EE 6263 Foundations of Knowledge Representation for
Software Engineering Fall 2010
The goal for this course is to study the state
of the art of the
approaches, paradigms, techniques, languages, tools, etc.
used for knowledge representation and automated reasoning in computer and/or
intelligent systems with emphasis on comparative evaluation of these approaches
in the engineering context. This course also studies aspects of dealing with ontologies as well as the role of ontologies
in electrical, computer and software engineering, and related engineering
disciplines. Practical use of knowledge engineering tools is an integral part
of this course.
Preliminary Schedule: MW 10:30-11:20 GWD 120
INSTRUCTOR: Yevgen Biletskiy e-mail: biletski на unb.ca
Office: GWC115 phone:
447-3495
Topics:
1. Introduction to knowledge representation and automated reasoning: syntactical and inferential aspects and capabilities of knowledge representation.
2. Knowledge representation in software engineering perspective.
3. Knowledge representation in computer systems.
4. Knowledge representation languages and models: relational model, UML, decision tables and trees, belief networks, artificial neural networks, propositional logic, first-order logic, description logic, fuzzy logic, frames, semantic networks, graphs, object-attribute-value triples, rules and Horn logic, and production rules.
5. Modern ontology representation languages: Ontolingua and KIF, LOOM, F-logic, XML/XSD, SHOE, DAML+OIL, RDF/RDFS, OWL, RuleML, and SWRL.
6. Evaluation and comparison of knowledge representation in computer systems.
7. Ontology engineering: ontology representation and reasoning, ontology design, ontology infrastructure, and ontology applications.
8. Knowledge engineering tools (e.g. Protégé-2000) – an umbrella activity through the course.
Literature (optional):
Antoniou, G., van Harmelen, F., (2004) A Semantic Web Primer, The MIT Press, 272
p., ISBN 0-262-01210-3.
Yevgen Biletskiy, Girish R Ranganathan. An
invertebrate semantic/software application development framework for
knowledge-based systems, Knowledge-Based Systems, Elsevier, Volume 21,
Issue 5, pp. 371-376, 2008.
Yevgen Biletskiy, Girish R Ranganathan, J Anthony
Brown, Representing User-Friendly Business Rules in a
Semantic Web-Based Format, ISAST Transactions on Computers and Software
Engineering (in press), 2(1), pp. 8-12, 2008, on-line journal, available: http://www.isastorganization.org/CS2ready.pdf.
Corcho, O., Gómez-Pérez (2000), A. Evaluating
Knowledge Representation and Reasoning Capabilities of Ontology Specification
Languages, In
Proc. of ECAI-00 Workshop on Applications of Ontologies
and Problem-Solving Methods.
Gómez-Pérez, A. Fernandez-Lopez, M., Corcho, O., (2003) Ontological Engineering, Springer, 403 p., ISBN 1-85233-551-3.
Gruber T., (1995), Towards Principles for the Design
of Ontologies Used
for Knowledge Sharing, International Journal of Human-Computer studies, 43
(5/6): 907 -- 928.
Russel S., Norvig, P. (1995)
Artificial Intelligence: A Modern Approach, Prentice Hall, 932 p., ISBN
0-13-103805-2.
Reichgelt, H. (1994) Knowledge
Representation, Ablex Publishing Corporation, 251 p.,
ISBN 0-89391-590-4.
Sowa J.F., (2000) Knowledge Representation,
Brooks/Cole Thomson Learning, 594 p., ISBN 0-534-95965-7.
Staab, S., Studer,
R. (2004) Handbook on Ontologies, Springer, 660 p., ISBN 3-540-40834-7.
Studer, R., Benjamins, V.R., Fensel, D., Knowledge Engineering: Principles and Methods, Data and Knowledge Engineering, 25:
161-197, 1998.
Grading scheme:
Participation 25 %
Assignments 25 %
Final paper 50 %