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COMP30411: Knowledge Representation (2007-2008)

This is an archived syllabus from 2007-2008

Knowledge Representation
Level: 3
Credit rating: 10
Pre-requisites: COMP10020
Co-requisites: No Co-requisites
Duration: 11 weeks
Lecturers: Sean Bechhofer, Bijan Parsia
Course lecturers: Sean Bechhofer

Bijan Parsia

Additional staff: view all staff
Sem 1 w1-5,7-12 Lecture LF17 Tue 11:00 - 12:00 -
Sem 1 w1-5,7-12 Lecture LF17 Thu 16:00 - 17:00 -
Assessment Breakdown
Exam: 0%
Coursework: 0%
Lab: 0%


The field of Knowledge Representation (KR) lies at the intersection of (at least) Artificial Intelligence and Information Management. KR is the attempt to provide rich representations of the world and various things in it that supports building programs that are sensitive to the world via these representations. KR has been used to build expert and diagnostic systems, speech recognizers, games, automated planners, etc. and is the foundation of the Semantic Web, an attempt to remake the World Wide Web so that the content is accessible not only to human beings, but to sophisticated artificial agents.

In this course, we will explore various formalisms for knowledge representations primarily focusing on classical first order logic and interesting fragments thereof (primarily, Description Logics). We will look at attempts to represent various parts of commonsense and scientific knowledge, as well as the use of KR for conceptual modeling in information systems. We will pay special attention to knowledge representations found on the Web, and the special challenges involved.

We will also analyze the problems and promises of KR through discussion of some of the seminal articles of the field.


The course will provide students with an understanding of logic and logic-based knowledge representation formalisms, their theoretical and practical aspects, some relevant reasoning services, and how these are used to support modelling. It will also discuss various issues in knowledge acquisition and engineering with an emphasis on realistic applications.

Learning Outcomes

A student completing this course unit should:

1. Have an understanding of knowledge representation, its motivations, applicability, advantages and pitfalls (A)

2. Have knowledge and understanding of some basic KR formalisms namely first-order and description logics. (A)

3. Have knowledge and understanding of the way in which automated reasoning can be used to help with modelling. (A and B)

4. Have mastered the basic range of techniques for building knowledge representations using standard tooling (B, C and D)

Assessment of Learning outcomes

Learning outcomes will be assessed by examination. Outcomes 1 and 4 will also be assessed via exercises and laboratories during the course.

Contribution to Programme Learning Outcomes

A1, A5, B1, B3, C4


Topic covered include:

Knowledge acquisition
First order logic: syntax, semantics, proof theory, and applications
Description logics: syntax, semantics, proof theory, and applications
Ontologies and ontology engineering
Logic engineering
Conceptual Modelling
Commonsense and scientific representation

Reading List

Selected papers and technical reports will be distributed during the lectures.

Core Text
Title: Knowledge, Representation and Reasoning
Author: Ronald J. Brachman, Hector J. Levesque
ISBN: 1558609326
Publisher: Elsevier Science & Technology
Year: 2004

Supplementary Text
Title: Formal Theories of the Common Sense World
Author: Hobbs, Jerry R., Moore, Robert C. J.R. Hobbs, R.C. Moore
ISBN: 0893912131
Publisher: Intellect Books
Year: 1985

Supplementary Text
Title: Language, Proof and Logic
Author: Jon Barwise, John Etchemendy
ISBN: 157586374X
Publisher: The University of Chicago Press
Year: 2003

Supplementary Text
Title: Description Logic Handbook
Author: Franz Baader, Diego Calvanese, Deborah Mcguinness, Daniele Nardi, Peter Patel-Schneider
ISBN: 0521876257
Publisher: Cambridge University Press
Year: 2007