Module Details

Module Code: COMP9016
Title: Knowledge Representation
Long Title: Knowledge Representation
NFQ Level: Expert
Valid From: Semester 2 - 2019/20 ( January 2020 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 1 programme(s)
Module Description: Knowledge representation and reasoning (KR) is a field of AI that focuses on the representation of domain specific knowledge in a form that can be utilised by computer systems. Knowledge representations within a domain are often conceived as formalisms, a description of something in formal mathematical or logical terms.

This module will introduce students to methodologies for the visualisation and interpretation of domain specific knowledge and the translation of interpretations into KR formalisms. It will provide the student with an appreciation of how to evaluate the suitability of knowledge representation schemes, balance competing features/requirements and make informed decisions when designing KR formalisms.

The module will also focus on the application of KR to appropriate real world problems such as the semantic web, time-series indexing and temporal abstraction of expert knowledge.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Appraise domain specific formalisms used in knowledge representation schemes.
LO2 Compare and contrast current knowledge representation approaches integrated in systems relevant to AI.
LO3 Select, apply and evaluate a knowledge representation scheme for a specified domain.
LO4 Design and implement KR formalisms for a real-world data set.
LO5 Interpret, critique and communicate the suitability of data visualisation techniques used in conjunction with the design of KR formalisms and the analysis of the resulting output.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Introduction
Central role of knowledge representation; desired properties of representation schemes; overview of representation schemes including, e.g. logic, frames, neural networks, common-sense knowledge; the frame problem in logic.
Formalism
Propositional and predicate logic; syntax and semantics; rules of inference; logical consequence and proof; logic as knowledge representation formalism; unification; resolution theorem proving.
Knowledge Representation
Formalisms, semantic nets, systems architectures, frames rules and ontologies; automated reasoning, inference engines, theorem provers and classifiers; roles within KR frameworks, ontology engineering.
Knowledge Representation vrs Data Representation
Temporal reasoning and abstraction; change, causality and actions described in terms of time, decision analysis, spatial–temporal reasoning; time series representation, symbolic representation, discrete wavelet transform, discrete fourier transform; dimensionality reduction; comparison of data mining tasks (clustering, classification, indexing), upper and lower bounds, distance measures.
Data Visualisation
Data visualisation theory - targeting appropriate visual elements on a page, mapping values in the data domain to visual domain, human visualisation interaction - adding computation steering to visualisations. Charts, Plots & Layouts - graphical representations of data: Line Charts, Area Charts, Bubble Charts, Bar Charts, Scatterplots, Scaling Data, Axes, Geomapping.
Discussion Topics
For example, semantic web and natural language processing, expert systems; knowledge-based systems - acquisition and representation; indexing time series at scale.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 40
Timing Week 6 Learning Outcomes 1,2,3
Assessment Description
Detail and critically evaluate a knowledge representation scheme, its utilisation of formalisms and relevance to Artificial Intelligence.
Assessment Type Project % of Total Mark 60
Timing Sem End Learning Outcomes 1,2,3,4,5
Assessment Description
Design, develop and deploy a KR solution for a real world problem domain. e.g. a system for automating the interpretation of biomedical data requires a KR scheme that enables the translation of data such that the integrity of all domain specific information is maintained. Provide a rationale for the chosen visualisation approach taken at both the design and analysis stage.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture underpinning learning outcomes. Every Week 2.00 2
Lab Contact Lab supporting content delivered. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture underpinning learning outcomes. Every Week 2.00 2
Lab Contact Lab supporting content delivered. Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • S. Russell & P. Norvig. (2009), Artificial Intelligence A Modern Approach, 3rd. Prentice-Hall, [ISBN: S. RUSSELL & P. NORVIG, ARTIFICIAL INTELLIGENCE A].
  • R. Brachman & H. Levesque H. (2004), Knowledge Representation and Reasoning, 1st. Morgan Kaufmann, [ISBN: ISBN 1558609326, ISBN-13: 9781558609327].
Supplementary Book Resources
  • Michael Gelfond, Yulia Kahl. (2014), Knowledge Representation, Reasoning, and the Design of Intelligent Agents: The Answer-Set Programming Approach, 1st. Cambridge University Press, p.360, [ISBN: 978110702956].
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 1 Mandatory