COMP34512: Knowledge Representation and Reasoning

This page contains supporting materials for the module COMP34512 given by Sebastian Brandt. Information here is subject to change: check this page regularly for additional information and updates.

Coursework and discussion boards are on Blackboard, so please make sure you can log in.

Course Overview

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.


Assessment for the course will be 80% examination, 20% exercises and "labs". In addition to the formal lectures, the timetable includes a number of "lab" sessions (during the timetabled slots). Students will be expected to participate in these discussion sessions and present material prepared outside of the timetabled lectures. As a result, attendance for some of the timetabled slots for this module should be considered mandatory.

Late work will generally receive no credit. It is highly recommended that if you have a reasonable excuse to fill a mitigating circumstances form.

Timetable and Lecture Notes

The schedule will be shown below. Lecture notes and associated material will be linked when available. Note that this timetable is subject to change, so you should check this page regularly.

Lectures are on Tuesday at 12:00 in LF1.4 and Thurs at 12:00 in University Place 5.211 in the Kilburn Building.

(The week list will expand as lectures occur. Check back for links.)

All the slides as a single (huge) PDF (54 megs!)

Week Day Topics Resources/Reading Notes
Blackboard Discussion Forum
1 Tues, Jan 28 Introduction and Administrivia
The Regular Expression Analogy
McCarthy, Programs with Common Sense Course Intro
Thurs, Jan 30 Knowledge Elicitation
Operational Definition of KA
Domain Expert vs. Knowledge Engineer
Desiderata for a Elicitation Technique
Card Sorting and Triadic Elicitation
An extensive set of web pages on Elicitation Knowledge Elicitation
2 Tues, Feb 4 Triadic Elicitation
Identification, Normalisation, etc.
Modifier, Self-Standing, and Definable
Coursework 1: Due Feb 13! Knowledge Elicitation Exercise
Thurs, Feb 6 A Worked Acquisition Example
Identification, Normalisation, Laddering, etc. in practice!
Helpful for coursework
Moving toward formalisation
3 Tues, Feb 11 Working Example for Definable
Definitions driving introducing terms
Removing terms
Helpful for coursework
Toward Definitions
Thurs, Feb 13 Formalisms
Syntax and Semantics (for ALC!)
How reasoning helps
Hartel, et al Modeling a description logic vocabulary for cancer research Formalising Definitions
4 Tues, Feb 18 NCIt Case Study
Definitions for conflict resolution
Davis et al, What is a Knowledge Representation? Knowledge Representation In Action
Thurs, Feb 20 Knowledge Representation "definition"
The 5 Roles of KR (KNOW THESE)
Strong and Weak Cognitive Adequacy
Cognitive Level
Chen, The Entity-Relationship Model-Toward a Unified View of Data
Doctorow, Metacrap: Putting the torch to seven straw-men of the meta-utopia
What is a Knowledge Representation?
5 Tues, Feb 25 Propositional Logic
Syntax and Semantics
Truth tables
Aho and Ullman on Propositional Logic
Aho and Ullman on Predicate Logic
Logical Foundations
Thurs, Feb 27 Predicate Logic
Syntax and Semantics
Aho and Ullman on Propositional Logic
Aho and Ullman on Predicate Logic
Logical Foundations (2)
6 Tues, Mar 4 Prop vs. Pred representation
Prop computation
Truth table and Early Abort TT methods
OWL Primer
OWL Structural Spec
Formalism Design
Thurs, Mar 6 Goal Driven TT procedure
Complexity classes
Predicate logic complexity
Relation between computational and cognitive complexity
Formalism Design (2)
7 Tues, Mar 11 RDFS-- Expressivity, Cog Complexity
Semantics by translation
ALC Intro
Computational Robustness
More on Computational Issues
Thurs, Mar 13 ALC Semantics ALC + Disjointness
ALC + Counting and Transitivity
Some Logical Nitty Gritty
8 Tues, Mar 18 Strong Cognitive Adequacy (SCA)
Weak Cognitive Adequacy (WCA)
Their relation
Effects on various tasks
Strube, The Role of Cognitive Science in Knowledge Engineering Cognitive Adequacy
Thurs, Mar 20 Pre-coordination
Run- vs. dev-time
Definition Oriented Development (encore)
9 Tues, Mar 25
Data Integration with Ontologies
Spring Break
Thurs, Mar 27 OWL and Data (Properties)
10 Tues, Apr 1
Quantities and Policies
Thurs, Apr 3 Data Integration with Ontologies
11 Tues, Apr 30
Thurs, Apr 29 A Different Kind of KR
12 Tues, May 1
Old Wine in New Bottles? The Semantic Web
Thurs, May 8 Exam Revision

Past Papers

The exam is given electronically via Blackboard and contains Multiple Choice as well as Essay questions. As such, it is not released by the university. Below are the last paper based exams given which give a feel for the content of the exam. Throughout the course, we will given coursework that is design to prepare you for the exam, including a 1/4 sized practice exam in the last week.

Exam paper from 2009-2010.

Exam paper from 2008-2009.