COMP30411: Knowledge Representation (2007-2008)
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.
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 outcomesLearning 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 OutcomesA1, A5, B1, B3, C4
Topic covered include:
First order logic: syntax, semantics, proof theory, and applications
Description logics: syntax, semantics, proof theory, and applications
Ontologies and ontology engineering
Commonsense and scientific representation
Selected papers and technical reports will be distributed during the lectures.
Core TextTitle: Knowledge, Representation and Reasoning
Author: Ronald J. Brachman, Hector J. Levesque
Publisher: Elsevier Science & Technology
Supplementary TextTitle: Formal Theories of the Common Sense World
Author: Hobbs, Jerry R., Moore, Robert C. J.R. Hobbs, R.C. Moore
Publisher: Intellect Books
Supplementary TextTitle: Language, Proof and Logic
Author: Jon Barwise, John Etchemendy
Publisher: The University of Chicago Press
Supplementary TextTitle: Description Logic Handbook
Author: Franz Baader, Diego Calvanese, Deborah Mcguinness, Daniele Nardi, Peter Patel-Schneider
Publisher: Cambridge University Press