School of Electronics and Computer Science:
COMP3028 Knowledge Technologies
Basic Information
| School | |
|---|---|
| Known as | COMP3028. |
| Session and Semester | Semester One, 2011 - 2012 |
| Credit | 10 Credit Points |
| Unit Leader | Dr Nicholas Gibbins |
| Teachers | Prof Nigel R Shadbolt |
| Moderators | Dr Richard M Crowder |
| Study | 76 hours directed reading, coursework, and private study |
| Assessment | 80% examination 20% coursework |
| Coursework | Expert system design |
| Teaching | Lectures: 24 -- two per week during the teaching weeks. |
| Referral | On referral, this unit will be assessed 100% by examination. |
| Syllabus Approved |
Description
Aims
The unit aims to introduce a wide range of methods and techniques that are currently used and researched in systems and applications that are based on domain-specific knowledge.
Learning Outcomes
Knowledge and Understanding
Having successfully completed the module, you will be able to demonstrate knowledge and understanding of:
- Identify the distinction between computational methods in general and knowledge-based technologies;
- Understand the differences in approaches to knowledge representation;
- Understand the techniques for acquiring domain knowledge;
- Understand the techniques for managing uncertainty in rule-based systems;
- Relate methodologies and techniques to a range of practical applications.
Intellectual Skills
Having successfully completed the module, you will be able to:
- Isolate and organise conceptual elements of simple domains of discourse and identify the structures and processes by which they may be analysed;
- Organise the relevant aspects of a domain of knowledge according to some knowledge representation approach.
Practical Skills
Having successfully completed the module, you will be able to:
- Use a number of techniques addressed in the lectures to create useful knowledge technologies;
- Implement simple expert systems.
Topics Covered
Topics to be covered will vary from year to year, but will be selected from the following list. Topics will be updated to reflect ongoing developments in this rapidly moving field.
Knowledge Representation
- Ontologies
- Logic: propositional, predicate, description
- Semantic networks, frames, scripts, rules
- Uncertainty: certainty factors, fuzzy logic
Automated Reasoning
- Analytic tableaux
- Unification
- Resolution, hyperresolution
- Explanation and truth maintenance
Knowledge Acquisition and Modelling
- Elicitation: card-sort, repertory grids
- Structured Knowledge Engineering
Information Retrieval
- Boolean searches
- Vector space model
- Term selection and weighting
- Query refinement
- Evaluation: precision and recall
Teaching and learning activities
Teaching methods include
Lectures: two per week during the teaching weeks in Semester 2. These are used to present theoretical and practical aspects of knowledge technologies.
Assignment: There is one assignment, marked by the unit lecturers. This is used to familiarise students with a range of knowledge technologies, to engage them in a critical analysis of knowledge technologies, and to ensure an appreciation of the current state-of-the-art.
Learning activities include
During the lectures there may be presentations and discussions with plenary feedback. Participation in discussion, while not compulsory, is encouraged.
At the end of each teaching week, there will be directed reading from the core texts. This provides a guide to the minimum amount of reading expected during private study time. Students are encouraged to read around the subject.
In addition to the recommended texts, the website contains links to a number of other activities and information resources that may be useful during private study time.
Methods of assessment
| Assessment method | Number | % contribution to final mark |
|---|---|---|
| Examination [exam] | 1 | 80 |
| Coursework [cwork] | 1 | 20 |
Feedback and student support during module study
- Assignments will be marked and returned before the last teaching week.
- Student presentations and discussions will take place during lectures.
- There are further resources on the course web site
Relationship between the teaching, learning and assessment methods and the planned learning outcomes
Feedback and student support during module study
- Assignments will be marked and returned before the last teaching week.
- There are further resources on the course web site
Relationship between the teaching, learning and assessment methods and the planned learning outcomes
The knowledge, understanding and intellectual skills listed will be taught in lectures. In completing the assignment, students will demonstrate mastery of the skills listed, including the practical skills.
The purpose of the exam is to test understanding of topics not covered by or difficult to assess fully in an assignment
Resources
Background Resources
- John F. Sowa, Knowledge Representation: Logical, Philosophical, and Computational Foundations, Brooks Cole Publishing Co., 2000 [Library] [Shops]
- Daniel Jurafsky and James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Prentice Hall, 2000 [Library] [Shops]
- Stuart J Russell and Peter Norvig. Artificial Intelligence: A Modern Approach (2nd Edition) Prentice Hall, 2003
- Ian H. Witten and Eibe Frank Data Mining: Practical Machine Learning Tools and Techniques Second Edition, Morgan Kaufmann, 2005. [Library] [Shops]
- McGraw KL., Harbison-Briggs K. Knowledge Acquisition: principles and guidelines, Prentice Hall, 1989 [Library] [Shops]
- Michael Wooldridge. An Introduction to MultiAgent Systems, Wiley, 2002
- Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R, Shadbolt, N., Van de Velde, W., axtd Wielinga, B. Knowledge Engineering and Management The CommonKADS Methodology, The MIT Press, 2000
Taught to
COMP3028
Non-existing cohort: "ceMEng3" (Optional)Pt III BSc Computer Science (Optional)
Non-existing cohort: "csBScAi3" (Optional)
Non-existing cohort: "csBScDs3" (Optional)
Non-existing cohort: "csBScIm3" (Optional)
Computer Science Integrated PhD (Optional)
Pt III MEng Computer Science with Artificial Intelligence (Optional)
Pt III MEng Computer Science (Optional)
Pt III MEng Computer Science with Distributed Systems & Networks (Optional)
Pt III MEng Computer Science with Image and Multimedia Systems (Optional)
Pt III MEng Computer Science with Mobile and Secure Systems (Optional)
MSc in Artificial Intelligence (Optional)
Non-existing cohort: "csMScCo" (Optional)
ECS Socrates Students (Optional)
Pt III Units offered to other Faculties (Optional)
Pt III BEng Software Engineering (Optional)
Pt III MEng Software Engineering (Optional)
Students who are not registered on an ECS approved programme may take this module subject to meeting its pre-requisites and the availability of resources. To confirm this, please can you contact the module leader (as listed above) in the first instance. They will then refer you on to the appropriate director of studies for formal approval of your selection.