Class Description

Instructor:

Yugi (Yugyung) Lee, Ph.D.

Office Hour: M/W 4:00 – 5:00PM or by appointment

Office: FH 560D

Phone: 816-235-5932

Email: ude.ckmu|uyeel#ude.ckmu|uyeel

Website: www.sce.umkc.edu/~leeyu

Graduate Teaching Assistant:

Nitin Mamillapally

Office: FH527

Email: ude.ckmu|ftqmvn#ude.ckmu|ftqmvn

Office Hour: T 8:30 – 9:30PM in FH460 or by appointment

Course description:

This course teaches students fundamental theory and practice in the field of knowledge discovery and management and also provides them with hands-on experience through application development. Prerequisites: CS551, and either CS461 or CS464

Textbook, and other Materials Required and Recommended:

o Research papers (Class Handout)

o Data Mining: Concepts and Techniques by Jiawei Han and Micheline Kamber, ISBN: 1-55860-489-8

o Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce
by Dieter Fensel Springer Verlag; ISBN: 3540416021 ; 1st edition (August 15, 2001)

o Knowledge Representation: Logical, Philosophical, and Computational Foundations
by John F. Sowa, David Dietz Brooks/Cole Pub Co; ISBN: 0534949657 ; 1 edition (August 17, 1999)

o Internet Based Workflow Management: Towards a Semantic Web
by Dan C. Marinescu John Wiley & Sons; ISBN: 0471439622 ; 1st edition (April 5, 2002)

o Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential
by Dieter Fensel (Editor), Wolfgang Wahlster (Editor), Henry Lieberman (Editor) MIT Press; ISBN: 0262062321 ; (November 15, 2002)

Course Objectives, Goals or Learning Outcomes:

The goal of this course is to introduce theoretical and practical aspects of knowledge discovery and management (covering data mining and information extraction). Throughout this course, students will obtain extensive hands-on experiences in problems/solving in knowledge discovery and management and with various tools. In addition, students will be able to apply the concepts and techniques to emerging applications such as Semantic Web, Medical Informatics, Bioinformatics, distributed computing.

Instructional Strategies/Pedagogical Approach:

This course will require several distinct types of learning: Since this course is a research-oriented graduate course, a substantial portion of the quarter will be devoted to student presentations of techniques and research papers in the areas of Knowledge Engineering. Students will be expected to select a problem area in Knowledge Engineering and prepare an intensive presentation covering the methods and framework commonly employed to address their problem.

a. Research Project: Students will be asked to design and build an innovative research project for presentation at the end of the semester. Students should organize themselves into research project teams and develop their research project. A final written report will be submitted.

b. Reading/Discussion/Presentation: The lecture/discussions are designed to be highly participatory. Therefore, it is fair and just that points are awarded for effort and participation in these discussions. For each research paper in the assigned reading list: participate in the class discussion of each paper provide written summaries of each paper before class volunteer to present in class certain of the papers on the reading list, on a rotating basis.

Semester Schedule of Topics (“subject to modification”):

1. Introduction to Knowledge Engineering

2. Knowledge Representation

a. Concept, relations, property

b. Ontologies

c. Representation languages (Semantic Web): RDF, RDFS, OWL

d. Tools for Knowledge creation: Protégé

3. Knowledge Discovery

a. Data Mining

• Classification,

• Clustering,

• Association,

• Artificial Neural Network,

• Genetic Algorithm

b. Text Mining

c. Tools for Knowledge discovery: Weka, leximancer

4. Knowledge Processing

a. Query, Reasoning with Ontologies

b. Semantic Web: Rule-based Systems

c. Semantic Web Services : OWL-S

d. Tools for Knowledge processing: Jena, OWL-S Editor, Racer, Jess

5. Advanced Topics

a. Ontology integration

b. Ontology interoperability

c. Ontology summarization

d. Ontology modularization and patterns

6. Development of Knowledge-based Applications

a. Web

b. Biomedical Informatics

c. Pervasive Computing

d. Sensor Network

Assignments: Paper reading & critique: Every Fridays

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