic with data representation in databases:
-Base the representation of a domain on an ontology and justify the choice of its concepts;
-Describe the principles of the semantic web and its relation with classic knowledge representation;
-Represent knowledge in a domain using the ontology tools of the semantic web;
-Choose tools to support the knowledge representation component of a project.

Program

Knowledge representation in time: from Aristotle to Predicate Calculus, to databases, to the semantic web.
Conceptual maps. Fundamentals. Applications. Tools.
Propositional Logic. Representing facts. Connectives. Inference.
Predicate Logic. Quantification. Inference. Automated reasoning.
The semantic web. Fundamentals. Data modeling on the web.
Domain ontologies. Choice and representation of an ontology. Ontology languages. OWL. Inference in ontology languages.

Mandatory literature

Sowa, John F.; Knowledge representation. ISBN: 0-534-94965-7

Complementary Bibliography

Barwise, Jon; Language proof and logic. ISBN: 1-889119-08-3
W3C Semantic Web Activity: http://www.w3.org/2001/sw/

Teaching methods and learning activities

Tutorial classes are accompanied by practical sessions, using selected software. Students present their projects in the scheduled sessions.

Software

Protégé
LPL Software

keywords

Physical sciences > Computer science > Informatics

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	48,00
Exercise on Concept Maps	Teste	8,00		2011-10-03
Project 1: Logic	Trabalho escrito	20,00		2011-11-07
Project 2: Semantic Web	Trabalho escrito	20,00		2011-12-05
Test 1	Exame	1,00		2011-11-14
Test 2	Exame	1,00		2011-12-12
	Total:	-	0,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
Study	Estudo autónomo	64	2011-12-12
	Total:	64,00

Eligibility for exams

There is no final exam.
Students are required to have a minimum of 50% on each test and a minimum of 50% on each project.

Calculation formula of final grade

The final grade is computed using the formula
GRADE= round( 10% * Exercise on Concept Maps + 25% * Project 1 + 25% * Project 2 + 20% * Test 1 + 20% * Test 2 ).

Examinations or Special Assignments

None. All students have to complete the projects and present them as scheduled.

Special assessment (TE, DA, ...)

All students have to complete the projects and present them as scheduled.

Classification improvement

Improving the classification requires a new enrollment in the course, taking the course projects and tests again.