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This is an archived syllabus from 2013-2014

COMP34512 Knowledge Representation and Reasoning syllabus 2013-2014

COMP34512 Knowledge Representation and Reasoning

Level 3
Credits: 10
Enrolled students: 44

Course leader: Sebastian Brandt

Additional staff: view all staff


  • Pre-Requisite (Compulsory): COMP11120

Assessment methods

  • 80% Written exam
  • 20% Coursework
Sem 2 Lecture 1.4 Tue 12:00 - 12:00 -
Sem 2 Lecture Uni Place 5.211 Thu 12:00 - 12:00 -
Themes to which this unit belongs
  • Natural Language, Representation and Reasoning


The Web is one of the largest and most diverge bodies of knowledge ever. Strikingly, it is also both a relatively coherent information artifact, a distributed computation system, and a medium for human interaction on a wide scale. While much of the content of the Web originates in databases and other well modeled forms of data, it is primarily exposed in the form of HTML. In recent years, Web Services, REST services, XML, and other Web 2.0 technologies have striven to augment the "human oriented" web with a "program oriented" Web, e.g., a Web of Data.

Among these competing approaches, the Semantic Web is distinctive in reimagining the Web not as a Web of Data, but as a Web of Knowledge. The key idea of the Web, of course, is distributed hypertext. The key idea of the Web of Data is a distributed datastore. The key idea idea of the Semantic Web is a distributed knowledge representation

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.

The Semantic Web is where KR and the Web collide. This course will explore the aftermath.

In particular, we will focus on various logic based formalisms for knowledge representations including their design and use. 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 explore how to bring such formalism (and associated representations) into a Web context, with special attention paid to the unique challenges thereby raised.

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.


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

Feedback methods

Coursework in the form of modelling assignments, short essays, and short objective quizzes are spread throughout the semester. In addition, there are extensive in class activities.

Study hours

  • Lectures (24 hours)

Employability skills

  • Analytical skills
  • Innovation/creativity
  • Problem solving
  • Research
  • Written communication

Learning outcomes

On successful completion of this unit, a student will be able to:

Learning outcomes are detailed on the COMP34512 course unit syllabus page on the School of Computer Science's website for current students.

Reading list

No reading list found for COMP34512.

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.