Computer Science

ECMM408 - Logic, Ontology, and Knowledge Representation (2019)

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MODULE TITLELogic, Ontology, and Knowledge Representation CREDIT VALUE15
MODULE CODEECMM408 MODULE CONVENERDr Enrico Malizia (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 0 11 0
Number of Students Taking Module (anticipated) 30
DESCRIPTION - summary of the module content

Knowledge representation, the codification of knowledge and reasoning in a form that is amenable to computational manipulation, is a fundamental requirement for the application of Artificial Intelligence to real-world problems. The study of knowledge representation Is particularly fascinating as it combines technical issues concerning the digital representation and manipulation of knowledge with ontological issues concerned with the nature and structure of human knowledge and understanding of the world. Bridging the gap between these two aspects are formal languages or logics that can be used for expressing knowledge in a form that can be handled by computers. This module will provide an introduction to all these aspects, building on an assumed prior acquaintance with computer data structures and elementary formal logic.
 

PRE-REQUISITE MODULES ECM2418 Computer Languages and Representations (specifically the part relating to formal logic)

AIMS - intentions of the module

An important goal of Artificial Intelligence is to explore ways of endowing machines with the knowledge and reasoning capacities to enable them to behave in ways which we might recognise as intelligent. Of particular concern is the drive to emulate human ‘common-sense’ understanding, which requires the assimilation of a vast range of mundane facts, many of them seemingly trivial, on the basis of which we are able to conduct our day-to-day negotiations with the world and with each other. This enterprise requires us to answer such questions as: How do we describe and classify the elements that make up our common-sense knowledge of the world? How are all these elements interrelated? What methods can we use to reason effectively about our knowledge in order to derive new conclusions from existing facts? In this module you will be introduced to some of the main bodies of theory which have been employed to help answer these questions in the context of modern computer technology, and will see their uses illustrated in a number of application case studies

INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Use confidently logical systems such as first-order logic, description logic, and modal logic to express the knowledge representation needs of Artificial Intelligence;
2. Handle key ontological concepts such as universal vs particular, independent vs dependent, continuant vs occurrent, and inheritance.
3. Describe a range of knowledge representation formalisms and apply them to example domains.

Discipline Specific Skills and Knowledge

4. Analyse problems and situations logically and systematically, establishing the key concepts and their interrelationships;
5. Show an appreciation of how theoretical investigations can form an essential underpinning to practical research in the Computer Science domain.
6. Describe the main trends in logic-based AI research

Personal and Key Transferable / Employment Skills and Knowledge

7. Read, digest, and present a critical review of research papers from conferences and journals.
8. Relate theoretical knowledge to practical concerns.
SYLLABUS PLAN - summary of the structure and academic content of the module
  • Introduction and overview: What is logic? What is ontology? What is knowledge representation?
  • Historical development of knowledge representation in the context of Artificial Intelligence
  • Recapitulation of elementary propositional and first-order logic; properties of logical systems including soundness, completeness, expressivity, and tractability; introduction to further logics such as modal logic, default logic, and description logic.
  • Representations of common-sense knowledge
  • Historical introduction to ontology, from ancient philosophy to the semantic web
  • Fundamental principles of formal ontology: classes and instances, taxonomies and partonomies, dependence and independence.
  • Examples of modern formal ontologies, including upper ontologies and domain ontologies.
  • Knowledge Representation and Ontology case studies (details may vary from year to year; typical topics include space and time, causality, agents and roles, parts and wholes).
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 32.00 Guided Independent Study 118.00 Placement / Study Abroad 0.00
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching 22 Lectures
Scheduled learning and teaching 10 Tutorials
Guided independent study 30 Coursework
Guided independent study 88 Private Study

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Logic Exercise 3 pages 1 Marksheet and oral feedback
       
       
       
       

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Logic Exercise 20 4 pages 1 Annotated marksheet and orally in class
Ontological modelling exercise 40 2 pages plus computer implementation 1-4 Annotated marksheet and orally in class
Essay 40 2000 words 5-8 Annotated marksheet
         
         

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
All Written exercise encompassing elements from the three assessments All Ref/Def exam period
       
       

 

RE-ASSESSMENT NOTES

All re-assessment will be by a single written exercise. For referred candidates the mark will be capped at 50%. For deferred candidates, depending on circumstances, deferral may be possible in one or two of the original pieces of coursework.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Basic reading:

 

ELE: http://vle.exeter.ac.uk/

 

Web based and Electronic Resources:

 

Other Resources:

 

Reading list for this module:

Type Author Title Edition Publisher Year ISBN Search
Set Davis, Ernest Representations of Commonsense Knowledge Morgan Kaufmann 1990 [Library]
Set Sowa, John F. Knowledge Representation: Logical, Philosophical and Computational Foundations Brooks/Cole 2000 0-534-94965-7 [Library]
Set Brachman, Ronald and Leveque, Hector Knowledge Representation and Reasoning Morgan Kaufmann 2003 1-55860-932-6 [Library]
Set Hobbs JR and Moore R Formal Theories of the Commonsense World Ablex 1985 [Library]
Set Colomb, Robert M. Ontology and the Semantic Web IOS Press 2007 978-1-58603-729-1 [Library]
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 10 July 2018 LAST REVISION DATE Tuesday 10 July 2018
KEY WORDS SEARCH Logic, Ontology, Knowledge Representation